LitCovid-sentences  

PMC:7116472 / 17639-24288 JSONTXT 3 Projects

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Id Subject Object Predicate Lexical cue
T118 0-7 Sentence denotes Results
T119 8-184 Sentence denotes We collected data from 35 463 patients between 6 February 2020 and 20 May 2020 in the derivation cohort; 1275 (3.6%) patients had no outcome recorded and were considered alive.
T120 185-240 Sentence denotes The overall mortality rate was 32.2% (11 426 patients).
T121 241-399 Sentence denotes The median age of patients in the cohort was 73 years (interquartile range 59-83); 41.7% (14 741) were female and 76.0% (26 966) had at least one comorbidity.
T122 400-498 Sentence denotes Table 1 shows demographic and clinical characteristics for the derivation and validation datasets.
T123 500-517 Sentence denotes Model development
T124 518-635 Sentence denotes We identified 41 candidate predictor variables measured at hospital admission for model creation (fig 1, appendix 2).
T125 636-821 Sentence denotes After the creation of a composite variable containing all seven individual comorbidities and the exclusion of 13 variables owing to high levels of missing values, 21 variables remained.
T126 822-1135 Sentence denotes We identified eight important predictors of mortality by using generalised additive modelling with multiply imputed datasets: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, Glasgow coma scale, urea level, and C reactive protein (for variable selection process, see appendix 3).
T127 1136-1386 Sentence denotes Given the need for a pragmatic score for use at the bedside, continuous variables were converted to factors with cut-off values chosen by using component smoothed functions (on linear predictor scale) from generalised additive modelling (appendix 4).
T128 1387-1567 Sentence denotes On entering variables into a penalised logistic regression model (least absolute shrinkage and selection operator), all variables were retained within the final model (appendix 5).
T129 1568-1716 Sentence denotes We converted penalised regression coefficients into a prognostic index by using appropriate scaling (4C Mortality Score range 0-21 points; table 2).
T130 1717-1884 Sentence denotes The 4C Mortality Score showed good discrimination for death in hospital within the derivation cohort (table 3), with performance approaching that of the XGBoost model.
T131 1885-2069 Sentence denotes The 4C Mortality Score showed good calibration (calibration intercept=0, slope=1, Brier score 0.170) across the range of risk and no adjustment to the model was required (appendix 11).
T132 2071-2087 Sentence denotes Model validation
T133 2088-2304 Sentence denotes The validation cohort included data from 22 361 patients collected between 21 May 2020 and 29 June 2020 who had at least four weeks of follow-up; 743 (3.3%) patients had no outcome recorded and were considered alive.
T134 2305-2358 Sentence denotes The overall mortality rate was 30.1% (6729 patients).
T135 2359-2525 Sentence denotes The median age of patients in the cohort was 76 (interquartile range 60-85) years; 10 178 (45.6%) were female and 17 263 (77%) had at least one comorbidity (table 1).
T136 2526-2643 Sentence denotes Discrimination of the 4C Mortality Score in the validation cohort was similar to that of the XGBoost model (table 3).
T137 2644-2901 Sentence denotes Calibration was also found to be excellent in the validation cohort: overall observed (30.1%) versus predicted (30.1%) mortality was equal (calibration-in-the-large=0) and calibration was excellent over the range of risk (slope=1, Brier score 0.171; fig 2).
T138 2902-3023 Sentence denotes The 4C Mortality Score showed good performance in clinically relevant metrics across a range of cut-off values (table 4).
T139 3024-3260 Sentence denotes Four risk groups were defined with corresponding mortality rates determined (table 5): low risk (0-3 score, mortality rate 1.2%), intermediate risk (4-8 score, 9.9%), high risk (9-14 score, 31.4%), and very high risk (≥15 score, 61.5%).
T140 3261-3448 Sentence denotes Performance metrics showed a high sensitivity (99.7%) and negative predictive value (98.8%) for the low risk group, covering 7.4% of the cohort and a corresponding mortality rate of 1.2%.
T141 3449-3579 Sentence denotes Patients in the intermediate risk group (score 4-8, n=4889, 21.9%) had a mortality rate of 9.9% (negative predictive value 90.1%).
T142 3580-3823 Sentence denotes Patients in the high risk group (score 9-14, n=11 664, 52.2%) had a mortality rate of 31.4% (negative predictive value 68.6%), while patients scoring 15 or higher (n=4158, 18.6%) had a mortality rate of 61.5% (positive predictive value 61.5%).
T143 3824-3892 Sentence denotes An interactive infographic is available at https://isaric4c.net/risk
T144 3894-3924 Sentence denotes Comparison with existing tools
T145 3925-4211 Sentence denotes We performed a systematic literature search and identified 15 risk stratification scores that could beapplied to these data.62228-40 The 4C Mortality Score compared well against these existing risk stratification scores in predicting in-hospital mortality (table 6, fig 3, upper panel).
T146 4212-4595 Sentence denotes Risk stratification scores originally validated in patients with community acquired pneumonia (n=9) generally had higher discrimination for inhospital mortality in the validation cohort (eg, A-DROP (area under the receiver operating characteristic curve 0.74, 95% confidence interval 0.73 to 0.74) and E-CURB65 (0.76, 0.74 to 0.79)) than those developed within covid-19 cohorts (n=4:
T147 4596-4730 Sentence denotes Surgisphere (0.63, 0.62 to 0.64), DL score (0.67, 0.66 to 0.68), COVID-GRAM (0.71, 0.68 to 0.74), and Xie score (0.73, 0.70 to 0.75)).
T148 4731-4878 Sentence denotes Performance metrics for the 4C Mortality Score compared well against existing risk stratification scores at specified cut-off values (appendix 13).
T149 4879-5110 Sentence denotes The number of patients in whom risk stratification scores could be applied differed owing to certain variables not being available, either because of missingness or because they were not tested for or recorded in clinical practice.
T150 5111-5330 Sentence denotes Seven scores could be applied to fewer than 2000 patients (<10%) in the validation cohort owing to the requirement for biomarkers or physiological parameters that were not routinely captured (eg, lactate dehydrogenase).
T151 5331-5600 Sentence denotes Decision curve analysis showed that the 4C Mortality Score had better clinical utility across a wide range of threshold risks compared with the best performing existing scores applicable to more than 50% of the validation cohort (A-DROP and CURB65; fig 3, lower panel).
T152 5602-5622 Sentence denotes Sensitivity analysis
T153 5623-5803 Sentence denotes Sensitivity analyses that used complete case data showed similar discrimination (appendix 7) and performance metrics (appendices 8 and 9) to analyses that used the imputed dataset.
T154 5804-6152 Sentence denotes After stratification of the validation cohort into two geographical cohorts (validation north and south; appendix 14), discrimination remained similar for the 4C Mortality Score in the north subset (area under the receiver operating characteristic curve 0.77, 95% confidence interval 0.76 to 0.78) and south subset (0.76, 0.75 to 0.77; appendix 6).
T155 6153-6252 Sentence denotes Finally, we checked discrimination of the 4C Mortality Score by sex and ethnic group (appendix 10).
T156 6253-6419 Sentence denotes Discrimination was the same in men (area under the receiver operating characteristic curve 0.77, 95% confidence interval 0.76 to 0.78) and women (0.76, 0.75 to 0.77).
T157 6420-6506 Sentence denotes Discrimination was better in all nonwhite ethnic groups compared with the white group:
T158 6507-6649 Sentence denotes South Asian (0.82, 0.80 to 0.85), East Asian (0.85, 0.79 to 0.91), Black (0.83, 0.80 to 0.86), and other ethnic minority (0.81, 0.79 to 0.84).