Clinical prediction model for acute exacerbation of chronic heart failure: Development and application of a nomogram

Acute exacerbation of chronic heart failure (CHF) is a major cause of recurrent hospitalization and mortality in heart failure patients. Accurate prediction of acute exacerbation of CHF risk is crucial for optimizing clinical management and improving patient outcomes. This study aimed to develop and validate a simple and practical nomogram model for predicting the risk of acute exacerbation in patients with CHF. This retrospective cohort study included 220 patients with CHF hospitalized in the Department of Cardiology of our institution between January 2019 and December 2023. The patients were randomly divided into a training cohort (n = 154) and a validation cohort (n = 66) at a 7:3 ratio using a random number table. Independent risk factors were identified through least absolute shrinkage and selection operator regression analysis, and a nomogram prediction model was constructed. The discriminative ability and calibration of the model were evaluated using receiver operating characteristic curves, calibration curves, and the Hosmer-Lemeshow goodness-of-fit test. During the 12-month follow-up period, 108 patients (49.09%) experienced acute exacerbation. least absolute shrinkage and selection operator regression analysis identified age, hypertension, left ventricular ejection fraction, coronary heart disease, and lymphocyte count as independent predictors of acute exacerbation of CHF. The nomogram model constructed based on these factors demonstrated area under the curve of 0.838 (95% confidence interval: 0.775-0.902) in the training cohort and 0.712 (95% confidence interval: 0.581-0.843) in the validation cohort. The calibration curves indicated strong agreement between predicted probabilities and actual observed incidence (Hosmer-Lemeshow test: P = .8651). This study successfully developed and validated a nomogram model based on readily available clinical indicators that can effectively predict the risk of acute exacerbation in patients with CHF. The model demonstrates good discriminative ability and calibration, aiding clinicians in individualized risk assessment and precise management.