PMC:7116472 / 25509-28333 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7116472","sourcedb":"PMC","sourceid":"7116472","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7116472","text":"Comparison with other studies\nThe 4C Mortality Score contains parameters reflecting patient demographics, comorbidity, physiology, and inflammation at hospital admission; it shares characteristics with existing prognostic scores for sepsis and community acquired pneumonia but has important differences as well. No preexisting score appears to use this combination of variables and weightings. Altered consciousness and high respiratory rate are included in most risk stratification scores for sepsis and community acquired pneumonia,21222829323336 while raised urea is also a common component.212228 Increasing age is a strong predictor of in-hospital mortality in our cohort of patients admitted with covid-19 and is commonly included in other existing covid-19 scores,374142 together with comorbidity374142 and raised C reactive protein.4043\nDiscriminatory performance of existing covid-19 scores applied to our cohort was lower than reported in derivation cohorts (DL score 0.74, COVID-GRAM 0.88, Xie score 0.98).373840 The use of small inpatient cohorts from Wuhan, China for model development might have resulted in overfitting, limiting generalisability in other cohorts.3840 The Xie score demonstrated the highest discriminatory power (0.73), and included age, lymphocyte count, lactate dehydrogenase, and peripheral oxygen saturations. However, we were only able to apply this score for less than 10% of the validation cohort because lactate dehydrogenase is not routinely measured on hospital admission in the UK.\nOwing to challenges of clinical data collection during an epidemic, missing data are common, with choice of predictors influenced by data availability.40 Complete case analysis often leads to exclusion of a substantial proportion of the original sample, subsequently leading to a loss of precision and power.44 However, the assessment of missing data on model performance in novel covid-19 risk stratification scores has been limited37 or unexplored,3840 potentially introducing bias and further limiting generalisability to other cohorts. We found discriminatory performance in both derivation and validation cohorts remained similar after the imputation of a wide range of variables,41 further supporting the validity of our findings.\nThe presence of comorbidities is handled differently in covid-19 prognostic scores; comorbidities might be included individually,4042 given equal weight,37 or found to have no predictive effect.38 Recent evidence suggests an additive effect of comorbidity in patients with covid-19, with increasing number of comorbidities associated with poorer outcomes.16 In our cohort, the inclusion of individual comorbidities within the machine learning model conferred minimal additional discriminatory performance, supporting the inclusion of an overall comorbidity 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