Derivation and external validation of a nomogram predicting the occurrence of severe illness among hospitalized coronavirus disease 2019 patients: a 2020 Chinese multicenter retrospective study

Yun Feng; Luyu Yang; Zenghui Cheng; Jian Li

doi:10.21037/jtd-23-653

Derivation and external validation of a nomogram predicting the occurrence of severe illness among hospitalized coronavirus disease 2019 patients: a 2020 Chinese multicenter retrospective study

J Thorac Dis. 2023 Dec 30;15(12):6589-6603. doi: 10.21037/jtd-23-653. Epub 2023 Dec 15.

Authors

Yun Feng¹, Luyu Yang², Zenghui Cheng³, Jian Li⁴

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Department of ICU/Emergency, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China.
³ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: The worldwide pandemic of coronavirus disease 2019 (COVID-19) has still been an overwhelming public health challenge, and it is vital to identify determinants early to forecast the risk of severity using indicators easily available at admission. The current multicenter retrospective study aimed to derive and validate a user-friendly and effective nomogram to address this issue.

Methods: A training cohort consisting of 437 confirmed COVID-19 cases from three hospitals in Hubei province (Tongji Hospital affiliated with Huazhong University of Science and Technology, Wuhan Third Hospital of Wuhan University and Wuhan Jinyintan Hospital in Hubei province) was retrospectively analyzed to construct a predicting model, and another cohort of 161 hospitalized patients from Public Health Clinical Center of Shanghai was selected as an external validation cohort from January 1, 2020 to March 8, 2020. Determinants of developing into severe COVID-19 were probed using univariate regression together with a multivariate stepwise regression model. The risk of progression to severe COVID-19 was forecasted using the derived nomogram. The performances of the nomogram regarding the discrimination and calibration were assessed in the cohort of training as well as the cohort of external validation, respectively.

Results: A total of 144 (32.95%) and 54 (33.54%) patients, respectively, in cohorts of training and validation progressed to severe COVID-19 during hospitalization. Multivariable analyses showed determinants of severity consisted of hypertension, shortness of breath, platelet count, alanine aminotransferase (ALT), potassium, cardiac troponin I (cTnI), myohemoglobin, procalcitonin (PCT) and intervals from onset to diagnosis. The nomogram had good discrimination with concordance indices being 0.887 (95% CI: 0.854-0.919) and 0.850 (95% CI: 0.815-0.885) in internal and external validation, respectively. Calibration curves exhibited excellent concordance between the predictions by nomogram and actual observations in two cohorts.

Conclusions: We have established and validated an early predicting nomogram model, which can contribute to determine COVID-19 cases at risk of progression to severe illness.

Keywords: Nomogram; coronavirus disease 2019 (COVID-19); prediction; severity.