A Novel Nomogram for prolonged length of stay in older patients with chronic heart failure

Background Older patients with Chronic heart failure (Chronic HF) are frequently in hospital due to recurrent episodes of disease. Prolonged length of stay (LOS) not only increase the risk of infection and reduce bed turnover for patients, but also increase the burden of healthcare costs and overall social costs. LOS for older patients with Chronic HF cannot be ignored. Objective This study aimed to develop and validate a predictive model for a prolonged LOS in hospitalized older patients with chronic heart failure (Chronic HF) in China. Methods We analyzed 264 Chinese older patients with Chronic HF. Patient demographics, comorbidities, and laboratory test results were collected upon admission. The outcome was defined as a LOS longer than the median. Independent risk factors for prolonged LOS were identified using univariate and multivariate logistic regression analyses. We validated and presented the model using bootstrap re-sampling in the form of a nomogram. Results The predictors included in the model were the New York Heart Association functional class (NYHA), type of admission, diuretic use during hospitalization, number of past hospitalizations in the last year, and cardiac troponin I (CTNI). The original model had a c statistic of 0.780 and a Brier score of 0.189. The DCA curve showed that it has achieved good clinical benefits within a certain range. After internal validation by bootstrap re-sampling, the model had a c statistic of 0.761 and a Brier score of 0.200. Conclusion The model presented in this study can better predict the risk of prolonged LOS in older patients with Chronic HF, providing healthcare professionals with a reference for treatment and intervention.