PREDICTING MINS WITH FRAILTY AND BIOMARKERS IN GERIATRIC SURGERY

The primary objective of this study is to develop and validate a machine learning model that integrates preoperative clinical data, biomarkers, and modified frailty indices (mFI-5) to accurately predict myocardial injury after non-cardiac surgery (MINS) in geriatric patients ($\\ge$65 years) undergoing major orthopedic surgery and requiring postoperative intensive care. The research aims to compare the predictive performance of advanced algorithms, such as XGBoost and Random Forest, against traditional clinical risk scores like the Revised Cardiac Risk Index (RCRI), while specifically evaluating the impact of frailty on the model's area under the curve (AUC). Furthermore, by identifying the most critical preoperative predictors, this study seeks to establish an objective clinical decision support mechanism to guide clinicians in the early risk stratification of high-risk geriatric patients.

NCT Number	NCT07566013
Lead Sponsor	DİLEK KALAYCI
Conditions	Geriatric Patients, Postoperative Complications, Frailty, Myocardial Ischemia, Hip Surgeries
Enrollment	600 participants
Start Date	2026-04-01
Primary Completion	2026-06-01 (estimated)
Study Completion	2026-06-05 (estimated)
Updated on ClinicalTrials.gov	2026-05-04