Construction and Validation of a Predictive Nomogram for Prolonged Hospitalization in Elderly Patients with Hip Fractures: A Retrospective Cohort Study Incorporating Modifiable and Non-Modifiable Risk Factors

Background: the global aging population has intensified the public health burden of hip fractures, characterized by high morbidity, mortality, and healthcare costs. Prolonged hospitalization (PLH) in elderly hip fracture patients is associated with adverse outcomes, yet existing predictive models often overlook the multifactorial interplay of clinical, surgical, and laboratory variables. This study aimed to develop and validate a nomogram integrating modifiable and non-modifiable risk factors to predict PLOS and optimize clinical decision-making. Methods: a retrospective observational cohort of 1,674 elderly hip fracture patients (2012-2025) from the People’s Hospital of Lichuan City was analyzed. Patients were categorized into Non-PLH (≤12.5 days) and PLH (>12.5 days) groups based on the 75th percentile of hospitalization duration. Variables spanning demographics, fracture characteristics, perioperative management, and comorbidities were collected. Multivariable logistic regression identified independent predictors, and a nomogram was developed using R software. Model performance was assessed via AUC, calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Results: five independent predictors were identified: time from admission to surgery >48 hours (OR=16.62), hemiplegia (OR=3.48), heart failure (OR=2.66), lower extremity vascular disease (OR=2.31), and preoperative hemoglobin (protective, OR=0.96). The nomogram demonstrated robust discrimination (training AUC: 0.89, 95% CI: 0.87–0.91; testing AUC: 0.87, 95% CI: 0.83-0.91) and calibration (Brier scores: 0.18-0.21). DCA revealed significant clinical utility across thresholds of 0-97%, with a maximum net benefit of 0.25. Conclusion: this study presents a validated nomogram for predicting PLH in elderly hip fracture patients, integrating both modifiable and non-modifiable risk factors. The model’s high accuracy, interpretable risk stratification, and actionable thresholds enhance personalized resource allocation and preoperative optimization, advancing precision medicine in geriatric orthopedics.