External validation of the Hip Fracture Prognosis Tool to predict the probability of gait recovery after hip fracture
摘要
The Hip Fracture Prognosis (HF-Prognosis) Tool was developed using data from the Spanish National Hip Fracture Registry (SNHFR) to predict 30-day recovery of ambulation. Our study’s aim was to externally validate the model in a later cohort (2021–2022) to ensure its continued predictive accuracy and clinical utility.
MethodsData were obtained from the SNHFR, a multicenter, prospective registry. Patients aged ≥ 75 years admitted for fragility hip fracture with information on pre-fracture ambulation were included. The primary outcome was recovery of pre-fracture walking ability at 1 month. Predictors included age, pre-fracture ambulation, cognitive impairment, anesthetic risk, fracture type, surgical delay, early mobilization, weight-bearing authorization, pressure ulcers, and discharge destination. Logistic regression-based predictions were compared with observed outcomes. Model performance metrics (accuracy, precision, recall, specificity, F1 score) and discrimination with the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC), estimated via bootstrap resampling were evaluated.
Results13,824 patients were included (age range 75–109 years, 77.2% female). Baseline characteristics were generally similar to the development cohort, though some variables showed higher missing data in the validation cohort. Consistent with the original cohort, 30-day recovery of ambulation was 67.9%. The model demonstrated stable performance (AUC 0.717, 95% CI 0.708–0.726), and predicted probabilities closely matched observed outcomes.
ConclusionsThe HF-Prognosis Tool shows moderate predictive validity for 30-day recovery of ambulation after hip fracture using routinely collected variables. It is freely accessible online, providing clinicians with an evidence-based tool to inform clinical decision-making and discharge planning. Further validation in other national audits is recommended to confirm generalizability.