A predictive model for the severity of COVID-19 in elderly patients

Abstract

Elderly patients with coronavirus disease 2019 (COVID-19) are more likely to develop severe or critical pneumonia, with a high fatality rate. To date, there is no model to predict the severity of COVID-19 in elderly patients. In this study, patients who maintained a non-severe condition and patients who progressed to severe or critical COVID-19 during hospitalization were assigned to the non-severe and severe groups, respectively. Based on the admission data of these two groups in the training cohort, albumin (odds ratio [OR] = 0.871, 95% confidence interval [CI]: 0.809 - 0.937, P < 0.001), d-dimer (OR = 1.289, 95% CI: 1.042 - 1.594, P = 0.019) and onset to hospitalization time (OR = 0.935, 95% CI: 0.895 - 0.977, P = 0.003) were identified as significant predictors for the severity of COVID-19 in elderly patients. By combining these predictors, an effective risk nomogram was established for accurate individualized assessment of the severity of COVID-19 in elderly patients. The concordance index of the nomogram was 0.800 in the training cohort and 0.774 in the validation cohort. The calibration curve demonstrated excellent consistency between the prediction of our nomogram and the observed curve. Decision curve analysis further showed that our nomogram conferred significantly high clinical net benefit. Collectively, our nomogram will facilitate early appropriate supportive care and better use of medical resources and finally reduce the poor outcomes of elderly COVID-19 patients.

Keywords: COVID-19; elderly patients; nomogram; severity.

Figure 1

Flowchart of the study.

Figure 2

Identification of significant predictors for the severity of COVID-19 in elderly patients. (A) LASSO coefficient profiles of the candidate predictors. (B) Selection of the optimal penalization coefficient in the LASSO regression. (C) Univariate and multivariate logistic regression of the predictors.

Figure 3

Construction and validation of the predictive nomogram for the severity of COVID-19 in elderly patients. (A) Development of the nomogram to predict the severity of COVID-19 in elderly patients. For example, if the albumin (ALB), d-dimer and onset to hospitalization (OH) time of an admitted elderly COVID-19 patient were 30 g/L, 1 μg/L and 15 days, respectively, the corresponding points for ALB, d-dimer and OH time were 57.5, 5 and 35, respectively. The total points value for this patient was 97.5, with a probability of 0.75 for developing severe or critical illness after admission. (B, E) Receiver operating characteristic (ROC) curves of the nomogram in the training cohort (B) and validation cohort (E). (C, F) Calibration curve of the nomogram in the training cohort (C) and validation cohort (F). (D, G) Decision curve analysis in the training cohort (D) and validation cohort (G). The y-axis represents net benefits, calculated by subtracting the relative harms (false positives) from the benefits (true positives). The x-axis measures the threshold probability.