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Predictors and Outcomes of HAIs in COVID-19 Patients
Highlights
• Of 1565 patient, 140 (8.9%) separate HAIs from 73 different organisms developed in 59 (3.7%) patients.
• Tocilizumab, steroids and hydroxychloroquine are associated with higher rate of HAIs.
• HAIs are not associated with increased risk of death.
Abstract
Introduction
Healthcare Associated infections (HAI) after a viral illness are important source of morbidity and mortality, this has not been studied well in hospitalized COVID-19 patients.
Methods
We included all COVID-19 positive adult patients (≥18 years) hospitalized between March 1, 2020 to August 5th, 2020. We used CDC definitions of HAI in the acute care setting. Outcomes studied were rates and types of infections and in hospital mortality.. We constructed several multivariable logistic regression models to examine characteristics associated with development of HAI.
Results
Of 1565 patients, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of these, 23 were gram positive, 39 were gram negative and 11 were fungal. Patient developing HAI did not have higher odds of death (OR 0.85,95%CI 0.40-1.81, p = 0.69). HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04).
Conclusions
HAI are common in hospitalized covid-19 patients. Tocilizumab and steroids were associated with increased risk of HAIs.
INTRODUCTION
Secondary infections post viral illnesses occur frequently and may lead to adverse outcomes. In previous influenza epidemics, for example, many deaths were the direct result of secondary bacterial pneumonia(Morens et al., 2008). Though seemingly common, these infections remain poorly characterized. In a few small studies during the 2009 H1N1 pandemic(Dhanoa et al., 2011), approximately 19% of cases were reported to have secondary bacterial infection of which Streptococcus pneumoniae was the most common isolate (MacIntyre et al., 2018). Reports describing the epidemiology of secondary infections associated with COVID-19 pneumonia are limited and have small sample sizes. Zhou et al reported 50% rate of secondary infection in people who died compared to 1% in survivors(Zhou et al., 2020). Chen et al reported 4% rates of fungal infection(Chen et al., 2020).
Though management strategies for COVID-19 have evolved through the pandemic, use of therapies with immune modulating properties such as IL-6 receptor antagonists and corticosteroids is common. (Geleris et al., 2020, Jordan et al., 2020, Morena et al., 2020, Selvaraj et al., 2020, Valk et al., 2020). These medications are used in attempts to quell the hyperactive host immune response that appears to play a key role in clinical deterioration(Wiersinga et al., 2020). Many patients are also likely to be exposed to antibiotics secondary to ongoing fever which may not necessarily represent superimposed infections. Further in persons who are critically ill, the use of central venous and urinary catheters is common (O’Grady et al., 2011). These factors combined likely raise susceptibility to the acquisition of secondary infections in COVID-19.
The goals of this study are to describe the epidemiology of secondary infections after hospitalization for COVID-19 i. Since these are acquired while patients are hospitalized, we used CDC surveillance definition of healthcare associated infection (HAI) to study these infections (Horan et al., 2008). As an exploratory aim we also attempted to discern risk factors for acquiring such infections.
METHODS
Study design and Data source
We performed a retrospective analysis of adult covid-19 patients (age ≥18 years) admitted to a large community hospital in a rural setting in Northeast Georgia between March 1, 2020 to June 10th, 2020. COVID-19 patients were identified from our Epic® EMR using ICD10CM and/or CPT codes for COVID-19 and/or positive for SARS CoV-2- PCR. We obtained clinical and demographical details from Epic® Caboodle data warehouse and Cerner APACHE® Outcomes. Systems integration was provided by IPC Global by leveraging their in-Process Data Factory innovation running on an AWS® VPC. We excluded COVID-19 patients who required readmission to the hospital after initial discharge. The study was reviewed and found exempt by Northeast Georgia Health System IRB board.
General management of covid-19 patients
Patients were admitted to ICU if their FiO2 requirement was higher than 50% (> 10LPM of nasal cannula or >50% on high flow oxygen). All patients received therapeutic anticoagulation if they had D-dimer > 2 and SIC score > 3 (Ding et al., 2018). Initially all admitted patients received hydroxychloroquine till data regarding efficacy was released, thereafter patients did not receive this medication(Shah et al., 2020, Tang et al., 2020). We administered tocilizumab if patients met laboratory parameters supportive of cytokine storm which included 3 or more of the following - 1. Ferritin >300 ng/mL with doubling within 24 hours, 2. Ferritin >600 ng/mL, 3. LDH > 250 U/L, 4. Elevated D dimer (> 2 mcg/mL FEU), 5. High sensitivity CRP greater than 7 mg/L or 6. H score greater than 110. We used convalescent plasma if patients had dyspnea, RR ≥ 30, PF ratio ≤300 or had the treating clinician adjudicated life-threatening disease. We provided standard CMS guidelines-based management for prevention of VAP, CAUTI and CLABSI.
Definitions
We reviewed culture data to ascertain the presence of infection. We classified infections as respiratory (tracheal aspirate, bronchoalveolar lavage), blood, urine, and other (body fluids such as pleural cavity, abdominal cavity). We used CDC definitions of healthcare associated infections(HAIs) in the acute care setting (Horan et al., 2008). We deemed that a HAI was present if cultures were positive and obtained after 3 days of hospital admission. We defined days to positive cultures as time from day of admission to the day the culture was collected..
Since cultures can be negative, we defined ‘possible infection’ if patient developed fever > 100.4Of after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this fever. We also included within this definition if WBC count rose to > 15000 after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this elevation in WBC. Possible infection was not included in HAI.
Outcomes
Our primary outcome of interest were rates of HAIs. Secondary outcomes included in hospital mortality.
Statistics
We performed all statistical analysis using STATA MC 16.0 (Stata-Corp, College Station, Tx). We describe categorical data using frequency count and percentages. We report medians and inter quartile ranges for continuous variables as they were not normally distributed.
We compared demographical and clinical characteristics of persons who had HAIs with those who did not using chi square and Wilcoxon rank tests for categorical and continuous variables, respectively. For all analyses we deemed statistical significance a p-value < 0.05.
We used multivariable logistic regression models to determine factors associated with the development of HAIs in COVID-19. We used single predictor logistic regression to identify significant associations between putative risk factors and development of secondary infections. Variables found significant at p < 0.10 were candidates for inclusion in our primary model. Regardless of significance we included variables known to be associated with outcomes in COVID-19 as well as variables previously identified as risk factors for developing secondary infections. Backward elimination method was used to remove other variables from the final model. Final model was bootstrapped using 2,000 bootstrap replicates and case resampling with replacement from the original dataset.
We used multivariable logistic regression analysis to examine association of HAIs with in-hospital mortality. Since patients who developed HAIs were sicker and had higher inflammatory markers, we used propensity scores to adjust for these differences. Propensity score was used to identify probability that a patient would receive develop HAIs. This was calculated for every patient. Propensity score was computed using multivariable logistic regression model with HAIs as the dependent variable and incorporating multiple independent variables which included demographic and clinical characteristics along with laboratory markers. The propensity score thus obtained was used as a continuous variable to further adjust the above regression.
RESULTS
There were 1565 patients with COVID-19 within the study period. Of these, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of 140 HAIs, 53 were bacteremia, 67 were pneumonia and 17 were UTI. 38 patients had bacteremia from single organism and 15 patients had bacteremia from more than one organism.
73 separate organisms constituted 140 HAIs. Of these, 23(31.5%) were gram positive infections, 39(53.4%) were gram negative and 11(15%) were fungal infections. Among gram negatives, Pseudomonas (n = 10), Escherichia coli (n = 7) and Klebsiella (n = 7) and among gram positive – staphylococcus aureus(n = 13) and enterococcus (n = 5) were commonest bacterial organisms. Five out of eleven candidal infections were non albicans species.
The details of organism, source, and median time to development of HAI is shown in Table 3. Fourteen patients developed clostridium difficile infections.
There were 118 instances which qualified as possible infections. Of these 48 (40.7%) qualified as culture positive secondary infection.
Demographics & Clinical Characteristics
There was male and black race had higher rates of HAIs (Table 1 ). Rates of HAIs were higher in co-morbidities such as diabetes mellitus and ESRD (Table 1). HAIs were observed to be higher in patients receiving treatment for covid-19 such as hydroxychloroquine (27.4% vs 13.6%, p < 0.001), convalescent plasma (50.9% vs 14.1%, p < 0.001), tocilizumab (66.7% vs 11.6%, p < 0.001) and steroids (78.4% vs 40.2%, p < 0.001). Patients who received cefepime and vancomycin on admission developed higher rates of secondary infection (Table 1).
Table 1 Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection.
No HAI HAI p
Total 1514 59
Age 62(48-75) 61(52-69) 0.45
Male (%) 51.1 64.7 0.06
Race (%) 0.009
White 60.8 45.1
Blacks 9.4 17.7
Hispanics 26.0 25.5
Asians/pacific Islander 1 3.9
Others 3.0 7.8
Co-morbidities (%)
Hypertension 63.2 70.6 0.28
Congestive heart failure 23.3 27.5 0.48
Diabetes Mellitus 38.6 56.8 0.009
COPD 29.7 21.6 0.3
ESRD 3.4 7.8 0.09
Cirrhosis 9.9 5.9 0.34
Cancer 11.5 9.8 0.69
Rheumatological Diseases 3.3 3.9 0.80
Home meds (%)
Anticoagulation 10.4 7.8 0.55
Anti-platelets 17.4 11.7 0.30
ACE/ARB 25.8 35.3 0.13
Immunosuppressants 0.07 0.00 0.82
COVID-19 Medications (%)
Vitamin C 70.1 90.2 0.002
Zinc 69.7 88.2 0.004
Hydroxychloroquine 13.6 27.4 0.005
Tocilizumab 11.6 66.7 <0.001
Steroids 40.2 78.4 <0.001
Convalescent plasma 14.1 50.9 <0.001
Remedesivir 28.2 50.9 <0.001
Therapeutic anticoagulation 73.3 96.1 <0.001
Antibiotics started at time of admission (%)
Azithromycin 48.8 64.7 0.025
Doxycycline 8.1 11.7 0.34
Levofloxacin/Ciprofloxacin 6.1 3.9 0.52
Clindamycin 1.1 1.9 0.58
Ceftriaxone 55.0 62.7 0.27
Cefepime 11.7 23.5 0.01
Piperacillin/tazobactam 11.5 13.7 0.63
Vancomycin 22.2 43.1 0.001
Sicker patients on admission with higher SOFA score and higher inflammatory markers (such as ferritin, CRP, and d-dimer) developed higher rates of secondary infection (Table 2 ).
Table 2 Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without.
No HAI HAI p
Total 1506 59
SOFA score on admission 0(0-1),1506 1(0-2),59 0.001
Initial laboratory studies*
WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004
Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048
Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012
Platelets 212(164-275),1499 228(161-317),58 0.35
Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21
Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001
BUN 16(11-25),1446 20(14-33),58 0.008
Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001
ALT 32(22-54),1436 42(28-67),57 0.03
Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046
INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38
aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24
Ferritin 373(158-805),1083 743(442-1450),57 0.001
CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001
D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001
ICU admissions 422(28.0%) 58(98.3%) 0.001
SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75
Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001
Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001
Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001
Required proning if on ventilator (%) 14.6 30.4 0.007
Required paralytic if on ventilator (%) 23.5 51.8 0.001
Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007
Required tracheostomy if on ventilator (%) 6.8 25.0 0.001
Use of vasopressors
Required Norepinephrine (%) 11.9 83.0 <0.001
Required vasopressin (%) 5.5 52.5 <0.001
Required epinephrine (%) 2.1 13.7 <0.001
Required angiotensin 2(%) 0.5 1.7 0.24
CVL before HAI (%) 8.1 45.8 <0.001
Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001
Acute DVT/PE (%) 4.2 22.0 <0.001
* median (Inter Quartile range), Number of samples.
Table 3 Hospital Associated infections: organisms and source and time to infection.
Number of patients Source Median time to infection
Blood Respiratory Urine others
Gram positive infections
MRSA 7 5 7 0 1 14
MSSA 6 5 6 1 0 13
Staphylococcus hominis 1 1 1 0 0 14
Staphylococcus epidermidis 4 4 4 0 0 15
Group D Streptococcus 5 5 4 1 0 12.5
Gram negative infections
E. coli 7 5 4 4 0 19
Klebsiella 7 2 7 1 0 21
Pseudomonas 10 7 10 3 1 15.5
Serratia 3 2 3 1 0 28
Enterobacter 6 3 6 1 0 19.5
Proteus 2 2 2 1 0 21.5
Citrobacter 1 0 1 0 0 24
Acinetobacter 2 0 2 0 0 26
Stenotrophomonas 1 1 1 0 0 8
Fungal infections
Candida albicans 6 6 5 2 1 13
Non albicans Candida 5 5 4 2 0 15
C. difficile 14
Patients developing secondary infections were more often on the mechanical ventilation (42.4% vs 10.0%, p < 0.001) and had central venous lines (45.8% vs 8.1%, p < 0.001).
Multivariable analysis of HAIs
HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). Other medications – convalescent plasma and remedesivir were not associated with increased rates of HAIs.
Outcomes
Patients developing secondary infection had significantly higher in hospital mortality when compared to those who did not develop secondary infection (40.7% vs 11.8% p < 0.001) (Table 4 ). However, on multivariable analysis, secondary infections were not associated with increased risk of death (OR 0.85, 95%CI 0.40-1.80, p = 0.67) (Appendix 1).
Table 4 Outcomes of patients with Hospital Associated Infections
No HAI HAI p
Total
Died (%) 11.8 40.7 <0.001
LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001
Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001
Disposition (%) <0.001
Home 70.8 26.5
Home with health 12.9 17.6
Rehab/SNF/LTAC/Acute care 13.6 35.3
Others 2.6 20.6
The length of hospital stay was significantly longer in patients developing secondary infections. Disposition in patients with secondary infection was significantly higher to skilled nursing facility and long-term acute care (SNF/LTAC) when compared to those who did not have secondary infection (35.3% vs 13.6%, p < 0.001).
DISCUSSION
COVID-19 pandemic has observed use of immunomodulating medications to help prevent dysregulated immune response also called cytokine storm(Wiersinga et al., 2020). We report our experience of all COVID-19 patients who were admitted to the hospital till August 5thth,2020. There were more than 1500 patients, of which 250 patients required intubation from severe COVID-19. We have observed number of patients that develop HAIs to be about 3.7% (Table 5 ).
Table 5 Factors associated with development of HAIs.
Odds Ratio 95% confidence intervals p
Age 0.99 0.96-1.01 0.58
Male gender 0.76 0.37-1.53 0.45
DM 1.29 0.62-2.69 0.48
ESRD 1.05 0.24-4.55 0.94
COPD 0.82 0.38-1.77 0.62
Cancer 1.36 0.37-4.98 0.63
Hydroxychloroquine* 2.96 1.00-8.86 0.05
Steroids* 3.79 1.44-10.01 0.007
Tocilizumab* 5.04 2.39-10.65 <0.001
Convalescent plasma 1.86 0.88-3.92 0.10
Central venous catheter 2.47 0.87-6.97 0.088
Mechanical ventilation 1.11 0.34-3.54 0.86
AKI requiring hemodialysis* 3.67 1.05-12.80 0.04
Antibiotics on admission 1.02 0.31-3.32 0.96
SOFA score > 2 on admission 1.21 0.52-2.76 0.65
Multiple host and environmental factors determine development of HAI in any hospitalized patient. Secondary infections in post viral infection emanate from increased immune susceptibility, increasing risk for co-infections and HAIs(Hendaus et al., 2015, Smith and McCullers, 2014). In patients with influenza virus, there is increased susceptibility to bacterial adhesion by bacteria such as Streptococcus and pseudomonas(Pittet et al., 2010). Antibiotics used for bacterial infections can result in changes in normal flora of the host(Haak and Wiersinga, 2017). Patients with prolonged hospital course are at risk of nosocomial infections as well.
However, modifiable factors such as – protocols for mitigation of infection, staff education and practices and hospital policies for lines and catheters also play a role. Before COVID-19 pandemic, various CMS measures were in place at our hospital to prevent VAP, CLABSI and CAUTI. With immense surges of COVID-19, various system in place were under stress and unable to be utilized at their normal capacity. At our center, number of covid-19 patients in the ICU in the peak times ranged from 30 to 45. Our hospital system supported the COVID-19 crisis with additional 50 beds in surge capacity. Both doctors and nursing capacity was increased to cope up with the increase in patient volumes. Due to this extra support, we were still able to perform CMS core measures to a reasonable extent. Our rates of HAI may be difficult to compare to other hospital system due to this reason.
Similar to previous studies on HAIs, use of central venous lines was associated with higher rates of both bacterial and fungal infections(Muskett et al., 2011, Zhao et al., 2016). Use of antibiotics was associated with risk of fungal infections. All 11 patients who developed fungal infections received antibiotics on admission. Since most of the COVID-19 patients present with fever, cough and CXR findings of pneumonia, many of these patients receive empiric antibiotics for community acquired pneumonia. However, patients receiving cefepime and vancomycin on admission had higher rates of HAIs.
Tocilizumab has been used very often in COVID-19 due to concerns about cytokine storm from deregulated IL6 pathway being the underlying problem causing the severe disease(Morena et al., 2020). This drug has been used for MAS-HLH induced cytokine storm in disease like rheumatoid arthritis(Grom et al., 2016). Its use in ICU has not been studied and is likely not risk free as it blocks IL6R which is an important regulator of immune system. Tocilizumab was studied in 16,074 rheumatoid arthritis patients and was associated with increased risks bacterial infections – diverticulitis, pneumonia, and bacteremia(Pawar et al., 2019). Tocilizumab was given to 210 patients with severe COVID-19 at our center, of which 42(20%) developed HAI. We observed tocilizumab was associated with increased risk of HAIs.. In COVID-19 patients receiving tocilizumab, HAI has been reported to be around 27-32% (Morena et al., 2020, Rossotti et al., 2020).
In the initial phase of the pandemic, Hydroxychloroquine, was also used extensively at our center and 220 patients received this medication. Hydroxychloroquine destabilizes lysosomal membranes and promotes the release of lysosomal enzymes inside cells which can inhibit the function of lymphocytes and thus has immunomodulatory and anti-inflammatory effects(Schrezenmeier and Dorner, 2020). We found its association with HAIs, though the lower limit of confidence interval was one..
Though we have focused on culture positive organisms, there are significant number of patients who may have HAI but were culture negative. Both timing of cultures and use of antibiotics can affect the positivity of culture rates, with up to 50% of infection in ICU being culture negative(Gupta et al., 2016). To study these patients, we defined any fever/leukocytosis along with getting blood cultures and initiation of empiric antibiotics as possible infection. We observed only 40% culture positive rates when patient qualified as ‘possible infection’.
Though we report our experience with over 1500 covid-19 patients, our study has certain limitations. It is a single center study and it would be difficult to extrapolate the results to other centers which may have different support systems, protocols for prevention of infections and usage of COVID-19 medications. Retrospective nature of our study prevents us from drawing any causative risk factors. There may be other underlying factors that we do not know that would affect development of HAI in COVID-19 patients. Lastly, there were missing values with respect to the inflammatory markers. We were therefore unable to use them in our regression models which may have improved our model for this disease.
Despite these limitations, our study provides insight into rates of HAIs in this group of patients and assess risk factors.. Cautious use of hydroxychloroquine and tocilizumab is advised as these drugs can lead to higher rates of HAIs and have not shown to be beneficial (Kiley, 2020, Relations RGM, 2020). CMS guidelines for prevention of hospital acquired infections is vital.
Author contribution
Gagan Kumar MD: study design, data analysis, manuscript writing
Martin Herrera MD: manuscript writing
Alex Adams MD: study design, manuscript writing
Vartika Singh MD: study design, manuscript writing
Mark Meersman CPA: data validation
Drew Dalton BS: data validation
Dhaval Patel MD: study design, manuscript writing
Erine Rojas: study design, manuscript writing
Shravan Kethireddy: study design, manuscript writing
Ankit Sakhuja: Study design, data analysis, manuscript writing
Achuta Kumar Guddati MD: Study design, data analysis, Manuscript writing
Rahul Nanchal MD: Study design, data analysis, manuscript writing
Ethical approval
The study was reviewed and found exempt by Northeast Georgia Health System IRB board
Conflict of interest
The authors declare no competing financial interests.
Financial Support/Grant
None.
Appendix A Supplementary data
The following are Supplementary data to this article: Appendix A Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijid.2020.11.135.
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Document structure show
| article-title | Predictors and Outcomes of HAIs in COVID-19 Patients |
| abstract | Highlights • Of 1565 patient, 140 (8.9%) separate HAIs from 73 different organisms developed in 59 (3.7%) patients. • Tocilizumab, steroids and hydroxychloroquine are associated with higher rate of HAIs. • HAIs are not associated with increased risk of death. |
| title | Highlights |
| p | • Of 1565 patient, 140 (8.9%) separate HAIs from 73 different organisms developed in 59 (3.7%) patients. • Tocilizumab, steroids and hydroxychloroquine are associated with higher rate of HAIs. • HAIs are not associated with increased risk of death. |
| label | • |
| p | Of 1565 patient, 140 (8.9%) separate HAIs from 73 different organisms developed in 59 (3.7%) patients. |
| label | • |
| p | Tocilizumab, steroids and hydroxychloroquine are associated with higher rate of HAIs. |
| label | • |
| p | HAIs are not associated with increased risk of death. |
| abstract | Introduction Healthcare Associated infections (HAI) after a viral illness are important source of morbidity and mortality, this has not been studied well in hospitalized COVID-19 patients. Methods We included all COVID-19 positive adult patients (≥18 years) hospitalized between March 1, 2020 to August 5th, 2020. We used CDC definitions of HAI in the acute care setting. Outcomes studied were rates and types of infections and in hospital mortality.. We constructed several multivariable logistic regression models to examine characteristics associated with development of HAI. Results Of 1565 patients, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of these, 23 were gram positive, 39 were gram negative and 11 were fungal. Patient developing HAI did not have higher odds of death (OR 0.85,95%CI 0.40-1.81, p = 0.69). HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). Conclusions HAI are common in hospitalized covid-19 patients. Tocilizumab and steroids were associated with increased risk of HAIs. |
| sec | Introduction Healthcare Associated infections (HAI) after a viral illness are important source of morbidity and mortality, this has not been studied well in hospitalized COVID-19 patients. |
| title | Introduction |
| p | Healthcare Associated infections (HAI) after a viral illness are important source of morbidity and mortality, this has not been studied well in hospitalized COVID-19 patients. |
| sec | Methods We included all COVID-19 positive adult patients (≥18 years) hospitalized between March 1, 2020 to August 5th, 2020. We used CDC definitions of HAI in the acute care setting. Outcomes studied were rates and types of infections and in hospital mortality.. We constructed several multivariable logistic regression models to examine characteristics associated with development of HAI. |
| title | Methods |
| p | We included all COVID-19 positive adult patients (≥18 years) hospitalized between March 1, 2020 to August 5th, 2020. We used CDC definitions of HAI in the acute care setting. Outcomes studied were rates and types of infections and in hospital mortality.. We constructed several multivariable logistic regression models to examine characteristics associated with development of HAI. |
| sec | Results Of 1565 patients, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of these, 23 were gram positive, 39 were gram negative and 11 were fungal. Patient developing HAI did not have higher odds of death (OR 0.85,95%CI 0.40-1.81, p = 0.69). HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). |
| title | Results |
| p | Of 1565 patients, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of these, 23 were gram positive, 39 were gram negative and 11 were fungal. Patient developing HAI did not have higher odds of death (OR 0.85,95%CI 0.40-1.81, p = 0.69). HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). |
| sec | Conclusions HAI are common in hospitalized covid-19 patients. Tocilizumab and steroids were associated with increased risk of HAIs. |
| title | Conclusions |
| p | HAI are common in hospitalized covid-19 patients. Tocilizumab and steroids were associated with increased risk of HAIs. |
| body | INTRODUCTION Secondary infections post viral illnesses occur frequently and may lead to adverse outcomes. In previous influenza epidemics, for example, many deaths were the direct result of secondary bacterial pneumonia(Morens et al., 2008). Though seemingly common, these infections remain poorly characterized. In a few small studies during the 2009 H1N1 pandemic(Dhanoa et al., 2011), approximately 19% of cases were reported to have secondary bacterial infection of which Streptococcus pneumoniae was the most common isolate (MacIntyre et al., 2018). Reports describing the epidemiology of secondary infections associated with COVID-19 pneumonia are limited and have small sample sizes. Zhou et al reported 50% rate of secondary infection in people who died compared to 1% in survivors(Zhou et al., 2020). Chen et al reported 4% rates of fungal infection(Chen et al., 2020). Though management strategies for COVID-19 have evolved through the pandemic, use of therapies with immune modulating properties such as IL-6 receptor antagonists and corticosteroids is common. (Geleris et al., 2020, Jordan et al., 2020, Morena et al., 2020, Selvaraj et al., 2020, Valk et al., 2020). These medications are used in attempts to quell the hyperactive host immune response that appears to play a key role in clinical deterioration(Wiersinga et al., 2020). Many patients are also likely to be exposed to antibiotics secondary to ongoing fever which may not necessarily represent superimposed infections. Further in persons who are critically ill, the use of central venous and urinary catheters is common (O’Grady et al., 2011). These factors combined likely raise susceptibility to the acquisition of secondary infections in COVID-19. The goals of this study are to describe the epidemiology of secondary infections after hospitalization for COVID-19 i. Since these are acquired while patients are hospitalized, we used CDC surveillance definition of healthcare associated infection (HAI) to study these infections (Horan et al., 2008). As an exploratory aim we also attempted to discern risk factors for acquiring such infections. METHODS Study design and Data source We performed a retrospective analysis of adult covid-19 patients (age ≥18 years) admitted to a large community hospital in a rural setting in Northeast Georgia between March 1, 2020 to June 10th, 2020. COVID-19 patients were identified from our Epic® EMR using ICD10CM and/or CPT codes for COVID-19 and/or positive for SARS CoV-2- PCR. We obtained clinical and demographical details from Epic® Caboodle data warehouse and Cerner APACHE® Outcomes. Systems integration was provided by IPC Global by leveraging their in-Process Data Factory innovation running on an AWS® VPC. We excluded COVID-19 patients who required readmission to the hospital after initial discharge. The study was reviewed and found exempt by Northeast Georgia Health System IRB board. General management of covid-19 patients Patients were admitted to ICU if their FiO2 requirement was higher than 50% (> 10LPM of nasal cannula or >50% on high flow oxygen). All patients received therapeutic anticoagulation if they had D-dimer > 2 and SIC score > 3 (Ding et al., 2018). Initially all admitted patients received hydroxychloroquine till data regarding efficacy was released, thereafter patients did not receive this medication(Shah et al., 2020, Tang et al., 2020). We administered tocilizumab if patients met laboratory parameters supportive of cytokine storm which included 3 or more of the following - 1. Ferritin >300 ng/mL with doubling within 24 hours, 2. Ferritin >600 ng/mL, 3. LDH > 250 U/L, 4. Elevated D dimer (> 2 mcg/mL FEU), 5. High sensitivity CRP greater than 7 mg/L or 6. H score greater than 110. We used convalescent plasma if patients had dyspnea, RR ≥ 30, PF ratio ≤300 or had the treating clinician adjudicated life-threatening disease. We provided standard CMS guidelines-based management for prevention of VAP, CAUTI and CLABSI. Definitions We reviewed culture data to ascertain the presence of infection. We classified infections as respiratory (tracheal aspirate, bronchoalveolar lavage), blood, urine, and other (body fluids such as pleural cavity, abdominal cavity). We used CDC definitions of healthcare associated infections(HAIs) in the acute care setting (Horan et al., 2008). We deemed that a HAI was present if cultures were positive and obtained after 3 days of hospital admission. We defined days to positive cultures as time from day of admission to the day the culture was collected.. Since cultures can be negative, we defined ‘possible infection’ if patient developed fever > 100.4Of after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this fever. We also included within this definition if WBC count rose to > 15000 after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this elevation in WBC. Possible infection was not included in HAI. Outcomes Our primary outcome of interest were rates of HAIs. Secondary outcomes included in hospital mortality. Statistics We performed all statistical analysis using STATA MC 16.0 (Stata-Corp, College Station, Tx). We describe categorical data using frequency count and percentages. We report medians and inter quartile ranges for continuous variables as they were not normally distributed. We compared demographical and clinical characteristics of persons who had HAIs with those who did not using chi square and Wilcoxon rank tests for categorical and continuous variables, respectively. For all analyses we deemed statistical significance a p-value < 0.05. We used multivariable logistic regression models to determine factors associated with the development of HAIs in COVID-19. We used single predictor logistic regression to identify significant associations between putative risk factors and development of secondary infections. Variables found significant at p < 0.10 were candidates for inclusion in our primary model. Regardless of significance we included variables known to be associated with outcomes in COVID-19 as well as variables previously identified as risk factors for developing secondary infections. Backward elimination method was used to remove other variables from the final model. Final model was bootstrapped using 2,000 bootstrap replicates and case resampling with replacement from the original dataset. We used multivariable logistic regression analysis to examine association of HAIs with in-hospital mortality. Since patients who developed HAIs were sicker and had higher inflammatory markers, we used propensity scores to adjust for these differences. Propensity score was used to identify probability that a patient would receive develop HAIs. This was calculated for every patient. Propensity score was computed using multivariable logistic regression model with HAIs as the dependent variable and incorporating multiple independent variables which included demographic and clinical characteristics along with laboratory markers. The propensity score thus obtained was used as a continuous variable to further adjust the above regression. RESULTS There were 1565 patients with COVID-19 within the study period. Of these, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of 140 HAIs, 53 were bacteremia, 67 were pneumonia and 17 were UTI. 38 patients had bacteremia from single organism and 15 patients had bacteremia from more than one organism. 73 separate organisms constituted 140 HAIs. Of these, 23(31.5%) were gram positive infections, 39(53.4%) were gram negative and 11(15%) were fungal infections. Among gram negatives, Pseudomonas (n = 10), Escherichia coli (n = 7) and Klebsiella (n = 7) and among gram positive – staphylococcus aureus(n = 13) and enterococcus (n = 5) were commonest bacterial organisms. Five out of eleven candidal infections were non albicans species. The details of organism, source, and median time to development of HAI is shown in Table 3. Fourteen patients developed clostridium difficile infections. There were 118 instances which qualified as possible infections. Of these 48 (40.7%) qualified as culture positive secondary infection. Demographics & Clinical Characteristics There was male and black race had higher rates of HAIs (Table 1 ). Rates of HAIs were higher in co-morbidities such as diabetes mellitus and ESRD (Table 1). HAIs were observed to be higher in patients receiving treatment for covid-19 such as hydroxychloroquine (27.4% vs 13.6%, p < 0.001), convalescent plasma (50.9% vs 14.1%, p < 0.001), tocilizumab (66.7% vs 11.6%, p < 0.001) and steroids (78.4% vs 40.2%, p < 0.001). Patients who received cefepime and vancomycin on admission developed higher rates of secondary infection (Table 1). Table 1 Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. No HAI HAI p Total 1514 59 Age 62(48-75) 61(52-69) 0.45 Male (%) 51.1 64.7 0.06 Race (%) 0.009 White 60.8 45.1 Blacks 9.4 17.7 Hispanics 26.0 25.5 Asians/pacific Islander 1 3.9 Others 3.0 7.8 Co-morbidities (%) Hypertension 63.2 70.6 0.28 Congestive heart failure 23.3 27.5 0.48 Diabetes Mellitus 38.6 56.8 0.009 COPD 29.7 21.6 0.3 ESRD 3.4 7.8 0.09 Cirrhosis 9.9 5.9 0.34 Cancer 11.5 9.8 0.69 Rheumatological Diseases 3.3 3.9 0.80 Home meds (%) Anticoagulation 10.4 7.8 0.55 Anti-platelets 17.4 11.7 0.30 ACE/ARB 25.8 35.3 0.13 Immunosuppressants 0.07 0.00 0.82 COVID-19 Medications (%) Vitamin C 70.1 90.2 0.002 Zinc 69.7 88.2 0.004 Hydroxychloroquine 13.6 27.4 0.005 Tocilizumab 11.6 66.7 <0.001 Steroids 40.2 78.4 <0.001 Convalescent plasma 14.1 50.9 <0.001 Remedesivir 28.2 50.9 <0.001 Therapeutic anticoagulation 73.3 96.1 <0.001 Antibiotics started at time of admission (%) Azithromycin 48.8 64.7 0.025 Doxycycline 8.1 11.7 0.34 Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 Clindamycin 1.1 1.9 0.58 Ceftriaxone 55.0 62.7 0.27 Cefepime 11.7 23.5 0.01 Piperacillin/tazobactam 11.5 13.7 0.63 Vancomycin 22.2 43.1 0.001 Sicker patients on admission with higher SOFA score and higher inflammatory markers (such as ferritin, CRP, and d-dimer) developed higher rates of secondary infection (Table 2 ). Table 2 Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. No HAI HAI p Total 1506 59 SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 Initial laboratory studies* WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 Platelets 212(164-275),1499 228(161-317),58 0.35 Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 BUN 16(11-25),1446 20(14-33),58 0.008 Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 ALT 32(22-54),1436 42(28-67),57 0.03 Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 Ferritin 373(158-805),1083 743(442-1450),57 0.001 CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 ICU admissions 422(28.0%) 58(98.3%) 0.001 SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 Required proning if on ventilator (%) 14.6 30.4 0.007 Required paralytic if on ventilator (%) 23.5 51.8 0.001 Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 Use of vasopressors Required Norepinephrine (%) 11.9 83.0 <0.001 Required vasopressin (%) 5.5 52.5 <0.001 Required epinephrine (%) 2.1 13.7 <0.001 Required angiotensin 2(%) 0.5 1.7 0.24 CVL before HAI (%) 8.1 45.8 <0.001 Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 Acute DVT/PE (%) 4.2 22.0 <0.001 * median (Inter Quartile range), Number of samples. Table 3 Hospital Associated infections: organisms and source and time to infection. Number of patients Source Median time to infection Blood Respiratory Urine others Gram positive infections MRSA 7 5 7 0 1 14 MSSA 6 5 6 1 0 13 Staphylococcus hominis 1 1 1 0 0 14 Staphylococcus epidermidis 4 4 4 0 0 15 Group D Streptococcus 5 5 4 1 0 12.5 Gram negative infections E. coli 7 5 4 4 0 19 Klebsiella 7 2 7 1 0 21 Pseudomonas 10 7 10 3 1 15.5 Serratia 3 2 3 1 0 28 Enterobacter 6 3 6 1 0 19.5 Proteus 2 2 2 1 0 21.5 Citrobacter 1 0 1 0 0 24 Acinetobacter 2 0 2 0 0 26 Stenotrophomonas 1 1 1 0 0 8 Fungal infections Candida albicans 6 6 5 2 1 13 Non albicans Candida 5 5 4 2 0 15 C. difficile 14 Patients developing secondary infections were more often on the mechanical ventilation (42.4% vs 10.0%, p < 0.001) and had central venous lines (45.8% vs 8.1%, p < 0.001). Multivariable analysis of HAIs HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). Other medications – convalescent plasma and remedesivir were not associated with increased rates of HAIs. Outcomes Patients developing secondary infection had significantly higher in hospital mortality when compared to those who did not develop secondary infection (40.7% vs 11.8% p < 0.001) (Table 4 ). However, on multivariable analysis, secondary infections were not associated with increased risk of death (OR 0.85, 95%CI 0.40-1.80, p = 0.67) (Appendix 1). Table 4 Outcomes of patients with Hospital Associated Infections No HAI HAI p Total Died (%) 11.8 40.7 <0.001 LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 Disposition (%) <0.001 Home 70.8 26.5 Home with health 12.9 17.6 Rehab/SNF/LTAC/Acute care 13.6 35.3 Others 2.6 20.6 The length of hospital stay was significantly longer in patients developing secondary infections. Disposition in patients with secondary infection was significantly higher to skilled nursing facility and long-term acute care (SNF/LTAC) when compared to those who did not have secondary infection (35.3% vs 13.6%, p < 0.001). DISCUSSION COVID-19 pandemic has observed use of immunomodulating medications to help prevent dysregulated immune response also called cytokine storm(Wiersinga et al., 2020). We report our experience of all COVID-19 patients who were admitted to the hospital till August 5thth,2020. There were more than 1500 patients, of which 250 patients required intubation from severe COVID-19. We have observed number of patients that develop HAIs to be about 3.7% (Table 5 ). Table 5 Factors associated with development of HAIs. Odds Ratio 95% confidence intervals p Age 0.99 0.96-1.01 0.58 Male gender 0.76 0.37-1.53 0.45 DM 1.29 0.62-2.69 0.48 ESRD 1.05 0.24-4.55 0.94 COPD 0.82 0.38-1.77 0.62 Cancer 1.36 0.37-4.98 0.63 Hydroxychloroquine* 2.96 1.00-8.86 0.05 Steroids* 3.79 1.44-10.01 0.007 Tocilizumab* 5.04 2.39-10.65 <0.001 Convalescent plasma 1.86 0.88-3.92 0.10 Central venous catheter 2.47 0.87-6.97 0.088 Mechanical ventilation 1.11 0.34-3.54 0.86 AKI requiring hemodialysis* 3.67 1.05-12.80 0.04 Antibiotics on admission 1.02 0.31-3.32 0.96 SOFA score > 2 on admission 1.21 0.52-2.76 0.65 Multiple host and environmental factors determine development of HAI in any hospitalized patient. Secondary infections in post viral infection emanate from increased immune susceptibility, increasing risk for co-infections and HAIs(Hendaus et al., 2015, Smith and McCullers, 2014). In patients with influenza virus, there is increased susceptibility to bacterial adhesion by bacteria such as Streptococcus and pseudomonas(Pittet et al., 2010). Antibiotics used for bacterial infections can result in changes in normal flora of the host(Haak and Wiersinga, 2017). Patients with prolonged hospital course are at risk of nosocomial infections as well. However, modifiable factors such as – protocols for mitigation of infection, staff education and practices and hospital policies for lines and catheters also play a role. Before COVID-19 pandemic, various CMS measures were in place at our hospital to prevent VAP, CLABSI and CAUTI. With immense surges of COVID-19, various system in place were under stress and unable to be utilized at their normal capacity. At our center, number of covid-19 patients in the ICU in the peak times ranged from 30 to 45. Our hospital system supported the COVID-19 crisis with additional 50 beds in surge capacity. Both doctors and nursing capacity was increased to cope up with the increase in patient volumes. Due to this extra support, we were still able to perform CMS core measures to a reasonable extent. Our rates of HAI may be difficult to compare to other hospital system due to this reason. Similar to previous studies on HAIs, use of central venous lines was associated with higher rates of both bacterial and fungal infections(Muskett et al., 2011, Zhao et al., 2016). Use of antibiotics was associated with risk of fungal infections. All 11 patients who developed fungal infections received antibiotics on admission. Since most of the COVID-19 patients present with fever, cough and CXR findings of pneumonia, many of these patients receive empiric antibiotics for community acquired pneumonia. However, patients receiving cefepime and vancomycin on admission had higher rates of HAIs. Tocilizumab has been used very often in COVID-19 due to concerns about cytokine storm from deregulated IL6 pathway being the underlying problem causing the severe disease(Morena et al., 2020). This drug has been used for MAS-HLH induced cytokine storm in disease like rheumatoid arthritis(Grom et al., 2016). Its use in ICU has not been studied and is likely not risk free as it blocks IL6R which is an important regulator of immune system. Tocilizumab was studied in 16,074 rheumatoid arthritis patients and was associated with increased risks bacterial infections – diverticulitis, pneumonia, and bacteremia(Pawar et al., 2019). Tocilizumab was given to 210 patients with severe COVID-19 at our center, of which 42(20%) developed HAI. We observed tocilizumab was associated with increased risk of HAIs.. In COVID-19 patients receiving tocilizumab, HAI has been reported to be around 27-32% (Morena et al., 2020, Rossotti et al., 2020). In the initial phase of the pandemic, Hydroxychloroquine, was also used extensively at our center and 220 patients received this medication. Hydroxychloroquine destabilizes lysosomal membranes and promotes the release of lysosomal enzymes inside cells which can inhibit the function of lymphocytes and thus has immunomodulatory and anti-inflammatory effects(Schrezenmeier and Dorner, 2020). We found its association with HAIs, though the lower limit of confidence interval was one.. Though we have focused on culture positive organisms, there are significant number of patients who may have HAI but were culture negative. Both timing of cultures and use of antibiotics can affect the positivity of culture rates, with up to 50% of infection in ICU being culture negative(Gupta et al., 2016). To study these patients, we defined any fever/leukocytosis along with getting blood cultures and initiation of empiric antibiotics as possible infection. We observed only 40% culture positive rates when patient qualified as ‘possible infection’. Though we report our experience with over 1500 covid-19 patients, our study has certain limitations. It is a single center study and it would be difficult to extrapolate the results to other centers which may have different support systems, protocols for prevention of infections and usage of COVID-19 medications. Retrospective nature of our study prevents us from drawing any causative risk factors. There may be other underlying factors that we do not know that would affect development of HAI in COVID-19 patients. Lastly, there were missing values with respect to the inflammatory markers. We were therefore unable to use them in our regression models which may have improved our model for this disease. Despite these limitations, our study provides insight into rates of HAIs in this group of patients and assess risk factors.. Cautious use of hydroxychloroquine and tocilizumab is advised as these drugs can lead to higher rates of HAIs and have not shown to be beneficial (Kiley, 2020, Relations RGM, 2020). CMS guidelines for prevention of hospital acquired infections is vital. Author contribution Gagan Kumar MD: study design, data analysis, manuscript writing Martin Herrera MD: manuscript writing Alex Adams MD: study design, manuscript writing Vartika Singh MD: study design, manuscript writing Mark Meersman CPA: data validation Drew Dalton BS: data validation Dhaval Patel MD: study design, manuscript writing Erine Rojas: study design, manuscript writing Shravan Kethireddy: study design, manuscript writing Ankit Sakhuja: Study design, data analysis, manuscript writing Achuta Kumar Guddati MD: Study design, data analysis, Manuscript writing Rahul Nanchal MD: Study design, data analysis, manuscript writing Ethical approval The study was reviewed and found exempt by Northeast Georgia Health System IRB board Conflict of interest The authors declare no competing financial interests. Financial Support/Grant None. |
| sec | INTRODUCTION Secondary infections post viral illnesses occur frequently and may lead to adverse outcomes. In previous influenza epidemics, for example, many deaths were the direct result of secondary bacterial pneumonia(Morens et al., 2008). Though seemingly common, these infections remain poorly characterized. In a few small studies during the 2009 H1N1 pandemic(Dhanoa et al., 2011), approximately 19% of cases were reported to have secondary bacterial infection of which Streptococcus pneumoniae was the most common isolate (MacIntyre et al., 2018). Reports describing the epidemiology of secondary infections associated with COVID-19 pneumonia are limited and have small sample sizes. Zhou et al reported 50% rate of secondary infection in people who died compared to 1% in survivors(Zhou et al., 2020). Chen et al reported 4% rates of fungal infection(Chen et al., 2020). Though management strategies for COVID-19 have evolved through the pandemic, use of therapies with immune modulating properties such as IL-6 receptor antagonists and corticosteroids is common. (Geleris et al., 2020, Jordan et al., 2020, Morena et al., 2020, Selvaraj et al., 2020, Valk et al., 2020). These medications are used in attempts to quell the hyperactive host immune response that appears to play a key role in clinical deterioration(Wiersinga et al., 2020). Many patients are also likely to be exposed to antibiotics secondary to ongoing fever which may not necessarily represent superimposed infections. Further in persons who are critically ill, the use of central venous and urinary catheters is common (O’Grady et al., 2011). These factors combined likely raise susceptibility to the acquisition of secondary infections in COVID-19. The goals of this study are to describe the epidemiology of secondary infections after hospitalization for COVID-19 i. Since these are acquired while patients are hospitalized, we used CDC surveillance definition of healthcare associated infection (HAI) to study these infections (Horan et al., 2008). As an exploratory aim we also attempted to discern risk factors for acquiring such infections. |
| title | INTRODUCTION |
| p | Secondary infections post viral illnesses occur frequently and may lead to adverse outcomes. In previous influenza epidemics, for example, many deaths were the direct result of secondary bacterial pneumonia(Morens et al., 2008). Though seemingly common, these infections remain poorly characterized. In a few small studies during the 2009 H1N1 pandemic(Dhanoa et al., 2011), approximately 19% of cases were reported to have secondary bacterial infection of which Streptococcus pneumoniae was the most common isolate (MacIntyre et al., 2018). Reports describing the epidemiology of secondary infections associated with COVID-19 pneumonia are limited and have small sample sizes. Zhou et al reported 50% rate of secondary infection in people who died compared to 1% in survivors(Zhou et al., 2020). Chen et al reported 4% rates of fungal infection(Chen et al., 2020). |
| p | Though management strategies for COVID-19 have evolved through the pandemic, use of therapies with immune modulating properties such as IL-6 receptor antagonists and corticosteroids is common. (Geleris et al., 2020, Jordan et al., 2020, Morena et al., 2020, Selvaraj et al., 2020, Valk et al., 2020). These medications are used in attempts to quell the hyperactive host immune response that appears to play a key role in clinical deterioration(Wiersinga et al., 2020). Many patients are also likely to be exposed to antibiotics secondary to ongoing fever which may not necessarily represent superimposed infections. Further in persons who are critically ill, the use of central venous and urinary catheters is common (O’Grady et al., 2011). These factors combined likely raise susceptibility to the acquisition of secondary infections in COVID-19. |
| p | The goals of this study are to describe the epidemiology of secondary infections after hospitalization for COVID-19 i. Since these are acquired while patients are hospitalized, we used CDC surveillance definition of healthcare associated infection (HAI) to study these infections (Horan et al., 2008). As an exploratory aim we also attempted to discern risk factors for acquiring such infections. |
| sec | METHODS Study design and Data source We performed a retrospective analysis of adult covid-19 patients (age ≥18 years) admitted to a large community hospital in a rural setting in Northeast Georgia between March 1, 2020 to June 10th, 2020. COVID-19 patients were identified from our Epic® EMR using ICD10CM and/or CPT codes for COVID-19 and/or positive for SARS CoV-2- PCR. We obtained clinical and demographical details from Epic® Caboodle data warehouse and Cerner APACHE® Outcomes. Systems integration was provided by IPC Global by leveraging their in-Process Data Factory innovation running on an AWS® VPC. We excluded COVID-19 patients who required readmission to the hospital after initial discharge. The study was reviewed and found exempt by Northeast Georgia Health System IRB board. General management of covid-19 patients Patients were admitted to ICU if their FiO2 requirement was higher than 50% (> 10LPM of nasal cannula or >50% on high flow oxygen). All patients received therapeutic anticoagulation if they had D-dimer > 2 and SIC score > 3 (Ding et al., 2018). Initially all admitted patients received hydroxychloroquine till data regarding efficacy was released, thereafter patients did not receive this medication(Shah et al., 2020, Tang et al., 2020). We administered tocilizumab if patients met laboratory parameters supportive of cytokine storm which included 3 or more of the following - 1. Ferritin >300 ng/mL with doubling within 24 hours, 2. Ferritin >600 ng/mL, 3. LDH > 250 U/L, 4. Elevated D dimer (> 2 mcg/mL FEU), 5. High sensitivity CRP greater than 7 mg/L or 6. H score greater than 110. We used convalescent plasma if patients had dyspnea, RR ≥ 30, PF ratio ≤300 or had the treating clinician adjudicated life-threatening disease. We provided standard CMS guidelines-based management for prevention of VAP, CAUTI and CLABSI. Definitions We reviewed culture data to ascertain the presence of infection. We classified infections as respiratory (tracheal aspirate, bronchoalveolar lavage), blood, urine, and other (body fluids such as pleural cavity, abdominal cavity). We used CDC definitions of healthcare associated infections(HAIs) in the acute care setting (Horan et al., 2008). We deemed that a HAI was present if cultures were positive and obtained after 3 days of hospital admission. We defined days to positive cultures as time from day of admission to the day the culture was collected.. Since cultures can be negative, we defined ‘possible infection’ if patient developed fever > 100.4Of after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this fever. We also included within this definition if WBC count rose to > 15000 after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this elevation in WBC. Possible infection was not included in HAI. Outcomes Our primary outcome of interest were rates of HAIs. Secondary outcomes included in hospital mortality. Statistics We performed all statistical analysis using STATA MC 16.0 (Stata-Corp, College Station, Tx). We describe categorical data using frequency count and percentages. We report medians and inter quartile ranges for continuous variables as they were not normally distributed. We compared demographical and clinical characteristics of persons who had HAIs with those who did not using chi square and Wilcoxon rank tests for categorical and continuous variables, respectively. For all analyses we deemed statistical significance a p-value < 0.05. We used multivariable logistic regression models to determine factors associated with the development of HAIs in COVID-19. We used single predictor logistic regression to identify significant associations between putative risk factors and development of secondary infections. Variables found significant at p < 0.10 were candidates for inclusion in our primary model. Regardless of significance we included variables known to be associated with outcomes in COVID-19 as well as variables previously identified as risk factors for developing secondary infections. Backward elimination method was used to remove other variables from the final model. Final model was bootstrapped using 2,000 bootstrap replicates and case resampling with replacement from the original dataset. We used multivariable logistic regression analysis to examine association of HAIs with in-hospital mortality. Since patients who developed HAIs were sicker and had higher inflammatory markers, we used propensity scores to adjust for these differences. Propensity score was used to identify probability that a patient would receive develop HAIs. This was calculated for every patient. Propensity score was computed using multivariable logistic regression model with HAIs as the dependent variable and incorporating multiple independent variables which included demographic and clinical characteristics along with laboratory markers. The propensity score thus obtained was used as a continuous variable to further adjust the above regression. |
| title | METHODS |
| sec | Study design and Data source We performed a retrospective analysis of adult covid-19 patients (age ≥18 years) admitted to a large community hospital in a rural setting in Northeast Georgia between March 1, 2020 to June 10th, 2020. COVID-19 patients were identified from our Epic® EMR using ICD10CM and/or CPT codes for COVID-19 and/or positive for SARS CoV-2- PCR. We obtained clinical and demographical details from Epic® Caboodle data warehouse and Cerner APACHE® Outcomes. Systems integration was provided by IPC Global by leveraging their in-Process Data Factory innovation running on an AWS® VPC. We excluded COVID-19 patients who required readmission to the hospital after initial discharge. The study was reviewed and found exempt by Northeast Georgia Health System IRB board. |
| title | Study design and Data source |
| p | We performed a retrospective analysis of adult covid-19 patients (age ≥18 years) admitted to a large community hospital in a rural setting in Northeast Georgia between March 1, 2020 to June 10th, 2020. COVID-19 patients were identified from our Epic® EMR using ICD10CM and/or CPT codes for COVID-19 and/or positive for SARS CoV-2- PCR. We obtained clinical and demographical details from Epic® Caboodle data warehouse and Cerner APACHE® Outcomes. Systems integration was provided by IPC Global by leveraging their in-Process Data Factory innovation running on an AWS® VPC. We excluded COVID-19 patients who required readmission to the hospital after initial discharge. The study was reviewed and found exempt by Northeast Georgia Health System IRB board. |
| sec | General management of covid-19 patients Patients were admitted to ICU if their FiO2 requirement was higher than 50% (> 10LPM of nasal cannula or >50% on high flow oxygen). All patients received therapeutic anticoagulation if they had D-dimer > 2 and SIC score > 3 (Ding et al., 2018). Initially all admitted patients received hydroxychloroquine till data regarding efficacy was released, thereafter patients did not receive this medication(Shah et al., 2020, Tang et al., 2020). We administered tocilizumab if patients met laboratory parameters supportive of cytokine storm which included 3 or more of the following - 1. Ferritin >300 ng/mL with doubling within 24 hours, 2. Ferritin >600 ng/mL, 3. LDH > 250 U/L, 4. Elevated D dimer (> 2 mcg/mL FEU), 5. High sensitivity CRP greater than 7 mg/L or 6. H score greater than 110. We used convalescent plasma if patients had dyspnea, RR ≥ 30, PF ratio ≤300 or had the treating clinician adjudicated life-threatening disease. We provided standard CMS guidelines-based management for prevention of VAP, CAUTI and CLABSI. |
| title | General management of covid-19 patients |
| p | Patients were admitted to ICU if their FiO2 requirement was higher than 50% (> 10LPM of nasal cannula or >50% on high flow oxygen). All patients received therapeutic anticoagulation if they had D-dimer > 2 and SIC score > 3 (Ding et al., 2018). Initially all admitted patients received hydroxychloroquine till data regarding efficacy was released, thereafter patients did not receive this medication(Shah et al., 2020, Tang et al., 2020). We administered tocilizumab if patients met laboratory parameters supportive of cytokine storm which included 3 or more of the following - 1. Ferritin >300 ng/mL with doubling within 24 hours, 2. Ferritin >600 ng/mL, 3. LDH > 250 U/L, 4. Elevated D dimer (> 2 mcg/mL FEU), 5. High sensitivity CRP greater than 7 mg/L or 6. H score greater than 110. We used convalescent plasma if patients had dyspnea, RR ≥ 30, PF ratio ≤300 or had the treating clinician adjudicated life-threatening disease. We provided standard CMS guidelines-based management for prevention of VAP, CAUTI and CLABSI. |
| sec | Definitions We reviewed culture data to ascertain the presence of infection. We classified infections as respiratory (tracheal aspirate, bronchoalveolar lavage), blood, urine, and other (body fluids such as pleural cavity, abdominal cavity). We used CDC definitions of healthcare associated infections(HAIs) in the acute care setting (Horan et al., 2008). We deemed that a HAI was present if cultures were positive and obtained after 3 days of hospital admission. We defined days to positive cultures as time from day of admission to the day the culture was collected.. Since cultures can be negative, we defined ‘possible infection’ if patient developed fever > 100.4Of after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this fever. We also included within this definition if WBC count rose to > 15000 after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this elevation in WBC. Possible infection was not included in HAI. |
| title | Definitions |
| p | We reviewed culture data to ascertain the presence of infection. We classified infections as respiratory (tracheal aspirate, bronchoalveolar lavage), blood, urine, and other (body fluids such as pleural cavity, abdominal cavity). We used CDC definitions of healthcare associated infections(HAIs) in the acute care setting (Horan et al., 2008). We deemed that a HAI was present if cultures were positive and obtained after 3 days of hospital admission. We defined days to positive cultures as time from day of admission to the day the culture was collected.. |
| p | Since cultures can be negative, we defined ‘possible infection’ if patient developed fever > 100.4Of after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this fever. We also included within this definition if WBC count rose to > 15000 after 3rd day of admission and there were blood cultures drawn and antibiotics started within 24 hours of this elevation in WBC. Possible infection was not included in HAI. |
| sec | Outcomes Our primary outcome of interest were rates of HAIs. Secondary outcomes included in hospital mortality. |
| title | Outcomes |
| p | Our primary outcome of interest were rates of HAIs. Secondary outcomes included in hospital mortality. |
| sec | Statistics We performed all statistical analysis using STATA MC 16.0 (Stata-Corp, College Station, Tx). We describe categorical data using frequency count and percentages. We report medians and inter quartile ranges for continuous variables as they were not normally distributed. We compared demographical and clinical characteristics of persons who had HAIs with those who did not using chi square and Wilcoxon rank tests for categorical and continuous variables, respectively. For all analyses we deemed statistical significance a p-value < 0.05. We used multivariable logistic regression models to determine factors associated with the development of HAIs in COVID-19. We used single predictor logistic regression to identify significant associations between putative risk factors and development of secondary infections. Variables found significant at p < 0.10 were candidates for inclusion in our primary model. Regardless of significance we included variables known to be associated with outcomes in COVID-19 as well as variables previously identified as risk factors for developing secondary infections. Backward elimination method was used to remove other variables from the final model. Final model was bootstrapped using 2,000 bootstrap replicates and case resampling with replacement from the original dataset. We used multivariable logistic regression analysis to examine association of HAIs with in-hospital mortality. Since patients who developed HAIs were sicker and had higher inflammatory markers, we used propensity scores to adjust for these differences. Propensity score was used to identify probability that a patient would receive develop HAIs. This was calculated for every patient. Propensity score was computed using multivariable logistic regression model with HAIs as the dependent variable and incorporating multiple independent variables which included demographic and clinical characteristics along with laboratory markers. The propensity score thus obtained was used as a continuous variable to further adjust the above regression. |
| title | Statistics |
| p | We performed all statistical analysis using STATA MC 16.0 (Stata-Corp, College Station, Tx). We describe categorical data using frequency count and percentages. We report medians and inter quartile ranges for continuous variables as they were not normally distributed. |
| p | We compared demographical and clinical characteristics of persons who had HAIs with those who did not using chi square and Wilcoxon rank tests for categorical and continuous variables, respectively. For all analyses we deemed statistical significance a p-value < 0.05. |
| p | We used multivariable logistic regression models to determine factors associated with the development of HAIs in COVID-19. We used single predictor logistic regression to identify significant associations between putative risk factors and development of secondary infections. Variables found significant at p < 0.10 were candidates for inclusion in our primary model. Regardless of significance we included variables known to be associated with outcomes in COVID-19 as well as variables previously identified as risk factors for developing secondary infections. Backward elimination method was used to remove other variables from the final model. Final model was bootstrapped using 2,000 bootstrap replicates and case resampling with replacement from the original dataset. |
| p | We used multivariable logistic regression analysis to examine association of HAIs with in-hospital mortality. Since patients who developed HAIs were sicker and had higher inflammatory markers, we used propensity scores to adjust for these differences. Propensity score was used to identify probability that a patient would receive develop HAIs. This was calculated for every patient. Propensity score was computed using multivariable logistic regression model with HAIs as the dependent variable and incorporating multiple independent variables which included demographic and clinical characteristics along with laboratory markers. The propensity score thus obtained was used as a continuous variable to further adjust the above regression. |
| sec | RESULTS There were 1565 patients with COVID-19 within the study period. Of these, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of 140 HAIs, 53 were bacteremia, 67 were pneumonia and 17 were UTI. 38 patients had bacteremia from single organism and 15 patients had bacteremia from more than one organism. 73 separate organisms constituted 140 HAIs. Of these, 23(31.5%) were gram positive infections, 39(53.4%) were gram negative and 11(15%) were fungal infections. Among gram negatives, Pseudomonas (n = 10), Escherichia coli (n = 7) and Klebsiella (n = 7) and among gram positive – staphylococcus aureus(n = 13) and enterococcus (n = 5) were commonest bacterial organisms. Five out of eleven candidal infections were non albicans species. The details of organism, source, and median time to development of HAI is shown in Table 3. Fourteen patients developed clostridium difficile infections. There were 118 instances which qualified as possible infections. Of these 48 (40.7%) qualified as culture positive secondary infection. Demographics & Clinical Characteristics There was male and black race had higher rates of HAIs (Table 1 ). Rates of HAIs were higher in co-morbidities such as diabetes mellitus and ESRD (Table 1). HAIs were observed to be higher in patients receiving treatment for covid-19 such as hydroxychloroquine (27.4% vs 13.6%, p < 0.001), convalescent plasma (50.9% vs 14.1%, p < 0.001), tocilizumab (66.7% vs 11.6%, p < 0.001) and steroids (78.4% vs 40.2%, p < 0.001). Patients who received cefepime and vancomycin on admission developed higher rates of secondary infection (Table 1). Table 1 Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. No HAI HAI p Total 1514 59 Age 62(48-75) 61(52-69) 0.45 Male (%) 51.1 64.7 0.06 Race (%) 0.009 White 60.8 45.1 Blacks 9.4 17.7 Hispanics 26.0 25.5 Asians/pacific Islander 1 3.9 Others 3.0 7.8 Co-morbidities (%) Hypertension 63.2 70.6 0.28 Congestive heart failure 23.3 27.5 0.48 Diabetes Mellitus 38.6 56.8 0.009 COPD 29.7 21.6 0.3 ESRD 3.4 7.8 0.09 Cirrhosis 9.9 5.9 0.34 Cancer 11.5 9.8 0.69 Rheumatological Diseases 3.3 3.9 0.80 Home meds (%) Anticoagulation 10.4 7.8 0.55 Anti-platelets 17.4 11.7 0.30 ACE/ARB 25.8 35.3 0.13 Immunosuppressants 0.07 0.00 0.82 COVID-19 Medications (%) Vitamin C 70.1 90.2 0.002 Zinc 69.7 88.2 0.004 Hydroxychloroquine 13.6 27.4 0.005 Tocilizumab 11.6 66.7 <0.001 Steroids 40.2 78.4 <0.001 Convalescent plasma 14.1 50.9 <0.001 Remedesivir 28.2 50.9 <0.001 Therapeutic anticoagulation 73.3 96.1 <0.001 Antibiotics started at time of admission (%) Azithromycin 48.8 64.7 0.025 Doxycycline 8.1 11.7 0.34 Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 Clindamycin 1.1 1.9 0.58 Ceftriaxone 55.0 62.7 0.27 Cefepime 11.7 23.5 0.01 Piperacillin/tazobactam 11.5 13.7 0.63 Vancomycin 22.2 43.1 0.001 Sicker patients on admission with higher SOFA score and higher inflammatory markers (such as ferritin, CRP, and d-dimer) developed higher rates of secondary infection (Table 2 ). Table 2 Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. No HAI HAI p Total 1506 59 SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 Initial laboratory studies* WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 Platelets 212(164-275),1499 228(161-317),58 0.35 Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 BUN 16(11-25),1446 20(14-33),58 0.008 Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 ALT 32(22-54),1436 42(28-67),57 0.03 Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 Ferritin 373(158-805),1083 743(442-1450),57 0.001 CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 ICU admissions 422(28.0%) 58(98.3%) 0.001 SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 Required proning if on ventilator (%) 14.6 30.4 0.007 Required paralytic if on ventilator (%) 23.5 51.8 0.001 Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 Use of vasopressors Required Norepinephrine (%) 11.9 83.0 <0.001 Required vasopressin (%) 5.5 52.5 <0.001 Required epinephrine (%) 2.1 13.7 <0.001 Required angiotensin 2(%) 0.5 1.7 0.24 CVL before HAI (%) 8.1 45.8 <0.001 Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 Acute DVT/PE (%) 4.2 22.0 <0.001 * median (Inter Quartile range), Number of samples. Table 3 Hospital Associated infections: organisms and source and time to infection. Number of patients Source Median time to infection Blood Respiratory Urine others Gram positive infections MRSA 7 5 7 0 1 14 MSSA 6 5 6 1 0 13 Staphylococcus hominis 1 1 1 0 0 14 Staphylococcus epidermidis 4 4 4 0 0 15 Group D Streptococcus 5 5 4 1 0 12.5 Gram negative infections E. coli 7 5 4 4 0 19 Klebsiella 7 2 7 1 0 21 Pseudomonas 10 7 10 3 1 15.5 Serratia 3 2 3 1 0 28 Enterobacter 6 3 6 1 0 19.5 Proteus 2 2 2 1 0 21.5 Citrobacter 1 0 1 0 0 24 Acinetobacter 2 0 2 0 0 26 Stenotrophomonas 1 1 1 0 0 8 Fungal infections Candida albicans 6 6 5 2 1 13 Non albicans Candida 5 5 4 2 0 15 C. difficile 14 Patients developing secondary infections were more often on the mechanical ventilation (42.4% vs 10.0%, p < 0.001) and had central venous lines (45.8% vs 8.1%, p < 0.001). Multivariable analysis of HAIs HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). Other medications – convalescent plasma and remedesivir were not associated with increased rates of HAIs. Outcomes Patients developing secondary infection had significantly higher in hospital mortality when compared to those who did not develop secondary infection (40.7% vs 11.8% p < 0.001) (Table 4 ). However, on multivariable analysis, secondary infections were not associated with increased risk of death (OR 0.85, 95%CI 0.40-1.80, p = 0.67) (Appendix 1). Table 4 Outcomes of patients with Hospital Associated Infections No HAI HAI p Total Died (%) 11.8 40.7 <0.001 LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 Disposition (%) <0.001 Home 70.8 26.5 Home with health 12.9 17.6 Rehab/SNF/LTAC/Acute care 13.6 35.3 Others 2.6 20.6 The length of hospital stay was significantly longer in patients developing secondary infections. Disposition in patients with secondary infection was significantly higher to skilled nursing facility and long-term acute care (SNF/LTAC) when compared to those who did not have secondary infection (35.3% vs 13.6%, p < 0.001). |
| title | RESULTS |
| p | There were 1565 patients with COVID-19 within the study period. Of these, 140 separate HAIs from 73 different organisms developed in 59 (3.7%) patients. Of 140 HAIs, 53 were bacteremia, 67 were pneumonia and 17 were UTI. 38 patients had bacteremia from single organism and 15 patients had bacteremia from more than one organism. |
| p | 73 separate organisms constituted 140 HAIs. Of these, 23(31.5%) were gram positive infections, 39(53.4%) were gram negative and 11(15%) were fungal infections. Among gram negatives, Pseudomonas (n = 10), Escherichia coli (n = 7) and Klebsiella (n = 7) and among gram positive – staphylococcus aureus(n = 13) and enterococcus (n = 5) were commonest bacterial organisms. Five out of eleven candidal infections were non albicans species. |
| p | The details of organism, source, and median time to development of HAI is shown in Table 3. Fourteen patients developed clostridium difficile infections. |
| p | There were 118 instances which qualified as possible infections. Of these 48 (40.7%) qualified as culture positive secondary infection. |
| sec | Demographics & Clinical Characteristics There was male and black race had higher rates of HAIs (Table 1 ). Rates of HAIs were higher in co-morbidities such as diabetes mellitus and ESRD (Table 1). HAIs were observed to be higher in patients receiving treatment for covid-19 such as hydroxychloroquine (27.4% vs 13.6%, p < 0.001), convalescent plasma (50.9% vs 14.1%, p < 0.001), tocilizumab (66.7% vs 11.6%, p < 0.001) and steroids (78.4% vs 40.2%, p < 0.001). Patients who received cefepime and vancomycin on admission developed higher rates of secondary infection (Table 1). Table 1 Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. No HAI HAI p Total 1514 59 Age 62(48-75) 61(52-69) 0.45 Male (%) 51.1 64.7 0.06 Race (%) 0.009 White 60.8 45.1 Blacks 9.4 17.7 Hispanics 26.0 25.5 Asians/pacific Islander 1 3.9 Others 3.0 7.8 Co-morbidities (%) Hypertension 63.2 70.6 0.28 Congestive heart failure 23.3 27.5 0.48 Diabetes Mellitus 38.6 56.8 0.009 COPD 29.7 21.6 0.3 ESRD 3.4 7.8 0.09 Cirrhosis 9.9 5.9 0.34 Cancer 11.5 9.8 0.69 Rheumatological Diseases 3.3 3.9 0.80 Home meds (%) Anticoagulation 10.4 7.8 0.55 Anti-platelets 17.4 11.7 0.30 ACE/ARB 25.8 35.3 0.13 Immunosuppressants 0.07 0.00 0.82 COVID-19 Medications (%) Vitamin C 70.1 90.2 0.002 Zinc 69.7 88.2 0.004 Hydroxychloroquine 13.6 27.4 0.005 Tocilizumab 11.6 66.7 <0.001 Steroids 40.2 78.4 <0.001 Convalescent plasma 14.1 50.9 <0.001 Remedesivir 28.2 50.9 <0.001 Therapeutic anticoagulation 73.3 96.1 <0.001 Antibiotics started at time of admission (%) Azithromycin 48.8 64.7 0.025 Doxycycline 8.1 11.7 0.34 Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 Clindamycin 1.1 1.9 0.58 Ceftriaxone 55.0 62.7 0.27 Cefepime 11.7 23.5 0.01 Piperacillin/tazobactam 11.5 13.7 0.63 Vancomycin 22.2 43.1 0.001 Sicker patients on admission with higher SOFA score and higher inflammatory markers (such as ferritin, CRP, and d-dimer) developed higher rates of secondary infection (Table 2 ). Table 2 Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. No HAI HAI p Total 1506 59 SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 Initial laboratory studies* WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 Platelets 212(164-275),1499 228(161-317),58 0.35 Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 BUN 16(11-25),1446 20(14-33),58 0.008 Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 ALT 32(22-54),1436 42(28-67),57 0.03 Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 Ferritin 373(158-805),1083 743(442-1450),57 0.001 CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 ICU admissions 422(28.0%) 58(98.3%) 0.001 SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 Required proning if on ventilator (%) 14.6 30.4 0.007 Required paralytic if on ventilator (%) 23.5 51.8 0.001 Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 Use of vasopressors Required Norepinephrine (%) 11.9 83.0 <0.001 Required vasopressin (%) 5.5 52.5 <0.001 Required epinephrine (%) 2.1 13.7 <0.001 Required angiotensin 2(%) 0.5 1.7 0.24 CVL before HAI (%) 8.1 45.8 <0.001 Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 Acute DVT/PE (%) 4.2 22.0 <0.001 * median (Inter Quartile range), Number of samples. Table 3 Hospital Associated infections: organisms and source and time to infection. Number of patients Source Median time to infection Blood Respiratory Urine others Gram positive infections MRSA 7 5 7 0 1 14 MSSA 6 5 6 1 0 13 Staphylococcus hominis 1 1 1 0 0 14 Staphylococcus epidermidis 4 4 4 0 0 15 Group D Streptococcus 5 5 4 1 0 12.5 Gram negative infections E. coli 7 5 4 4 0 19 Klebsiella 7 2 7 1 0 21 Pseudomonas 10 7 10 3 1 15.5 Serratia 3 2 3 1 0 28 Enterobacter 6 3 6 1 0 19.5 Proteus 2 2 2 1 0 21.5 Citrobacter 1 0 1 0 0 24 Acinetobacter 2 0 2 0 0 26 Stenotrophomonas 1 1 1 0 0 8 Fungal infections Candida albicans 6 6 5 2 1 13 Non albicans Candida 5 5 4 2 0 15 C. difficile 14 Patients developing secondary infections were more often on the mechanical ventilation (42.4% vs 10.0%, p < 0.001) and had central venous lines (45.8% vs 8.1%, p < 0.001). |
| title | Demographics & Clinical Characteristics |
| p | There was male and black race had higher rates of HAIs (Table 1 ). Rates of HAIs were higher in co-morbidities such as diabetes mellitus and ESRD (Table 1). HAIs were observed to be higher in patients receiving treatment for covid-19 such as hydroxychloroquine (27.4% vs 13.6%, p < 0.001), convalescent plasma (50.9% vs 14.1%, p < 0.001), tocilizumab (66.7% vs 11.6%, p < 0.001) and steroids (78.4% vs 40.2%, p < 0.001). Patients who received cefepime and vancomycin on admission developed higher rates of secondary infection (Table 1). Table 1 Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. No HAI HAI p Total 1514 59 Age 62(48-75) 61(52-69) 0.45 Male (%) 51.1 64.7 0.06 Race (%) 0.009 White 60.8 45.1 Blacks 9.4 17.7 Hispanics 26.0 25.5 Asians/pacific Islander 1 3.9 Others 3.0 7.8 Co-morbidities (%) Hypertension 63.2 70.6 0.28 Congestive heart failure 23.3 27.5 0.48 Diabetes Mellitus 38.6 56.8 0.009 COPD 29.7 21.6 0.3 ESRD 3.4 7.8 0.09 Cirrhosis 9.9 5.9 0.34 Cancer 11.5 9.8 0.69 Rheumatological Diseases 3.3 3.9 0.80 Home meds (%) Anticoagulation 10.4 7.8 0.55 Anti-platelets 17.4 11.7 0.30 ACE/ARB 25.8 35.3 0.13 Immunosuppressants 0.07 0.00 0.82 COVID-19 Medications (%) Vitamin C 70.1 90.2 0.002 Zinc 69.7 88.2 0.004 Hydroxychloroquine 13.6 27.4 0.005 Tocilizumab 11.6 66.7 <0.001 Steroids 40.2 78.4 <0.001 Convalescent plasma 14.1 50.9 <0.001 Remedesivir 28.2 50.9 <0.001 Therapeutic anticoagulation 73.3 96.1 <0.001 Antibiotics started at time of admission (%) Azithromycin 48.8 64.7 0.025 Doxycycline 8.1 11.7 0.34 Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 Clindamycin 1.1 1.9 0.58 Ceftriaxone 55.0 62.7 0.27 Cefepime 11.7 23.5 0.01 Piperacillin/tazobactam 11.5 13.7 0.63 Vancomycin 22.2 43.1 0.001 |
| table-wrap | Table 1 Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. No HAI HAI p Total 1514 59 Age 62(48-75) 61(52-69) 0.45 Male (%) 51.1 64.7 0.06 Race (%) 0.009 White 60.8 45.1 Blacks 9.4 17.7 Hispanics 26.0 25.5 Asians/pacific Islander 1 3.9 Others 3.0 7.8 Co-morbidities (%) Hypertension 63.2 70.6 0.28 Congestive heart failure 23.3 27.5 0.48 Diabetes Mellitus 38.6 56.8 0.009 COPD 29.7 21.6 0.3 ESRD 3.4 7.8 0.09 Cirrhosis 9.9 5.9 0.34 Cancer 11.5 9.8 0.69 Rheumatological Diseases 3.3 3.9 0.80 Home meds (%) Anticoagulation 10.4 7.8 0.55 Anti-platelets 17.4 11.7 0.30 ACE/ARB 25.8 35.3 0.13 Immunosuppressants 0.07 0.00 0.82 COVID-19 Medications (%) Vitamin C 70.1 90.2 0.002 Zinc 69.7 88.2 0.004 Hydroxychloroquine 13.6 27.4 0.005 Tocilizumab 11.6 66.7 <0.001 Steroids 40.2 78.4 <0.001 Convalescent plasma 14.1 50.9 <0.001 Remedesivir 28.2 50.9 <0.001 Therapeutic anticoagulation 73.3 96.1 <0.001 Antibiotics started at time of admission (%) Azithromycin 48.8 64.7 0.025 Doxycycline 8.1 11.7 0.34 Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 Clindamycin 1.1 1.9 0.58 Ceftriaxone 55.0 62.7 0.27 Cefepime 11.7 23.5 0.01 Piperacillin/tazobactam 11.5 13.7 0.63 Vancomycin 22.2 43.1 0.001 |
| label | Table 1 |
| caption | Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. |
| p | Demographical and clinical characteristics of COVID-19 patients – With and without Hospital Associated Infection. |
| table | No HAI HAI p Total 1514 59 Age 62(48-75) 61(52-69) 0.45 Male (%) 51.1 64.7 0.06 Race (%) 0.009 White 60.8 45.1 Blacks 9.4 17.7 Hispanics 26.0 25.5 Asians/pacific Islander 1 3.9 Others 3.0 7.8 Co-morbidities (%) Hypertension 63.2 70.6 0.28 Congestive heart failure 23.3 27.5 0.48 Diabetes Mellitus 38.6 56.8 0.009 COPD 29.7 21.6 0.3 ESRD 3.4 7.8 0.09 Cirrhosis 9.9 5.9 0.34 Cancer 11.5 9.8 0.69 Rheumatological Diseases 3.3 3.9 0.80 Home meds (%) Anticoagulation 10.4 7.8 0.55 Anti-platelets 17.4 11.7 0.30 ACE/ARB 25.8 35.3 0.13 Immunosuppressants 0.07 0.00 0.82 COVID-19 Medications (%) Vitamin C 70.1 90.2 0.002 Zinc 69.7 88.2 0.004 Hydroxychloroquine 13.6 27.4 0.005 Tocilizumab 11.6 66.7 <0.001 Steroids 40.2 78.4 <0.001 Convalescent plasma 14.1 50.9 <0.001 Remedesivir 28.2 50.9 <0.001 Therapeutic anticoagulation 73.3 96.1 <0.001 Antibiotics started at time of admission (%) Azithromycin 48.8 64.7 0.025 Doxycycline 8.1 11.7 0.34 Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 Clindamycin 1.1 1.9 0.58 Ceftriaxone 55.0 62.7 0.27 Cefepime 11.7 23.5 0.01 Piperacillin/tazobactam 11.5 13.7 0.63 Vancomycin 22.2 43.1 0.001 |
| tr | No HAI HAI p |
| th | No HAI |
| th | HAI |
| th | p |
| tr | Total 1514 59 |
| td | Total |
| td | 1514 |
| td | 59 |
| tr | Age 62(48-75) 61(52-69) 0.45 |
| td | Age |
| td | 62(48-75) |
| td | 61(52-69) |
| td | 0.45 |
| tr | Male (%) 51.1 64.7 0.06 |
| td | Male (%) |
| td | 51.1 |
| td | 64.7 |
| td | 0.06 |
| tr | Race (%) 0.009 |
| td | Race (%) |
| td | 0.009 |
| tr | White 60.8 45.1 |
| td | White |
| td | 60.8 |
| td | 45.1 |
| tr | Blacks 9.4 17.7 |
| td | Blacks |
| td | 9.4 |
| td | 17.7 |
| tr | Hispanics 26.0 25.5 |
| td | Hispanics |
| td | 26.0 |
| td | 25.5 |
| tr | Asians/pacific Islander 1 3.9 |
| td | Asians/pacific Islander |
| td | 1 |
| td | 3.9 |
| tr | Others 3.0 7.8 |
| td | Others |
| td | 3.0 |
| td | 7.8 |
| tr | Co-morbidities (%) |
| td | Co-morbidities (%) |
| tr | Hypertension 63.2 70.6 0.28 |
| td | Hypertension |
| td | 63.2 |
| td | 70.6 |
| td | 0.28 |
| tr | Congestive heart failure 23.3 27.5 0.48 |
| td | Congestive heart failure |
| td | 23.3 |
| td | 27.5 |
| td | 0.48 |
| tr | Diabetes Mellitus 38.6 56.8 0.009 |
| td | Diabetes Mellitus |
| td | 38.6 |
| td | 56.8 |
| td | 0.009 |
| tr | COPD 29.7 21.6 0.3 |
| td | COPD |
| td | 29.7 |
| td | 21.6 |
| td | 0.3 |
| tr | ESRD 3.4 7.8 0.09 |
| td | ESRD |
| td | 3.4 |
| td | 7.8 |
| td | 0.09 |
| tr | Cirrhosis 9.9 5.9 0.34 |
| td | Cirrhosis |
| td | 9.9 |
| td | 5.9 |
| td | 0.34 |
| tr | Cancer 11.5 9.8 0.69 |
| td | Cancer |
| td | 11.5 |
| td | 9.8 |
| td | 0.69 |
| tr | Rheumatological Diseases 3.3 3.9 0.80 |
| td | Rheumatological Diseases |
| td | 3.3 |
| td | 3.9 |
| td | 0.80 |
| tr | |
| td | |
| tr | Home meds (%) |
| td | Home meds (%) |
| tr | Anticoagulation 10.4 7.8 0.55 |
| td | Anticoagulation |
| td | 10.4 |
| td | 7.8 |
| td | 0.55 |
| tr | Anti-platelets 17.4 11.7 0.30 |
| td | Anti-platelets |
| td | 17.4 |
| td | 11.7 |
| td | 0.30 |
| tr | ACE/ARB 25.8 35.3 0.13 |
| td | ACE/ARB |
| td | 25.8 |
| td | 35.3 |
| td | 0.13 |
| tr | Immunosuppressants 0.07 0.00 0.82 |
| td | Immunosuppressants |
| td | 0.07 |
| td | 0.00 |
| td | 0.82 |
| tr | |
| td | |
| tr | COVID-19 Medications (%) |
| td | COVID-19 Medications (%) |
| tr | Vitamin C 70.1 90.2 0.002 |
| td | Vitamin C |
| td | 70.1 |
| td | 90.2 |
| td | 0.002 |
| tr | Zinc 69.7 88.2 0.004 |
| td | Zinc |
| td | 69.7 |
| td | 88.2 |
| td | 0.004 |
| tr | Hydroxychloroquine 13.6 27.4 0.005 |
| td | Hydroxychloroquine |
| td | 13.6 |
| td | 27.4 |
| td | 0.005 |
| tr | Tocilizumab 11.6 66.7 <0.001 |
| td | Tocilizumab |
| td | 11.6 |
| td | 66.7 |
| td | <0.001 |
| tr | Steroids 40.2 78.4 <0.001 |
| td | Steroids |
| td | 40.2 |
| td | 78.4 |
| td | <0.001 |
| tr | Convalescent plasma 14.1 50.9 <0.001 |
| td | Convalescent plasma |
| td | 14.1 |
| td | 50.9 |
| td | <0.001 |
| tr | Remedesivir 28.2 50.9 <0.001 |
| td | Remedesivir |
| td | 28.2 |
| td | 50.9 |
| td | <0.001 |
| tr | Therapeutic anticoagulation 73.3 96.1 <0.001 |
| td | Therapeutic anticoagulation |
| td | 73.3 |
| td | 96.1 |
| td | <0.001 |
| tr | |
| td | |
| tr | Antibiotics started at time of admission (%) |
| td | Antibiotics started at time of admission (%) |
| tr | Azithromycin 48.8 64.7 0.025 |
| td | Azithromycin |
| td | 48.8 |
| td | 64.7 |
| td | 0.025 |
| tr | Doxycycline 8.1 11.7 0.34 |
| td | Doxycycline |
| td | 8.1 |
| td | 11.7 |
| td | 0.34 |
| tr | Levofloxacin/Ciprofloxacin 6.1 3.9 0.52 |
| td | Levofloxacin/Ciprofloxacin |
| td | 6.1 |
| td | 3.9 |
| td | 0.52 |
| tr | Clindamycin 1.1 1.9 0.58 |
| td | Clindamycin |
| td | 1.1 |
| td | 1.9 |
| td | 0.58 |
| tr | Ceftriaxone 55.0 62.7 0.27 |
| td | Ceftriaxone |
| td | 55.0 |
| td | 62.7 |
| td | 0.27 |
| tr | Cefepime 11.7 23.5 0.01 |
| td | Cefepime |
| td | 11.7 |
| td | 23.5 |
| td | 0.01 |
| tr | Piperacillin/tazobactam 11.5 13.7 0.63 |
| td | Piperacillin/tazobactam |
| td | 11.5 |
| td | 13.7 |
| td | 0.63 |
| tr | Vancomycin 22.2 43.1 0.001 |
| td | Vancomycin |
| td | 22.2 |
| td | 43.1 |
| td | 0.001 |
| p | Sicker patients on admission with higher SOFA score and higher inflammatory markers (such as ferritin, CRP, and d-dimer) developed higher rates of secondary infection (Table 2 ). Table 2 Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. No HAI HAI p Total 1506 59 SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 Initial laboratory studies* WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 Platelets 212(164-275),1499 228(161-317),58 0.35 Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 BUN 16(11-25),1446 20(14-33),58 0.008 Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 ALT 32(22-54),1436 42(28-67),57 0.03 Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 Ferritin 373(158-805),1083 743(442-1450),57 0.001 CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 ICU admissions 422(28.0%) 58(98.3%) 0.001 SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 Required proning if on ventilator (%) 14.6 30.4 0.007 Required paralytic if on ventilator (%) 23.5 51.8 0.001 Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 Use of vasopressors Required Norepinephrine (%) 11.9 83.0 <0.001 Required vasopressin (%) 5.5 52.5 <0.001 Required epinephrine (%) 2.1 13.7 <0.001 Required angiotensin 2(%) 0.5 1.7 0.24 CVL before HAI (%) 8.1 45.8 <0.001 Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 Acute DVT/PE (%) 4.2 22.0 <0.001 * median (Inter Quartile range), Number of samples. Table 3 Hospital Associated infections: organisms and source and time to infection. Number of patients Source Median time to infection Blood Respiratory Urine others Gram positive infections MRSA 7 5 7 0 1 14 MSSA 6 5 6 1 0 13 Staphylococcus hominis 1 1 1 0 0 14 Staphylococcus epidermidis 4 4 4 0 0 15 Group D Streptococcus 5 5 4 1 0 12.5 Gram negative infections E. coli 7 5 4 4 0 19 Klebsiella 7 2 7 1 0 21 Pseudomonas 10 7 10 3 1 15.5 Serratia 3 2 3 1 0 28 Enterobacter 6 3 6 1 0 19.5 Proteus 2 2 2 1 0 21.5 Citrobacter 1 0 1 0 0 24 Acinetobacter 2 0 2 0 0 26 Stenotrophomonas 1 1 1 0 0 8 Fungal infections Candida albicans 6 6 5 2 1 13 Non albicans Candida 5 5 4 2 0 15 C. difficile 14 |
| table-wrap | Table 2 Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. No HAI HAI p Total 1506 59 SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 Initial laboratory studies* WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 Platelets 212(164-275),1499 228(161-317),58 0.35 Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 BUN 16(11-25),1446 20(14-33),58 0.008 Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 ALT 32(22-54),1436 42(28-67),57 0.03 Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 Ferritin 373(158-805),1083 743(442-1450),57 0.001 CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 ICU admissions 422(28.0%) 58(98.3%) 0.001 SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 Required proning if on ventilator (%) 14.6 30.4 0.007 Required paralytic if on ventilator (%) 23.5 51.8 0.001 Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 Use of vasopressors Required Norepinephrine (%) 11.9 83.0 <0.001 Required vasopressin (%) 5.5 52.5 <0.001 Required epinephrine (%) 2.1 13.7 <0.001 Required angiotensin 2(%) 0.5 1.7 0.24 CVL before HAI (%) 8.1 45.8 <0.001 Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 Acute DVT/PE (%) 4.2 22.0 <0.001 * median (Inter Quartile range), Number of samples. |
| label | Table 2 |
| caption | Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. |
| p | Clinical features and inflammatory markers in covid-19 patients – comparison of patients with Hospital Associated Infection and those without. |
| table | No HAI HAI p Total 1506 59 SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 Initial laboratory studies* WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 Platelets 212(164-275),1499 228(161-317),58 0.35 Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 BUN 16(11-25),1446 20(14-33),58 0.008 Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 ALT 32(22-54),1436 42(28-67),57 0.03 Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 Ferritin 373(158-805),1083 743(442-1450),57 0.001 CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 ICU admissions 422(28.0%) 58(98.3%) 0.001 SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 Required proning if on ventilator (%) 14.6 30.4 0.007 Required paralytic if on ventilator (%) 23.5 51.8 0.001 Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 Use of vasopressors Required Norepinephrine (%) 11.9 83.0 <0.001 Required vasopressin (%) 5.5 52.5 <0.001 Required epinephrine (%) 2.1 13.7 <0.001 Required angiotensin 2(%) 0.5 1.7 0.24 CVL before HAI (%) 8.1 45.8 <0.001 Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 Acute DVT/PE (%) 4.2 22.0 <0.001 |
| tr | No HAI HAI p |
| th | No HAI |
| th | HAI |
| th | p |
| tr | Total 1506 59 |
| td | Total |
| td | 1506 |
| td | 59 |
| tr | SOFA score on admission 0(0-1),1506 1(0-2),59 0.001 |
| td | SOFA score on admission |
| td | 0(0-1),1506 |
| td | 1(0-2),59 |
| td | 0.001 |
| tr | Initial laboratory studies* |
| td | Initial laboratory studies* |
| tr | WBC 8.1(5.9-11.4),1501 10.3(7.3-14.2),58 0.004 |
| td | WBC |
| td | 8.1(5.9-11.4),1501 |
| td | 10.3(7.3-14.2),58 |
| td | 0.004 |
| tr | Lymphocyte count 1.06(0.73-1.51),1431 0.85(0.63-1.26),59 0.048 |
| td | Lymphocyte count |
| td | 1.06(0.73-1.51),1431 |
| td | 0.85(0.63-1.26),59 |
| td | 0.048 |
| tr | Hemoglobin 13(11.5-14.4),1501 13.8(12.7-15),58 0.012 |
| td | Hemoglobin |
| td | 13(11.5-14.4),1501 |
| td | 13.8(12.7-15),58 |
| td | 0.012 |
| tr | Platelets 212(164-275),1499 228(161-317),58 0.35 |
| td | Platelets |
| td | 212(164-275),1499 |
| td | 228(161-317),58 |
| td | 0.35 |
| tr | Troponin 0.02(0.02-0.02),1234 0.02(0.02-0.05),54 0.21 |
| td | Troponin |
| td | 0.02(0.02-0.02),1234 |
| td | 0.02(0.02-0.05),54 |
| td | 0.21 |
| tr | Lactate 1.1(0.8-1.5),846 1.5(1.1-2.1),56 0.001 |
| td | Lactate |
| td | 1.1(0.8-1.5),846 |
| td | 1.5(1.1-2.1),56 |
| td | 0.001 |
| tr | BUN 16(11-25),1446 20(14-33),58 0.008 |
| td | BUN |
| td | 16(11-25),1446 |
| td | 20(14-33),58 |
| td | 0.008 |
| tr | Creatinine 1.02(0.82-1.35),1447 1.24(0.96-1.59),58 0.001 |
| td | Creatinine |
| td | 1.02(0.82-1.35),1447 |
| td | 1.24(0.96-1.59),58 |
| td | 0.001 |
| tr | ALT 32(22-54),1436 42(28-67),57 0.03 |
| td | ALT |
| td | 32(22-54),1436 |
| td | 42(28-67),57 |
| td | 0.03 |
| tr | Bilirubin 0.5(0.4-0.7),1405 0.6(0.4-0.9),57 0.046 |
| td | Bilirubin |
| td | 0.5(0.4-0.7),1405 |
| td | 0.6(0.4-0.9),57 |
| td | 0.046 |
| tr | INR 1.15(1.07-1.27),1275 1.15(1.09-1.35),59 0.38 |
| td | INR |
| td | 1.15(1.07-1.27),1275 |
| td | 1.15(1.09-1.35),59 |
| td | 0.38 |
| tr | aPTT 29.7(27.1-32.8),897 28.3(26.2-31.4),52 0.24 |
| td | aPTT |
| td | 29.7(27.1-32.8),897 |
| td | 28.3(26.2-31.4),52 |
| td | 0.24 |
| tr | Ferritin 373(158-805),1083 743(442-1450),57 0.001 |
| td | Ferritin |
| td | 373(158-805),1083 |
| td | 743(442-1450),57 |
| td | 0.001 |
| tr | CRP 7.4(2.9-12.8),1101 12(8.9-21.2),58 0.001 |
| td | CRP |
| td | 7.4(2.9-12.8),1101 |
| td | 12(8.9-21.2),58 |
| td | 0.001 |
| tr | D-dimer 0.79(0.48-1.45),1066 0.92(0.60-2.02),56 0.001 |
| td | D-dimer |
| td | 0.79(0.48-1.45),1066 |
| td | 0.92(0.60-2.02),56 |
| td | 0.001 |
| tr | |
| td | |
| tr | ICU admissions 422(28.0%) 58(98.3%) 0.001 |
| td | ICU admissions |
| td | 422(28.0%) |
| td | 58(98.3%) |
| td | 0.001 |
| tr | SOFA score on ICU admission* 1(0-3),400 1(0-3),52 0.75 |
| td | SOFA score on ICU admission* |
| td | 1(0-3),400 |
| td | 1(0-3),52 |
| td | 0.75 |
| tr | |
| td | |
| tr | Use of mechanical ventilation (%) 191(12.7%) 56(94.9%) <0.001 |
| td | Use of mechanical ventilation (%) |
| td | 191(12.7%) |
| td | 56(94.9%) |
| td | <0.001 |
| tr | Mechanical ventilation before HAI 150(10.0%) 25 (42.4%) <0.001 |
| td | Mechanical ventilation before HAI |
| td | 150(10.0%) |
| td | 25 (42.4%) |
| td | <0.001 |
| tr | Length of mechanical ventilation (days)* 6(1-12),188 18(12-29),55 <0.001 |
| td | Length of mechanical ventilation (days)* |
| td | 6(1-12),188 |
| td | 18(12-29),55 |
| td | <0.001 |
| tr | Required proning if on ventilator (%) 14.6 30.4 0.007 |
| td | Required proning if on ventilator (%) |
| td | 14.6 |
| td | 30.4 |
| td | 0.007 |
| tr | Required paralytic if on ventilator (%) 23.5 51.8 0.001 |
| td | Required paralytic if on ventilator (%) |
| td | 23.5 |
| td | 51.8 |
| td | 0.001 |
| tr | Required inhaled vasodilators if on ventilator (%) 6.3 17.8 0.007 |
| td | Required inhaled vasodilators if on ventilator (%) |
| td | 6.3 |
| td | 17.8 |
| td | 0.007 |
| tr | Required tracheostomy if on ventilator (%) 6.8 25.0 0.001 |
| td | Required tracheostomy if on ventilator (%) |
| td | 6.8 |
| td | 25.0 |
| td | 0.001 |
| tr | Use of vasopressors |
| td | Use of vasopressors |
| tr | Required Norepinephrine (%) 11.9 83.0 <0.001 |
| td | Required Norepinephrine (%) |
| td | 11.9 |
| td | 83.0 |
| td | <0.001 |
| tr | Required vasopressin (%) 5.5 52.5 <0.001 |
| td | Required vasopressin (%) |
| td | 5.5 |
| td | 52.5 |
| td | <0.001 |
| tr | Required epinephrine (%) 2.1 13.7 <0.001 |
| td | Required epinephrine (%) |
| td | 2.1 |
| td | 13.7 |
| td | <0.001 |
| tr | Required angiotensin 2(%) 0.5 1.7 0.24 |
| td | Required angiotensin 2(%) |
| td | 0.5 |
| td | 1.7 |
| td | 0.24 |
| tr | CVL before HAI (%) 8.1 45.8 <0.001 |
| td | CVL before HAI (%) |
| td | 8.1 |
| td | 45.8 |
| td | <0.001 |
| tr | Acute renal failure requiring hemodialysis (%) 1.5 22.0 <0.001 |
| td | Acute renal failure requiring hemodialysis (%) |
| td | 1.5 |
| td | 22.0 |
| td | <0.001 |
| tr | Acute DVT/PE (%) 4.2 22.0 <0.001 |
| td | Acute DVT/PE (%) |
| td | 4.2 |
| td | 22.0 |
| td | <0.001 |
| table-wrap-foot | * median (Inter Quartile range), Number of samples. |
| footnote | * median (Inter Quartile range), Number of samples. |
| label | * |
| p | median (Inter Quartile range), Number of samples. |
| table-wrap | Table 3 Hospital Associated infections: organisms and source and time to infection. Number of patients Source Median time to infection Blood Respiratory Urine others Gram positive infections MRSA 7 5 7 0 1 14 MSSA 6 5 6 1 0 13 Staphylococcus hominis 1 1 1 0 0 14 Staphylococcus epidermidis 4 4 4 0 0 15 Group D Streptococcus 5 5 4 1 0 12.5 Gram negative infections E. coli 7 5 4 4 0 19 Klebsiella 7 2 7 1 0 21 Pseudomonas 10 7 10 3 1 15.5 Serratia 3 2 3 1 0 28 Enterobacter 6 3 6 1 0 19.5 Proteus 2 2 2 1 0 21.5 Citrobacter 1 0 1 0 0 24 Acinetobacter 2 0 2 0 0 26 Stenotrophomonas 1 1 1 0 0 8 Fungal infections Candida albicans 6 6 5 2 1 13 Non albicans Candida 5 5 4 2 0 15 C. difficile 14 |
| label | Table 3 |
| caption | Hospital Associated infections: organisms and source and time to infection. |
| p | Hospital Associated infections: organisms and source and time to infection. |
| table | Number of patients Source Median time to infection Blood Respiratory Urine others Gram positive infections MRSA 7 5 7 0 1 14 MSSA 6 5 6 1 0 13 Staphylococcus hominis 1 1 1 0 0 14 Staphylococcus epidermidis 4 4 4 0 0 15 Group D Streptococcus 5 5 4 1 0 12.5 Gram negative infections E. coli 7 5 4 4 0 19 Klebsiella 7 2 7 1 0 21 Pseudomonas 10 7 10 3 1 15.5 Serratia 3 2 3 1 0 28 Enterobacter 6 3 6 1 0 19.5 Proteus 2 2 2 1 0 21.5 Citrobacter 1 0 1 0 0 24 Acinetobacter 2 0 2 0 0 26 Stenotrophomonas 1 1 1 0 0 8 Fungal infections Candida albicans 6 6 5 2 1 13 Non albicans Candida 5 5 4 2 0 15 C. difficile 14 |
| tr | Number of patients Source Median time to infection |
| th | Number of patients |
| th | Source |
| th | Median time to infection |
| tr | Blood Respiratory Urine others |
| th | Blood |
| th | Respiratory |
| th | Urine |
| th | others |
| tr | Gram positive infections |
| td | Gram positive infections |
| tr | MRSA 7 5 7 0 1 14 |
| td | MRSA |
| td | 7 |
| td | 5 |
| td | 7 |
| td | 0 |
| td | 1 |
| td | 14 |
| tr | MSSA 6 5 6 1 0 13 |
| td | MSSA |
| td | 6 |
| td | 5 |
| td | 6 |
| td | 1 |
| td | 0 |
| td | 13 |
| tr | Staphylococcus hominis 1 1 1 0 0 14 |
| td | Staphylococcus hominis |
| td | 1 |
| td | 1 |
| td | 1 |
| td | 0 |
| td | 0 |
| td | 14 |
| tr | Staphylococcus epidermidis 4 4 4 0 0 15 |
| td | Staphylococcus epidermidis |
| td | 4 |
| td | 4 |
| td | 4 |
| td | 0 |
| td | 0 |
| td | 15 |
| tr | Group D Streptococcus 5 5 4 1 0 12.5 |
| td | Group D Streptococcus |
| td | 5 |
| td | 5 |
| td | 4 |
| td | 1 |
| td | 0 |
| td | 12.5 |
| tr | Gram negative infections |
| td | Gram negative infections |
| tr | E. coli 7 5 4 4 0 19 |
| td | E. coli |
| td | 7 |
| td | 5 |
| td | 4 |
| td | 4 |
| td | 0 |
| td | 19 |
| tr | Klebsiella 7 2 7 1 0 21 |
| td | Klebsiella |
| td | 7 |
| td | 2 |
| td | 7 |
| td | 1 |
| td | 0 |
| td | 21 |
| tr | Pseudomonas 10 7 10 3 1 15.5 |
| td | Pseudomonas |
| td | 10 |
| td | 7 |
| td | 10 |
| td | 3 |
| td | 1 |
| td | 15.5 |
| tr | Serratia 3 2 3 1 0 28 |
| td | Serratia |
| td | 3 |
| td | 2 |
| td | 3 |
| td | 1 |
| td | 0 |
| td | 28 |
| tr | Enterobacter 6 3 6 1 0 19.5 |
| td | Enterobacter |
| td | 6 |
| td | 3 |
| td | 6 |
| td | 1 |
| td | 0 |
| td | 19.5 |
| tr | Proteus 2 2 2 1 0 21.5 |
| td | Proteus |
| td | 2 |
| td | 2 |
| td | 2 |
| td | 1 |
| td | 0 |
| td | 21.5 |
| tr | Citrobacter 1 0 1 0 0 24 |
| td | Citrobacter |
| td | 1 |
| td | 0 |
| td | 1 |
| td | 0 |
| td | 0 |
| td | 24 |
| tr | Acinetobacter 2 0 2 0 0 26 |
| td | Acinetobacter |
| td | 2 |
| td | 0 |
| td | 2 |
| td | 0 |
| td | 0 |
| td | 26 |
| tr | Stenotrophomonas 1 1 1 0 0 8 |
| td | Stenotrophomonas |
| td | 1 |
| td | 1 |
| td | 1 |
| td | 0 |
| td | 0 |
| td | 8 |
| tr | Fungal infections |
| td | Fungal infections |
| tr | Candida albicans 6 6 5 2 1 13 |
| td | Candida albicans |
| td | 6 |
| td | 6 |
| td | 5 |
| td | 2 |
| td | 1 |
| td | 13 |
| tr | Non albicans Candida 5 5 4 2 0 15 |
| td | Non albicans Candida |
| td | 5 |
| td | 5 |
| td | 4 |
| td | 2 |
| td | 0 |
| td | 15 |
| tr | C. difficile 14 |
| td | C. difficile |
| td | 14 |
| p | Patients developing secondary infections were more often on the mechanical ventilation (42.4% vs 10.0%, p < 0.001) and had central venous lines (45.8% vs 8.1%, p < 0.001). |
| sec | Multivariable analysis of HAIs HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). Other medications – convalescent plasma and remedesivir were not associated with increased rates of HAIs. |
| title | Multivariable analysis of HAIs |
| p | HAIs were associated with use of tocilizumab (OR 5.04, 95%CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95%CI 1.4-10, p = 0.007), hydroxychloroquine(OR 3.0, 95%CI 1.0-8.8, p = 0.05) and acute kidney injury requiring hemodialysis (OR 3.7, 95%CI 1.1-12.8, p = 0.04). Other medications – convalescent plasma and remedesivir were not associated with increased rates of HAIs. |
| sec | Outcomes Patients developing secondary infection had significantly higher in hospital mortality when compared to those who did not develop secondary infection (40.7% vs 11.8% p < 0.001) (Table 4 ). However, on multivariable analysis, secondary infections were not associated with increased risk of death (OR 0.85, 95%CI 0.40-1.80, p = 0.67) (Appendix 1). Table 4 Outcomes of patients with Hospital Associated Infections No HAI HAI p Total Died (%) 11.8 40.7 <0.001 LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 Disposition (%) <0.001 Home 70.8 26.5 Home with health 12.9 17.6 Rehab/SNF/LTAC/Acute care 13.6 35.3 Others 2.6 20.6 The length of hospital stay was significantly longer in patients developing secondary infections. Disposition in patients with secondary infection was significantly higher to skilled nursing facility and long-term acute care (SNF/LTAC) when compared to those who did not have secondary infection (35.3% vs 13.6%, p < 0.001). |
| title | Outcomes |
| p | Patients developing secondary infection had significantly higher in hospital mortality when compared to those who did not develop secondary infection (40.7% vs 11.8% p < 0.001) (Table 4 ). However, on multivariable analysis, secondary infections were not associated with increased risk of death (OR 0.85, 95%CI 0.40-1.80, p = 0.67) (Appendix 1). Table 4 Outcomes of patients with Hospital Associated Infections No HAI HAI p Total Died (%) 11.8 40.7 <0.001 LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 Disposition (%) <0.001 Home 70.8 26.5 Home with health 12.9 17.6 Rehab/SNF/LTAC/Acute care 13.6 35.3 Others 2.6 20.6 |
| table-wrap | Table 4 Outcomes of patients with Hospital Associated Infections No HAI HAI p Total Died (%) 11.8 40.7 <0.001 LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 Disposition (%) <0.001 Home 70.8 26.5 Home with health 12.9 17.6 Rehab/SNF/LTAC/Acute care 13.6 35.3 Others 2.6 20.6 |
| label | Table 4 |
| caption | Outcomes of patients with Hospital Associated Infections |
| p | Outcomes of patients with Hospital Associated Infections |
| table | No HAI HAI p Total Died (%) 11.8 40.7 <0.001 LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 Disposition (%) <0.001 Home 70.8 26.5 Home with health 12.9 17.6 Rehab/SNF/LTAC/Acute care 13.6 35.3 Others 2.6 20.6 |
| tr | No HAI HAI p |
| th | No HAI |
| th | HAI |
| th | p |
| tr | Total |
| td | Total |
| tr | Died (%) 11.8 40.7 <0.001 |
| td | Died (%) |
| td | 11.8 |
| td | 40.7 |
| td | <0.001 |
| tr | LOS in survivors, median (IQR) 5(3-9) 32(26-41) <0.001 |
| td | LOS in survivors, median (IQR) |
| td | 5(3-9) |
| td | 32(26-41) |
| td | <0.001 |
| tr | Time to death, median (IQR) 8(3-15) 25.5(20.5-30.5) <0.001 |
| td | Time to death, median (IQR) |
| td | 8(3-15) |
| td | 25.5(20.5-30.5) |
| td | <0.001 |
| tr | Disposition (%) <0.001 |
| td | Disposition (%) |
| td | <0.001 |
| tr | Home 70.8 26.5 |
| td | Home |
| td | 70.8 |
| td | 26.5 |
| tr | Home with health 12.9 17.6 |
| td | Home with health |
| td | 12.9 |
| td | 17.6 |
| tr | Rehab/SNF/LTAC/Acute care 13.6 35.3 |
| td | Rehab/SNF/LTAC/Acute care |
| td | 13.6 |
| td | 35.3 |
| tr | Others 2.6 20.6 |
| td | Others |
| td | 2.6 |
| td | 20.6 |
| p | The length of hospital stay was significantly longer in patients developing secondary infections. Disposition in patients with secondary infection was significantly higher to skilled nursing facility and long-term acute care (SNF/LTAC) when compared to those who did not have secondary infection (35.3% vs 13.6%, p < 0.001). |
| sec | DISCUSSION COVID-19 pandemic has observed use of immunomodulating medications to help prevent dysregulated immune response also called cytokine storm(Wiersinga et al., 2020). We report our experience of all COVID-19 patients who were admitted to the hospital till August 5thth,2020. There were more than 1500 patients, of which 250 patients required intubation from severe COVID-19. We have observed number of patients that develop HAIs to be about 3.7% (Table 5 ). Table 5 Factors associated with development of HAIs. Odds Ratio 95% confidence intervals p Age 0.99 0.96-1.01 0.58 Male gender 0.76 0.37-1.53 0.45 DM 1.29 0.62-2.69 0.48 ESRD 1.05 0.24-4.55 0.94 COPD 0.82 0.38-1.77 0.62 Cancer 1.36 0.37-4.98 0.63 Hydroxychloroquine* 2.96 1.00-8.86 0.05 Steroids* 3.79 1.44-10.01 0.007 Tocilizumab* 5.04 2.39-10.65 <0.001 Convalescent plasma 1.86 0.88-3.92 0.10 Central venous catheter 2.47 0.87-6.97 0.088 Mechanical ventilation 1.11 0.34-3.54 0.86 AKI requiring hemodialysis* 3.67 1.05-12.80 0.04 Antibiotics on admission 1.02 0.31-3.32 0.96 SOFA score > 2 on admission 1.21 0.52-2.76 0.65 Multiple host and environmental factors determine development of HAI in any hospitalized patient. Secondary infections in post viral infection emanate from increased immune susceptibility, increasing risk for co-infections and HAIs(Hendaus et al., 2015, Smith and McCullers, 2014). In patients with influenza virus, there is increased susceptibility to bacterial adhesion by bacteria such as Streptococcus and pseudomonas(Pittet et al., 2010). Antibiotics used for bacterial infections can result in changes in normal flora of the host(Haak and Wiersinga, 2017). Patients with prolonged hospital course are at risk of nosocomial infections as well. However, modifiable factors such as – protocols for mitigation of infection, staff education and practices and hospital policies for lines and catheters also play a role. Before COVID-19 pandemic, various CMS measures were in place at our hospital to prevent VAP, CLABSI and CAUTI. With immense surges of COVID-19, various system in place were under stress and unable to be utilized at their normal capacity. At our center, number of covid-19 patients in the ICU in the peak times ranged from 30 to 45. Our hospital system supported the COVID-19 crisis with additional 50 beds in surge capacity. Both doctors and nursing capacity was increased to cope up with the increase in patient volumes. Due to this extra support, we were still able to perform CMS core measures to a reasonable extent. Our rates of HAI may be difficult to compare to other hospital system due to this reason. Similar to previous studies on HAIs, use of central venous lines was associated with higher rates of both bacterial and fungal infections(Muskett et al., 2011, Zhao et al., 2016). Use of antibiotics was associated with risk of fungal infections. All 11 patients who developed fungal infections received antibiotics on admission. Since most of the COVID-19 patients present with fever, cough and CXR findings of pneumonia, many of these patients receive empiric antibiotics for community acquired pneumonia. However, patients receiving cefepime and vancomycin on admission had higher rates of HAIs. Tocilizumab has been used very often in COVID-19 due to concerns about cytokine storm from deregulated IL6 pathway being the underlying problem causing the severe disease(Morena et al., 2020). This drug has been used for MAS-HLH induced cytokine storm in disease like rheumatoid arthritis(Grom et al., 2016). Its use in ICU has not been studied and is likely not risk free as it blocks IL6R which is an important regulator of immune system. Tocilizumab was studied in 16,074 rheumatoid arthritis patients and was associated with increased risks bacterial infections – diverticulitis, pneumonia, and bacteremia(Pawar et al., 2019). Tocilizumab was given to 210 patients with severe COVID-19 at our center, of which 42(20%) developed HAI. We observed tocilizumab was associated with increased risk of HAIs.. In COVID-19 patients receiving tocilizumab, HAI has been reported to be around 27-32% (Morena et al., 2020, Rossotti et al., 2020). In the initial phase of the pandemic, Hydroxychloroquine, was also used extensively at our center and 220 patients received this medication. Hydroxychloroquine destabilizes lysosomal membranes and promotes the release of lysosomal enzymes inside cells which can inhibit the function of lymphocytes and thus has immunomodulatory and anti-inflammatory effects(Schrezenmeier and Dorner, 2020). We found its association with HAIs, though the lower limit of confidence interval was one.. Though we have focused on culture positive organisms, there are significant number of patients who may have HAI but were culture negative. Both timing of cultures and use of antibiotics can affect the positivity of culture rates, with up to 50% of infection in ICU being culture negative(Gupta et al., 2016). To study these patients, we defined any fever/leukocytosis along with getting blood cultures and initiation of empiric antibiotics as possible infection. We observed only 40% culture positive rates when patient qualified as ‘possible infection’. Though we report our experience with over 1500 covid-19 patients, our study has certain limitations. It is a single center study and it would be difficult to extrapolate the results to other centers which may have different support systems, protocols for prevention of infections and usage of COVID-19 medications. Retrospective nature of our study prevents us from drawing any causative risk factors. There may be other underlying factors that we do not know that would affect development of HAI in COVID-19 patients. Lastly, there were missing values with respect to the inflammatory markers. We were therefore unable to use them in our regression models which may have improved our model for this disease. Despite these limitations, our study provides insight into rates of HAIs in this group of patients and assess risk factors.. Cautious use of hydroxychloroquine and tocilizumab is advised as these drugs can lead to higher rates of HAIs and have not shown to be beneficial (Kiley, 2020, Relations RGM, 2020). CMS guidelines for prevention of hospital acquired infections is vital. |
| title | DISCUSSION |
| p | COVID-19 pandemic has observed use of immunomodulating medications to help prevent dysregulated immune response also called cytokine storm(Wiersinga et al., 2020). We report our experience of all COVID-19 patients who were admitted to the hospital till August 5thth,2020. There were more than 1500 patients, of which 250 patients required intubation from severe COVID-19. We have observed number of patients that develop HAIs to be about 3.7% (Table 5 ). Table 5 Factors associated with development of HAIs. Odds Ratio 95% confidence intervals p Age 0.99 0.96-1.01 0.58 Male gender 0.76 0.37-1.53 0.45 DM 1.29 0.62-2.69 0.48 ESRD 1.05 0.24-4.55 0.94 COPD 0.82 0.38-1.77 0.62 Cancer 1.36 0.37-4.98 0.63 Hydroxychloroquine* 2.96 1.00-8.86 0.05 Steroids* 3.79 1.44-10.01 0.007 Tocilizumab* 5.04 2.39-10.65 <0.001 Convalescent plasma 1.86 0.88-3.92 0.10 Central venous catheter 2.47 0.87-6.97 0.088 Mechanical ventilation 1.11 0.34-3.54 0.86 AKI requiring hemodialysis* 3.67 1.05-12.80 0.04 Antibiotics on admission 1.02 0.31-3.32 0.96 SOFA score > 2 on admission 1.21 0.52-2.76 0.65 |
| table-wrap | Table 5 Factors associated with development of HAIs. Odds Ratio 95% confidence intervals p Age 0.99 0.96-1.01 0.58 Male gender 0.76 0.37-1.53 0.45 DM 1.29 0.62-2.69 0.48 ESRD 1.05 0.24-4.55 0.94 COPD 0.82 0.38-1.77 0.62 Cancer 1.36 0.37-4.98 0.63 Hydroxychloroquine* 2.96 1.00-8.86 0.05 Steroids* 3.79 1.44-10.01 0.007 Tocilizumab* 5.04 2.39-10.65 <0.001 Convalescent plasma 1.86 0.88-3.92 0.10 Central venous catheter 2.47 0.87-6.97 0.088 Mechanical ventilation 1.11 0.34-3.54 0.86 AKI requiring hemodialysis* 3.67 1.05-12.80 0.04 Antibiotics on admission 1.02 0.31-3.32 0.96 SOFA score > 2 on admission 1.21 0.52-2.76 0.65 |
| label | Table 5 |
| caption | Factors associated with development of HAIs. |
| p | Factors associated with development of HAIs. |
| table | Odds Ratio 95% confidence intervals p Age 0.99 0.96-1.01 0.58 Male gender 0.76 0.37-1.53 0.45 DM 1.29 0.62-2.69 0.48 ESRD 1.05 0.24-4.55 0.94 COPD 0.82 0.38-1.77 0.62 Cancer 1.36 0.37-4.98 0.63 Hydroxychloroquine* 2.96 1.00-8.86 0.05 Steroids* 3.79 1.44-10.01 0.007 Tocilizumab* 5.04 2.39-10.65 <0.001 Convalescent plasma 1.86 0.88-3.92 0.10 Central venous catheter 2.47 0.87-6.97 0.088 Mechanical ventilation 1.11 0.34-3.54 0.86 AKI requiring hemodialysis* 3.67 1.05-12.80 0.04 Antibiotics on admission 1.02 0.31-3.32 0.96 SOFA score > 2 on admission 1.21 0.52-2.76 0.65 |
| tr | Odds Ratio 95% confidence intervals p |
| th | Odds Ratio |
| th | 95% confidence intervals |
| th | p |
| tr | Age 0.99 0.96-1.01 0.58 |
| td | Age |
| td | 0.99 |
| td | 0.96-1.01 |
| td | 0.58 |
| tr | Male gender 0.76 0.37-1.53 0.45 |
| td | Male gender |
| td | 0.76 |
| td | 0.37-1.53 |
| td | 0.45 |
| tr | DM 1.29 0.62-2.69 0.48 |
| td | DM |
| td | 1.29 |
| td | 0.62-2.69 |
| td | 0.48 |
| tr | ESRD 1.05 0.24-4.55 0.94 |
| td | ESRD |
| td | 1.05 |
| td | 0.24-4.55 |
| td | 0.94 |
| tr | COPD 0.82 0.38-1.77 0.62 |
| td | COPD |
| td | 0.82 |
| td | 0.38-1.77 |
| td | 0.62 |
| tr | Cancer 1.36 0.37-4.98 0.63 |
| td | Cancer |
| td | 1.36 |
| td | 0.37-4.98 |
| td | 0.63 |
| tr | Hydroxychloroquine* 2.96 1.00-8.86 0.05 |
| td | Hydroxychloroquine* |
| td | 2.96 |
| td | 1.00-8.86 |
| td | 0.05 |
| tr | Steroids* 3.79 1.44-10.01 0.007 |
| td | Steroids* |
| td | 3.79 |
| td | 1.44-10.01 |
| td | 0.007 |
| tr | Tocilizumab* 5.04 2.39-10.65 <0.001 |
| td | Tocilizumab* |
| td | 5.04 |
| td | 2.39-10.65 |
| td | <0.001 |
| tr | Convalescent plasma 1.86 0.88-3.92 0.10 |
| td | Convalescent plasma |
| td | 1.86 |
| td | 0.88-3.92 |
| td | 0.10 |
| tr | Central venous catheter 2.47 0.87-6.97 0.088 |
| td | Central venous catheter |
| td | 2.47 |
| td | 0.87-6.97 |
| td | 0.088 |
| tr | Mechanical ventilation 1.11 0.34-3.54 0.86 |
| td | Mechanical ventilation |
| td | 1.11 |
| td | 0.34-3.54 |
| td | 0.86 |
| tr | AKI requiring hemodialysis* 3.67 1.05-12.80 0.04 |
| td | AKI requiring hemodialysis* |
| td | 3.67 |
| td | 1.05-12.80 |
| td | 0.04 |
| tr | Antibiotics on admission 1.02 0.31-3.32 0.96 |
| td | Antibiotics on admission |
| td | 1.02 |
| td | 0.31-3.32 |
| td | 0.96 |
| tr | SOFA score > 2 on admission 1.21 0.52-2.76 0.65 |
| td | SOFA score > 2 on admission |
| td | 1.21 |
| td | 0.52-2.76 |
| td | 0.65 |
| p | Multiple host and environmental factors determine development of HAI in any hospitalized patient. Secondary infections in post viral infection emanate from increased immune susceptibility, increasing risk for co-infections and HAIs(Hendaus et al., 2015, Smith and McCullers, 2014). In patients with influenza virus, there is increased susceptibility to bacterial adhesion by bacteria such as Streptococcus and pseudomonas(Pittet et al., 2010). Antibiotics used for bacterial infections can result in changes in normal flora of the host(Haak and Wiersinga, 2017). Patients with prolonged hospital course are at risk of nosocomial infections as well. |
| p | However, modifiable factors such as – protocols for mitigation of infection, staff education and practices and hospital policies for lines and catheters also play a role. Before COVID-19 pandemic, various CMS measures were in place at our hospital to prevent VAP, CLABSI and CAUTI. With immense surges of COVID-19, various system in place were under stress and unable to be utilized at their normal capacity. At our center, number of covid-19 patients in the ICU in the peak times ranged from 30 to 45. Our hospital system supported the COVID-19 crisis with additional 50 beds in surge capacity. Both doctors and nursing capacity was increased to cope up with the increase in patient volumes. Due to this extra support, we were still able to perform CMS core measures to a reasonable extent. Our rates of HAI may be difficult to compare to other hospital system due to this reason. |
| p | Similar to previous studies on HAIs, use of central venous lines was associated with higher rates of both bacterial and fungal infections(Muskett et al., 2011, Zhao et al., 2016). Use of antibiotics was associated with risk of fungal infections. All 11 patients who developed fungal infections received antibiotics on admission. Since most of the COVID-19 patients present with fever, cough and CXR findings of pneumonia, many of these patients receive empiric antibiotics for community acquired pneumonia. However, patients receiving cefepime and vancomycin on admission had higher rates of HAIs. |
| p | Tocilizumab has been used very often in COVID-19 due to concerns about cytokine storm from deregulated IL6 pathway being the underlying problem causing the severe disease(Morena et al., 2020). This drug has been used for MAS-HLH induced cytokine storm in disease like rheumatoid arthritis(Grom et al., 2016). Its use in ICU has not been studied and is likely not risk free as it blocks IL6R which is an important regulator of immune system. Tocilizumab was studied in 16,074 rheumatoid arthritis patients and was associated with increased risks bacterial infections – diverticulitis, pneumonia, and bacteremia(Pawar et al., 2019). Tocilizumab was given to 210 patients with severe COVID-19 at our center, of which 42(20%) developed HAI. We observed tocilizumab was associated with increased risk of HAIs.. In COVID-19 patients receiving tocilizumab, HAI has been reported to be around 27-32% (Morena et al., 2020, Rossotti et al., 2020). |
| p | In the initial phase of the pandemic, Hydroxychloroquine, was also used extensively at our center and 220 patients received this medication. Hydroxychloroquine destabilizes lysosomal membranes and promotes the release of lysosomal enzymes inside cells which can inhibit the function of lymphocytes and thus has immunomodulatory and anti-inflammatory effects(Schrezenmeier and Dorner, 2020). We found its association with HAIs, though the lower limit of confidence interval was one.. |
| p | Though we have focused on culture positive organisms, there are significant number of patients who may have HAI but were culture negative. Both timing of cultures and use of antibiotics can affect the positivity of culture rates, with up to 50% of infection in ICU being culture negative(Gupta et al., 2016). To study these patients, we defined any fever/leukocytosis along with getting blood cultures and initiation of empiric antibiotics as possible infection. We observed only 40% culture positive rates when patient qualified as ‘possible infection’. |
| p | Though we report our experience with over 1500 covid-19 patients, our study has certain limitations. It is a single center study and it would be difficult to extrapolate the results to other centers which may have different support systems, protocols for prevention of infections and usage of COVID-19 medications. Retrospective nature of our study prevents us from drawing any causative risk factors. There may be other underlying factors that we do not know that would affect development of HAI in COVID-19 patients. Lastly, there were missing values with respect to the inflammatory markers. We were therefore unable to use them in our regression models which may have improved our model for this disease. |
| p | Despite these limitations, our study provides insight into rates of HAIs in this group of patients and assess risk factors.. Cautious use of hydroxychloroquine and tocilizumab is advised as these drugs can lead to higher rates of HAIs and have not shown to be beneficial (Kiley, 2020, Relations RGM, 2020). CMS guidelines for prevention of hospital acquired infections is vital. |
| sec | Author contribution Gagan Kumar MD: study design, data analysis, manuscript writing Martin Herrera MD: manuscript writing Alex Adams MD: study design, manuscript writing Vartika Singh MD: study design, manuscript writing Mark Meersman CPA: data validation Drew Dalton BS: data validation Dhaval Patel MD: study design, manuscript writing Erine Rojas: study design, manuscript writing Shravan Kethireddy: study design, manuscript writing Ankit Sakhuja: Study design, data analysis, manuscript writing Achuta Kumar Guddati MD: Study design, data analysis, Manuscript writing Rahul Nanchal MD: Study design, data analysis, manuscript writing |
| title | Author contribution |
| p | Gagan Kumar MD: study design, data analysis, manuscript writing |
| p | Martin Herrera MD: manuscript writing |
| p | Alex Adams MD: study design, manuscript writing |
| p | Vartika Singh MD: study design, manuscript writing |
| p | Mark Meersman CPA: data validation |
| p | Drew Dalton BS: data validation |
| p | Dhaval Patel MD: study design, manuscript writing |
| p | Erine Rojas: study design, manuscript writing |
| p | Shravan Kethireddy: study design, manuscript writing |
| p | Ankit Sakhuja: Study design, data analysis, manuscript writing |
| p | Achuta Kumar Guddati MD: Study design, data analysis, Manuscript writing |
| p | Rahul Nanchal MD: Study design, data analysis, manuscript writing |
| sec | Ethical approval The study was reviewed and found exempt by Northeast Georgia Health System IRB board |
| title | Ethical approval |
| p | The study was reviewed and found exempt by Northeast Georgia Health System IRB board |
| sec | Conflict of interest The authors declare no competing financial interests. Financial Support/Grant None. |
| title | Conflict of interest |
| p | The authors declare no competing financial interests. |
| p | Financial Support/Grant |
| p | None. |
| back | Appendix A Supplementary data The following are Supplementary data to this article: Appendix A Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijid.2020.11.135. |
| sec | Appendix A Supplementary data The following are Supplementary data to this article: |
| label | Appendix A |
| title | Supplementary data |
| p | The following are Supplementary data to this article: |
| footnote | Appendix A Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijid.2020.11.135. |
| label | Appendix A |
| p | Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijid.2020.11.135. |
Annnotations TAB TSV DIC JSON TextAE Lectin_function IAV-Glycan
last updated at 2021-04-26 05:26:10 UTC
- Denotations: 2
- Blocks: 0
- Relations: 0