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

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.

Figure 1

Nomogram incorporating nine variables for predicting the risk of progression to severe COVID-19. An individual patient’s value is located on each variable axis, and a line is drawn upward to obtain the scores for each variable value. Then summarized the individual scores to computer total scores on the total points axis, and a line is drawn vertically downward to the bottom axis to determine the likelihood of occurrence of severe illness. COVID-19, coronavirus disease 2019; ALT, alanine aminotransferase; cTnI, cardiac troponin I; PCT, procalcitonin.

Figure 2

Receiver-operating characteristic curve of the nomogram for predicting severe COVID-19 (A) and calibration curve of the nomogram (B) in training cohort. Nomogram-predicted probability of occurrence of severe illness is plotted on the x-axis, actual probability of severe illness is plotted on the y-axis. COVID-19, coronavirus disease 2019; ROC, receiver operating characteristic; AUC, area under the curve.

Figure 3

Receiver-operating characteristic curve of the nomogram for predicting severe COVID-19 (A) and calibration curve of the nomogram (B) in external validation cohort. Nomogram-predicted probability of occurrence of severe illness is plotted on the x-axis, actual probability of severe illness is plotted on the y-axis. COVID-19, coronavirus disease 2019; ROC, receiver operating characteristic; AUC, area under the curve.