Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID‐19 patients: Multicenter retrospective study. Issue 1 (30th October 2020)

• Record Type: Journal Article
• Authors: Zeng, Zihang; Ma, Yiming; Zeng, Huihui; Huang, Peng; Liu, Wenlong; Jiang, Mingyan; Xiang, Xudong; Deng, Dingding; Liao, Xin; Chen, Ping; Chen, Yan
• Other Names: Luo Guangxiang (George) guestEditor.; Ly Hinh guestEditor.; Gao Shou‐Jiang guestEditor.
• Is Part Of: Journal of medical virology. Volume 93:Issue 1(2021)
• Page Start: 434
• Page End: 440
• Publication Date: 2020-10-30
• Subjects: COVID‐19 -- ICU transfer -- laboratory examination -- nomogram; Virology -- Periodicals; 616
• DOI: 10.1002/jmv.26244
• Languages: English
• ISSNs: 0146-6615

Abstract

Abstract: This retrospective, multicenter study investigated the risk factors associated with intensive care unit (ICU) admission and transfer in 461 adult patients with confirmed coronavirus disease 2019 (COVID‐19) hospitalized from 22 January to 14 March 2020 in Hunan, China. Outcomes of ICU and non‐ICU patients were compared, and a simple nomogram for predicting the probability of ICU transfer after hospital admission was developed based on initial laboratory data using a Cox proportional hazards regression model. Differences in laboratory indices were observed between patients admitted to the ICU and those who were not admitted. Several independent predictors of ICU transfer in COVID‐19 patients were identified including older age (≥65 years) (hazard ratio [HR] = 4.02), hypertension (HR = 2.65), neutrophil count (HR = 1.11), procalcitonin level (HR = 3.67), prothrombin time (HR = 1.28), and D‐dimer level (HR = 1.25). The lymphocyte count and albumin level were negatively associated with mortality (HR = 0.08 and 0.86, respectively). The developed model provides a means for identifying, at hospital admission, the subset of patients with COVID‐19 who are at high risk of progression and would require transfer to the ICU within 3 and 7 days after hospitalization. This method of early patient triage allows a more effective allocation of limited medical resources. Highlights: In this cohort study involved 461 patients with confirmed COVID‐19, laboratory characteristics of critically ill patients was described. Risk factors associated with ICU admission and transfer was identified by Cox proportional hazard model. We transformed the complex regression equation into a visual graph (nomogram), making the results of the prediction model more readable. These findings may help predict disease progression and rationalize medical resources. If implemented, our model will be able to activate alerts in high‐risk patients early on admission.