Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• European review for medical and pharmacological sciences, 2023-12, Vol.27 (23), p.11517

Ort / Verlag

Italy

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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. 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. 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.