Clinical Data-Driven AI Model for Mortality Prediction After Hip Fracture

Development of an AI Model Based on Clinical Data to Predict 30-Day and 1-Year Mortality Rates After Hip Fracture Surgery: A Retrospective Cohort Study

Hip fractures are a major cause of morbidity and mortality, particularly in elderly patients. Accurate prediction of postoperative mortality is critical for risk stratification and clinical decision-making. Traditional scoring systems, such as the Nottingham Hip Fracture Score, have limitations in capturing complex, non-linear relationships among clinical variables.

This retrospective cohort study aims to develop and validate an artificial intelligence-based model to predict 30-day mortality in patients undergoing hip fracture surgery. Clinical and laboratory data of approximately 1000 patients operated between January 1, 2022 and December 1, 2025 will be extracted from electronic health records. Variables include demographic characteristics, comorbidities, laboratory parameters, perioperative data, and postoperative complications.

The performance of the artificial intelligence model will be evaluated and compared with conventional risk scoring systems. The study seeks to determine whether AI-based approaches can provide improved predictive accuracy for postoperative mortality in hip fracture patients.

Inclusion Criteria:

Patients who underwent surgical treatment for hip fracture between January 1, 2022 and December 1, 2025 Age ≥18 years Availability of complete demographic, clinical, and laboratory data in the electronic health record system Documented 30-day follow-up or mortality status

Exclusion Criteria:

Patients with malignancy Patients undergoing revision hip surgery Patients with missing or incomplete key clinical or laboratory data required for analysis Patients with unavailable or undocumented 30-day mortality status