Development and Validation of Clinical Prediction Models to Estimate the Probability of Death in Hospitalized Patients with COVID‐19: Insights from a Nationwide Database

The final multivariate logistic regression model presented includes age, heart failure, diabetes, detection of pneumonia on computed tomography, and some hematological and serological parameters at baseline (Tab. 3).1 To our knowledge, this is the largest cohort study for predicting 30‐day in‐hospit...

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Bibliographic Details
Published in:Journal of Medical Virology 2021-09, Vol.93 (9), p.5226-5227
Main Author: Cakir, Banu
Format: Article
Language:English
Subjects:
COVID-19
Letter to the Editor
Letters to the Editor
Prediction models
Virology
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Summary:The final multivariate logistic regression model presented includes age, heart failure, diabetes, detection of pneumonia on computed tomography, and some hematological and serological parameters at baseline (Tab. 3).1 To our knowledge, this is the largest cohort study for predicting 30‐day in‐hospital fatality (of 4.0%) among hospitalized patients with at least one positive reverse‐transcription polymerase chain reaction test for severe acute respiratory syndrome coronavirus 2.1 The findings affirm those obtained earlier from multicenter cohorts or national registries elsewhere.2-5 Nonetheless, conclusions are still prone to accuracy and efficiency discussions due to insufficient data on patient‐/disease‐related characteristics obtained through electronic patient records and the modeling strategy (i.e., backward elimination method, lacking clinical endorsement for proven predictors of prognosis, such as steroid use, and/or potential effect modifiers). The predictive performance of the models and validation across time and location is reported to be high, in the absence of comprehensive indices of health status and disease severity at baseline, the exact timing of various lab test and/or the types of treatment modalities used. From a clinical point of view and practical benefits, treating laboratory variables as continuous measures, with restricted cubic spline transformations (four knots) to capture nonlinear associations, might not be the best approach.1, 6 There are critical thresholds for several tests commonly used in clinics for patient triage or estimation of prognosis; such thresholds or the values obtained from earlier work could be used, when appropriate.7 If the authors revealed that the effect of potential predictors on death was not linear and the observed diffraction values did not match those in clinical practice (Fig. 1),1 we would rather use dummy coding for such variables.3 As presented (Tab. 3),1 the simple model implies a 4.3 fold increase in the risk odds ratio of 30‐day in‐hospital deaths for 1‐year of an increasing age, adjusting for other risk factors; it is not in line with our clinical experience of an increasing slope after 50 years.
ISSN:0146-6615
1096-9071
DOI:10.1002/jmv.27030