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Infection
. 2020 May 21.
doi: 10.1007/s15010-020-01446-z. Online ahead of print.
A Simple Algorithm Helps Early Identification of SARS-CoV-2 Infection Patients With Severe Progression Tendency
Qiang Li 1 , Jianliang Zhang 1 , Yun Ling 2 , Weixia Li 2 , Xiaoyu Zhang 1 , Hongzhou Lu 3 , Liang Chen 4
Affiliations
- PMID: 32440918
- DOI: 10.1007/s15010-020-01446-z
Abstract
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.
Keywords: 2019 novel coronavirus disease; Risk factors; Severe acute respiratory syndrome coronavirus 2; Severe progression.