Aim: This study aims to explore the risk factors for perioperative acute heart failure in older patients with hip fracture and establish a nomogram prediction model.

Methods: The present study was a retrospective study. From January 2020 to December 2021, patients who underwent surgical treatment for hip fracture at the Third Hospital of Hebei Medical University were included. Heart failure was confirmed by discharge diagnosis or medical records. The samples were randomly divided into modeling and validation cohorts in a ratio of 7:3. Relevant demographic and clinic data of patients were collected. IBM SPSS Statistics 26.0 performed univariate and multivariate logistic regression analysis, to obtain the risk factors of acute heart failure. The R software was used to construct the nomogram prediction model.

Results: A total of 751 older patients with hip fracture were enrolled in this study, of which 138 patients (18.37%, 138/751) developed acute heart failure. Heart failure was confirmed by discharge diagnosis or medical records. Respiratory disease (odd ratio 7.68; 95% confidence interval 3.82-15.43; value of P 0.001), history of heart disease (chronic heart failure excluded) (odd ratio 2.21, 95% confidence interval 1.18-4.12; value of P 0.010), ASA ≥ 3 (odd ratio 14.46, 95% confidence interval 7.78-26.87; value of P 0.001), and preoperative waiting time ≤ 2 days (odd ratio 3.32, 95% confidence interval 1.33-8.30; value of P 0.010) were independent risk factors of perioperative acute heart failure in older patients with hip fracture. The area under the curve (AUC) of the prediction model based on these factors was calculated to be 0.877 (95% confidence interval 0.836-0.918). The sensitivity and specificity were 82.8% and 80.9%, respectively, and the fitting degree of the model was good. In the internal validation group, the AUC was 0.910, and the 95% confidence interval was 0.869-0.950.

Conclusions: Several risk factors are identified for acute heart failure in older patients, based on which pragmatic nomogram prediction model is developed, facilitating detection of patients at risk early.