Development and validation of a nomogram for 30-day readmission after hip fracture surgery in geriatric patients

W.-Y. Tang, W. Yao, W. Wang, Q.-M. Lv, W.-B. Ding, R.-J. He

Department of Orthopedics, Dandong Central Hospital, China Medical University, Dandong, China. dwb98003007@163.com

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• Geriatrics
• Orthopedics
• Surgery

OBJECTIVE: 30-day readmission after hip fracture surgery in the elderly is common and costly. A predictive tool to identify high-risk patients could significantly improve outcomes. This study aims to develop and validate a risk nomogram for 30-day readmission after hip fracture surgery in geriatric patients.

PATIENTS AND METHODS: We retrospectively analyzed 1,249 geriatric hip fracture patients (≥60 years) undergoing surgery at Dandong Central Hospital from October 2011 to October 2023. Using a 7:3 ratio, patients were randomly divided into training (n=877) and validation (n=372) sets. Independent risk factors for 30-day readmission were identified using LASSO regression and logistic regression in the training set. A nomogram was constructed using the identified predictors. Finally, the C-index, ROC curve, calibration curve, and decision curve analysis were used to validate the model in the training and validation sets respectively.

RESULTS: The nomogram was developed based on the 8 predictors of age, prior stroke, chronic liver disease, treatment, uric acid (UA), total protein (TP), albumin (ALB), and pneumonia that were found to be independently associated with 30-day readmission. The nomogram showed good discrimination with a C-index of 0.88 in the training set and 0.84 in the validation set. Calibration curves exhibited good agreement between predicted and observed outcomes. Decision curve analysis demonstrated clinical utility.

CONCLUSIONS: We developed and validated a nomogram incorporating eight clinical variables to accurately predict the individualized risk of 30-day readmission after hip fracture surgery in elderly patients. The model demonstrated favorable discrimination, calibration, and clinical utility. It can help to identify high-risk patients needing additional interventions to prevent avoidable hospital readmissions.

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