Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May 21;1-8.
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

Affiliations
Free PMC article

A Simple Algorithm Helps Early Identification of SARS-CoV-2 Infection Patients With Severe Progression Tendency

Qiang Li et al. Infection. .
Free PMC article

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.

Conflict of interest statement

The authors declare no competing financial and/or non-financial interests.

Figures

Fig. 1
Fig. 1
ROC curves of the age-LDH-CD4 model and single index in the training set (a) and the prospective validation set (b). The AUROC of age-LDH-CD4 model was significantly higher than that of CD4 count, LDH, and age. In the training set, the AUROC of age-LDH-CD4 model was significantly higher than that of CD4 (p = 0.005), LDH (p = 0.025), and age (p < 0.001). In the prospective validation set, the AUROC of age-LDH-CD4 model was also significantly higher than that of CD4 (p = 0.021), LDH (p = 0.027), and age (p = 0.028)

Similar articles

See all similar articles

References

    1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382:727–733. doi: 10.1056/NEJMoa2001017. - DOI - PMC - PubMed
    1. World Health Organization. Coronavirus disease 2019 Situation Report–51 (2020) https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200311-sitrep-51-covid-19.pdf. Accessed 12 Mar 2020
    1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5. - DOI - PMC - PubMed
    1. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
    1. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan China. JAMA. 2020 doi: 10.1001/jama.2020.1585. - DOI - PubMed

LinkOut - more resources

Feedback