A Novel Prediction Model of COVID-19 Progression: A Retrospective Cohort Study

Abstract

Introduction: Estimating the risk of disease progression is of utmost importance for planning appropriate setting of care and treatment for patients with coronavirus disease 2019 (COVID-19). This study aimed to develop and validate a novel prediction model of COVID-19 progression. Methods: In total, 814 patients in the training set were included to develop a novel scoring system; and 420 patients in the validation set were included to validate the model. Results: A prediction score, called ACCCDL, was developed on the basis of six risk factors associated with COVID-19 progression: age, comorbidity, CD4+ T cell count, C-reactive protein (CRP), D-dimer, and lactate dehydrogenase (LDH). For predicting COVID-19 progression, the ACCCDL score yielded a significantly higher area under the receiver operating characteristic curve (AUROC) compared with the CALL score, CoLACD score, PH-COVID-19 score, neutrophil–lymphocyte ratio, and lymphocyte–monocyte ratio both in the training set (0.92, 0.84, 0.83, 0.83, 0.76, and 0.65, respectively) and in the validation set (0.97, 0.83, 0.83, 0.78, 0.74, and 0.60, respectively). Over 99% of patients with the ACCCDL score < 12 points will not progress to severe cases, and over 30% of patients with the ACCCDL score > 20 points will progress to severe cases. Conclusion: The ACCCDL score could stratify patients with at risk of COVID-19 progression, and was useful in regulating the large flow of patients with COVID-19 between primary health care and tertiary centers.