Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.

backgroundPostoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to prolonged hospitalization, increased costs, and increased mortality. However, simple and reliable preoperative predictors remain limited. Therefore, this study aimed to develop and validate a hematology-based machine learning model for the early prediction of PPI in older hip fracture patients.

methodsA total of 3,944 patients aged ≥ 60 years who underwent hip fracture surgery were retrospectively enrolled from three cohorts: the discovery cohort (n = 1,745, Shanghai Xuhui Central Hospital, 2016-2020), the internal validation cohort (n = 1,306, 2021-2024), and the external validation cohort (n = 893, Shanghai Putuo People's Hospital, 2016-2024). Twenty-four preoperative hematologic variables were analyzed. Six supervised machine learning algorithms were compared via fivefold cross-validation. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration, and decision curve analysis (DCA).

resultsPatients who developed PPI were generally older and exhibited a neutrophil-dominant inflammatory profile, characterized by higher white blood cell counts, neutrophil, monocyte, platelet, and C-reactive protein levels, and lower lymphocyte, eosinophil, and basophil percentages (all p < 0.001). Among the evaluated algorithms, the extreme gradient boosting (XGBoost) model achieved the best overall performance, with AUCs of 1.00, 0.96, and 0.98 in the discovery, internal, and external cohorts, respectively. Calibration curves suggested good agreement between predicted and observed probabilities, and DCA indicated favorable clinical net benefit across threshold probabilities.

conclusionsA hematology-based XGBoost model was developed to predict in-hospital PPI in older adults following hip fracture surgery. The model demonstrated good discriminative performance and interpretability in this study cohort, suggesting its potential utility as a supplementary tool for cost-effective perioperative risk stratification. However, further prospective validation in diverse populations and healthcare settings is required to confirm its generalizability and clinical applicability.