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

NCT07495527 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2026-03-27

No results posted yet for this study

Summary

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.

Conditions

  • Hip Fracture , Postoperative Mortality

Sponsors & Collaborators

  • Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-12-01
Primary Completion
2026-03-10
Completion
2026-03-20

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07495527 on ClinicalTrials.gov