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

This study population consists of adult patients who underwent surgical treatment for hip fractures at a tertiary care oncology training and research hospital between January 1, 2022 and December 1, 2025. The cohort is derived retrospectively from electronic health records and includes approximately 1000 patients.

The population includes individuals with varying demographic characteristics, comorbidities, and perioperative risk profiles. Clinical data encompass demographic information, baseline functional and cognitive status, comorbid conditions, laboratory parameters, intraoperative variables, and postoperative complications.

Patients with malignancy and those undergoing revision surgery are excluded. The study population represents a real-world clinical cohort used to develop and validate an artificial intelligence-based model for predicting 30-day postoperative mortality following hip fracture surgery.

Individual participant data will not be shared due to the retrospective nature of the study and the inclusion of sensitive patient information obtained from electronic health records. All data will be analyzed in an anonymized form, and data sharing is restricted in accordance with institutional policies and ethical regulations