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Development and validation of clinical prediction model to estimate the probability of death in hospitalized patients with COVID‐19: Insights from a nationwide database
Author(s) -
Tanboğa Ibrahim Halil,
Canpolat Uğur,
Çetin Elif Hande Özcan,
Kundi Harun,
Çelik Osman,
Çağlayan Murat,
Ata Naim,
Özeke Özcan,
Çay Serkan,
Kaymaz Cihangir,
Topaloğlu Serkan
Publication year - 2021
Publication title -
journal of medical virology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 121
eISSN - 1096-9071
pISSN - 0146-6615
DOI - 10.1002/jmv.26844
Subject(s) - covid-19 , virology , medicine , database , computer science , disease , outbreak , infectious disease (medical specialty)
In the current study, we aimed to develop and validate a model, based on our nationwide centralized coronavirus disease 2019 (COVID‐19) database for predicting death. We conducted an observational study (CORONATION‐TR registry). All patients hospitalized with COVID‐19 in Turkey between March 11 and June 22, 2020 were included. We developed the model and validated both temporal and geographical models. Model performances were assessed by area under the curve‐receiver operating characteristic (AUC‐ROC or c‐index), R 2 , and calibration plots. The study population comprised a total of 60,980 hospitalized COVID‐19 patients. Of these patients, 7688 (13%) were transferred to intensive care unit, 4867 patients (8.0%) required mechanical ventilation, and 2682 patients (4.0%) died. Advanced age, increased levels of lactate dehydrogenase, C‐reactive protein, neutrophil–lymphocyte ratio, creatinine, albumine, and D‐dimer levels, and pneumonia on computed tomography, diabetes mellitus, and heart failure status at admission were found to be the strongest predictors of death at 30 days in the multivariable logistic regression model (area under the curve‐receiver operating characteristic = 0.942; 95% confidence interval: 0.939–0.945; R 2 = .457). There were also favorable temporal and geographic validations. We developed and validated the prediction model to identify in‐hospital deaths in all hospitalized COVID‐19 patients. Our model achieved reasonable performances in both temporal and geographic validations.