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Published on: June 5, 2021
A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency
Qiang Li1, Jianliang Zhang1, Yun Ling2
1Department of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, 2901 Cao Lang Road, Shanghai, 201508, China.
Objectives:
We aimed to develop a simple algorithm to help early identification of SARS-CoV-2 infection patients with severe progression tendency.
Methods:
The univariable and multivariable analysis were computed to identify the independent predictors of COVID-19 progression. The prediction model was established in a retrospective training set of 322 COVID-19 patients and was re-evaluated in a prospective validation set of 317 COVID-19 patients.
Results:
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 COVID-19 progression. Consequently, the age-LDH-CD4 algorithm was derived as (age × LDH)/CD4 count. In the training set, the area under the ROC curve (AUROC) of 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). In the prospective validation set, the AUROC of age-LDH-CD4 model was also significantly higher than that of single CD4 count, LDH, or age (0.92, 0.75, 0.81, and 0.82, respectively). The age-LDH-CD4 ≥ 82 has high sensitive (81%) and specific (93%) for the early identification of COVID-19 patients with severe progression tendency.
Conclusions:
The age-LDH-CD4 model is a simple algorithm for early identifying patients with severe progression tendency following SARS-CoV-2 infection, and warrants further validation.
