What question did this study set out to answer?

The aim is to develop and validate a prediction model for postoperative pneumonia in elderly hip fracture patients using multiple risk factors.

June 8, 2026Risk Management and Healthcare Policy0 citationsOpen Access

Development and Validation of a Robust Prediction Model for Postoperative Pneumonia in Elderly Patients with Hip Fracture: Integrating Frailty, Nutrition, and Comprehensive Risk Assessment

LMLiping Ma JTJiahong Tu YFYan Fu

Key Points

The aim is to develop and validate a prediction model for postoperative pneumonia in elderly hip fracture patients using multiple risk factors.
Conducted a retrospective cohort study of 2183 patients aged ≥ 65 years undergoing hip fracture surgery.
Used multivariable logistic regression and machine learning models to identify predictors of postoperative pneumonia.
Evaluated model performance through area under the curve, calibration, and decision curve analysis.
Identified independent predictors including chronic obstructive pulmonary disease (OR=2.60) and lower nutritional risk index (OR=0.871).
Extended logistic regression model showed strong discrimination (AUC=0.781) and good calibration.
Machine learning models confirmed key predictors with comparable but miscalibrated performance (XGBoost corrected AUC=0.791).

Abstract

Objective: To develop and rigorously validate a multivariable prediction model for postoperative pneumonia (POP) in elderly patients with hip fracture by integrating the five-item modified frailty index (5‑mFI), the Geriatric Nutritional Risk Index (GNRI), and various clinical variables, with comprehensive assessment of model performance. Methods: We conducted a retrospective cohort study of 2183 patients aged ≥ 65 years undergoing hip fracture surgery. Predictors included comorbidities, laboratory values (including partial pressure of oxygen PO 2 , B-type natriuretic peptide BNP, and GNRI), and the 5-mFI. We employed multivariable logistic regression to develop original and extended models, the latter adjusting for functional status and perioperative factors. Model performance was evaluated via area under the curve (AUC), bootstrap-corrected AUC, calibration, and decision curve analysis. Time-to-event and competing risk analyses were performed, and machine learning models (Random Forest, XGBoost) were compared. Results: The extended logistic regression model identified chronic obstructive pulmonary disease (odds ratio OR=2.60), postoperative intensive care unit admission (OR=2.72), lower PO 2 (OR=0.987), lower GNRI (OR=0.871), higher 5-mFI (OR=1.94), and higher BNP (OR=1.000) as independent predictors. The model demonstrated robust discrimination (AUC=0.781; bootstrap-corrected AUC=0.773), good calibration, and clinical utility. Results were consistent in competing risk analysis and robust to multiple imputation of missing data. Machine learning models confirmed GNRI and 5-mFI as top predictors, with comparable yet miscalibrated performance (XGBoost corrected AUC=0.791). Conclusion: We developed and internally validated a robust prediction model for POP that integrates frailty, nutrition, and key clinical variables. The model demonstrates strong, validated performance and clinical utility, providing a practical tool for preoperative risk stratification to guide targeted preventive measures in elderly patients with hip fracture. Keywords: hip fracture, postoperative pneumonia, frailty, nutritional status, risk prediction model, machine learning

Ask AI

Helpful

Bookmark

View Full Paper