Numeric variables were reported as means and Standard Deviations (SDs) or medians and Interquartile Ranges (IQR), according to their distribution. Occasionally, variables were also stratified into categories to simplify their clinical interpretation. Categorical variables were reported as counts and proportions. The authors then used demographic, clinical, and laboratory data to develop prediction scoring systems.
The authors randomly split our participants into derivation and validation samples using a 1:1 ratio and selected 25 variables to feed our models based on their clinical relevance and causal relations: (1) Demographics: age, sex, race/ethnicity; (2) Clinical history: hypertension, diabetes mellitus, heart disease, stroke history, chronic obstructive pulmonary disease, rheumatologic disease, cancer; (3) COVID-19 symptoms: fever, muscle pain, dyspnea, cough, dysgeusia or anosmia, headache, diarrhea; (4) Admission laboratory: hemoglobin, neutrophile-to-lymphocyte ratio, creatinine, C-reactive protein. The model including the complete list of independent variables for each outcome was defined as Model 1. As sensitivity analyses, the authors also examined our models excluding the reported COVID-19 symptoms, as these variables were more likely to be affected by information bias, particularly among patients with a more severe clinical presentation on admission. The model excluding COVID-19 symptoms for each outcome was defined as Model 2.
Subsequently, the authors explored the association between each variable of interest and the primary outcomes in univariable logistic regressions and used stepwise logistic regression models to select the final predictors to build our scoring system (variables with p-values < 0.1 were retained). The authors used variation inflation factors to assess for collinearity.
In accordance with the resulting models, the authors attributed points to each predictor dividing their respective beta coefficients by the lowest available beta coefficient and rounding the results to the nearest integer (0 or 5). The authors then used the sum of these points to estimate risk scores for our sample and examine their accuracy to predict hospital death and ICU admission. The authors validated the performances of the risk scoring systems using Receiver Operating Characteristic (ROC) analyses and test characteristics, including the Youden index, sensitivities, specificities, positive predictive values, and negative predictive values. The authors used the Youden index to identify optimal cut-offs for each model according to the outcome of interest.
The authors also compared the predictive performances from our models and the National Early Warning Score-2 (NEWS-2)19 (link) and 4C Mortality Score.20 (link) The authors used reclassification tables and measures of net reclassification improvement (the net percentage events correctly classified upward) and integrated discrimination improvement (difference in discrimination slopes between two models).
The authors randomly split our participants into derivation and validation samples using a 1:1 ratio and selected 25 variables to feed our models based on their clinical relevance and causal relations: (1) Demographics: age, sex, race/ethnicity; (2) Clinical history: hypertension, diabetes mellitus, heart disease, stroke history, chronic obstructive pulmonary disease, rheumatologic disease, cancer; (3) COVID-19 symptoms: fever, muscle pain, dyspnea, cough, dysgeusia or anosmia, headache, diarrhea; (4) Admission laboratory: hemoglobin, neutrophile-to-lymphocyte ratio, creatinine, C-reactive protein. The model including the complete list of independent variables for each outcome was defined as Model 1. As sensitivity analyses, the authors also examined our models excluding the reported COVID-19 symptoms, as these variables were more likely to be affected by information bias, particularly among patients with a more severe clinical presentation on admission. The model excluding COVID-19 symptoms for each outcome was defined as Model 2.
Subsequently, the authors explored the association between each variable of interest and the primary outcomes in univariable logistic regressions and used stepwise logistic regression models to select the final predictors to build our scoring system (variables with p-values < 0.1 were retained). The authors used variation inflation factors to assess for collinearity.
In accordance with the resulting models, the authors attributed points to each predictor dividing their respective beta coefficients by the lowest available beta coefficient and rounding the results to the nearest integer (0 or 5). The authors then used the sum of these points to estimate risk scores for our sample and examine their accuracy to predict hospital death and ICU admission. The authors validated the performances of the risk scoring systems using Receiver Operating Characteristic (ROC) analyses and test characteristics, including the Youden index, sensitivities, specificities, positive predictive values, and negative predictive values. The authors used the Youden index to identify optimal cut-offs for each model according to the outcome of interest.
The authors also compared the predictive performances from our models and the National Early Warning Score-2 (NEWS-2)19 (link) and 4C Mortality Score.20 (link) The authors used reclassification tables and measures of net reclassification improvement (the net percentage events correctly classified upward) and integrated discrimination improvement (difference in discrimination slopes between two models).
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