Abstract
BACKGROUND: Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis. OBJECTIVE: This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model. METHODS: This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model's calibration. RESULTS: Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381-0.734, P < 0.001), Age (OR = 1.760, 95% CI: 1.251-2.479, P = 0.001), Coronary Heart Disease (OR = 1.977, 95% CI: 1.454-2.687, P < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95% CI: 1.154-5.346, P = 0.020), Complications (OR = 1.516, 95% CI: 1.033-2.226, P = 0.033), Anemia (OR = 2.668, 95% CI: 1.850-3.847, P < 0.001), and Hypoalbuminemia (OR 2.442, 95% CI: 1.682-3.544, P < 0.001). The linear prediction model of acute heart failure was Logit(P) = -2.167-0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia, and the nomogram prediction model was established. The AUC of the predictive model was 0.763, indicating good predictive performance. Decision curve analysis revealed that the prediction model offers the greatest net benefit when the threshold probability ranges from 4 to 62%. CONCLUSION: The prediction model we developed exhibits excellent accuracy in predicting the onset of acute heart failure preoperatively in elderly patients with hip fractures. It could potentially serve as an effective and useful clinical tool for physicians in conducting clinical assessments and individualized treatments.