Nomogram combining bedside ultrasonography and clinical data accurately predicts postoperative pulmonary complications in geriatric hip fracture.

Geriatric patients suffering from hip fractures face a high risk of postoperative pulmonary complications (PPCs), which significantly increase morbidity and mortality. Current methods for perioperative pulmonary risk stratification often fall short, leading to suboptimal patient management. This study addresses the critical need for improved predictive tools by exploring the utility of non-invasive bedside lung ultrasonography and diaphragmatic mobility assessments, combined with established clinical parameters, to identify high-risk individuals before surgery.

This observational study enrolled 786 geriatric patients with hip fractures undergoing surgery, dividing them into a training cohort (n=594) and a validation cohort (n=192). Researchers assessed lung ultrasonography score and diaphragmatic mobility at the bedside one day pre-surgery. All patients received general anesthesia combined with a regional block and were followed for 2 weeks postoperatively to monitor for PPCs. Independent predictors were identified via logistic regression, and a nomogram was constructed. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, and decision curve analysis.

Postoperative pulmonary complications (PPCs) occurred in 32.84% (246/749) of the patients. The developed nomogram incorporated nine independent variables: American Society of Anesthesiologists physical status class, functional dependence, chronic obstructive pulmonary disease, lung ultrasonography score, diaphragmatic mobility, recent respiratory infection, hypoalbuminemia, anemia, and elevated N-terminal fragment of the pro-brain natriuretic peptide level. The model demonstrated strong discriminatory power, good calibration, and favorable clinical utility. Notably, the nomogram significantly outperformed the existing ARISCAT score in the overall cohort.

The model achieved an area under the curve (AUC) of 0.897 (95% confidence interval, 0.870-0.924), indicating excellent predictive accuracy for PPCs.