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PREDICTING MINS WITH FRAILTY AND BIOMARKERS IN GERIATRIC SURGERY
Sponsor: DİLEK KALAYCI
Summary
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.
Official title: THE ROLE OF FRAILTY INDICES AND PREOPERATIVE BIOMARKERS IN PREDICTING MYOCARDIAL INJURY AFTER NON-CARDIAC SURGERY IN ELDERLY ORTHOPEDIC PATIENTS: A MACHINE LEARNING ANALYSIS
Key Details
Gender
All
Age Range
65 Years - Any
Study Type
OBSERVATIONAL
Enrollment
600
Start Date
2026-04-01
Completion Date
2026-06-05
Last Updated
2026-05-04
Healthy Volunteers
No
Interventions
Preoperative Risk Assessment and Machine Learning Modeling
Standard clinical care for major orthopedic surgery including preoperative assessment of biomarkers (hs-cTnT, NT-proBNP), frailty screening (mFI-5), and clinical data collection for the development of a machine learning-based MINS prediction model.
Locations (1)
Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital
Ankara, Ankara, Turkey (Türkiye)