Evaluation of short- and long-term mortality prediction in patients undergoing hip fracture surgery using a...

The aim of this study was to develop an interpretable predictive modeling approach to predict short-term (three-month) and long-term (one-year) mortality in patients undergoing hip fracture surgery, and to evaluate the prognostic value of routinely available clinical and laboratory variables, including age, sex, lactate-to-albumin ratio (LAR), red cell distribution width-standard deviation (RDW-SD), and neutrophil-to-lymphocyte ratio (NLR).

This retrospective cohort study included 1,000 patients who underwent hip fracture surgery. Demographic characteristics and preoperative laboratory parameters (LAR, RDW-SD, and NLR) were recorded. Three-month and one-year mortality were defined as the primary outcomes. Independent predictors of mortality were assessed using a multivariable logistic regression model implemented within an interpretable predictive modeling framework. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision-recall AUC (PR-AUC), and Brier score. Calibration was assessed using calibration plots, and optimal probability thresholds were determined using the Youden J index.

Among the included patients, 17.4% died within three months and 24.6% within one year following surgery. Non-survivors were older and had higher LAR, RDW-SD, and NLR values compared with survivors (p < 0.001). In multivariable analyses, advanced age and elevated NLR were identified as independent predictors of both short-term and long-term mortality (p < 0.001). LAR was independently associated with one-year mortality (odds ratio: 1.035; 95% confidence interval: 1.008-1.063; p = 0.010). The model demonstrated acceptable discriminative performance, with an AUC of 0.807 for three-month mortality and 0.810 for one-year mortality. At the optimal thresholds, sensitivity and specificity were 80% and 73% for three-month mortality, and 77% and 71% for one-year mortality, respectively.

The biomarker-based interpretable predictive modeling approach developed in this study demonstrated acceptable discriminative performance for estimating short- and long-term mortality after hip fracture surgery. Advanced age, elevated NLR, and increased LAR emerged as important predictors. As these variables are routinely available in clinical practice, the proposed framework may support perioperative risk awareness and assist clinical evaluation when interpreted together with comprehensive clinical assessment.

Not applicable.