PREDICTING MINS WITH FRAILTY AND BIOMARKERS IN GERIATRIC SURGERY

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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.

Eligibility

Primary outcome measure(s)

• Incidence of Myocardial Injury after Non-cardiac Surgery (MINS) — 30 days postoperatively
  The area under the receiver operating characteristic curve (AUC-ROC) ,Percentage of participants)

Trial sites (1)

Other trials for the same condition