Heliyon (May 2024)
Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19
Abstract
Objective: Our aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19). Methods: Clinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort. Result: In total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027–2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253–2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042–1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707–3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003–1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001–1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440–0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641–0.923) and 0.814 (95 % CI: 0.702–0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765–0.962) and 0.875 (95 % CI: 0.741–0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value. Conclusion: In-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.
