Objectives We aimed to develop a simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency.
Methods 322 SARS-COV-2 infection patients were respectively enrolled. The univariable and multivariable analysis were computed to identify the independent predictors of severe progression, and the prediction model was established based on independent predictors. The areas under the ROC curves (AUROCs) were used to evaluate the diagnostic performances.
Results Of 322 confirmed SARS-COV-2 infection patients, 11 were diagnosed as severe cases on admission, 15 developed to severe cases after admission, and 296 were non-severe cases. The multivariable analysis identified age (OR=1.061, p=0.028), lactate dehydrogenase (LDH) (OR=1.006, p=0.037), and CD4 count (OR=0.993, p=0.006) as the independent predictors of severe progression. Consequently, the age-LDH-CD4 algorithm was derived as (age×LDH)/CD4. The AUROC of the age-LDH-CD4 model was significantly higher than that of single CD4 count, LDH, or age (0.92, 0.85, 0.80, and 0.75, respectively). The age-LDH-CD4 model ≥ 82 has high sensitive (81%) and specific (93%) for the early identification of patients with severe progression tendency following SARS-CoV-2 infection.
Conclusions The age-LDH-CD4 model is a simple algorithm for early identifying cases with severe progression tendency in SARS-CoV-2 infection patients, and warrants further validation.