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.

Myocardial injury after non-cardiac surgery (MINS) is defined as a troponin elevation occurring within the first 30 days following a surgical intervention, presumed to be caused by myocardial ischemia. Unlike the traditional diagnosis of myocardial infarction, MINS follows a "silent" course in more than 90% of cases, without ischemic symptoms or ECG changes. However, this silent progression is misleading; the 30-day postoperative mortality risk for patients who develop MINS is approximately 10 times higher than for those who do not. The geriatric orthopedic population, in particular, is in the highest risk group for this complication due to comorbidities and reduced physiological reserve. Currently, tools used in perioperative risk assessment, such as the Revised Cardiac Risk Index (RCRI) or ACS-NSQIP, focus primarily on chronic organ failures and remain insufficient in reflecting the dynamic physiological state of the geriatric patient. The low predictive success (AUC 0.54-0.62) of these scoring systems in the geriatric surgical group proves that clinicians require more precise tools for risk management.The Revised Cardiac Risk Index (RCRI), also known in the literature as the 'Lee Index,' is a widely used scoring system to predict perioperative major adverse cardiac events based on six clinical variables: high-risk surgery type, history of ischemic heart disease, congestive heart failure, history of cerebrovascular disease, preoperative insulin use, and a serum creatinine level above 2 mg/dL. However, RCRI focuses largely on the patient's existing chronic diagnoses; it does not account for the biological reserve loss that develops with aging, the depth of anemia, and specifically, the acute inflammatory response and fluid-electrolyte shifts triggered by orthopedic surgery. This situation significantly limits the sensitivity of RCRI in detecting silent myocardial injury (MINS) in the geriatric population. Given the high surgical urgency and stress in geriatric orthopedic patients, the early prediction of cardiovascular events has become a vital necessity.A review of the existing literature reveals that MINS prediction has focused either solely on clinical risk scores or on individual biomarkers (hs-cTnT, NT-proBNP). However, the concept of frailty, although it indicates the patient's biological reserve independent of chronological age, has not been sufficiently integrated into perioperative risk models. The combined effect of the "objective biological stress" data provided by biomarkers and the "physiological resilience" data provided by frailty indices has not yet been comprehensively modeled, specifically for orthopedic geriatrics. Traditional statistical methods struggle to capture the complex and non-linear relationships between these multidimensional data. There is a lack of a preoperative model in the literature where these variables are synthesized with machine learning algorithms.The primary objective of this study is to develop and validate a machine learning model that accurately predicts myocardial injury (MINS) following surgery in geriatric patients ($\ge$65 years) undergoing major orthopedic surgery and followed in the postoperative intensive care unit, by integrating only preoperative clinical data, biomarkers, and modified frailty indices. In addition to the primary aim of the research, the study intends to: compare the predictive performance of advanced machine learning models (XGBoost, Random Forest) with traditional clinical risk scores (Revised Cardiac Risk Index) used widely in the literature; reveal the impact of adding validated frailty indices (mFI-5) to patients' existing comorbidities on the model's predictive power (AUC); rank the preoperative variables with the highest predictive value in determining MINS risk in geriatric orthopedic patients; and provide a risk classification based on objective data to guide clinicians in the preoperative identification of high-risk patients.

Inclusion Criteria:

• All patients aged 65 years and older.
• Patients undergoing major orthopedic surgery (hip fracture repair, total knee/hip arthroplasty, and revision surgeries).
• Patients operated on within the designated study period (January 2021 - December 2023).
• Patients with complete access to preoperative clinical data (comorbidities, medication use) and baseline laboratory parameters (Hemoglobin, Creatinine, Albumin).
• Patients who had at least one postoperative cardiac troponin (hs-cTn) measurement within the first 72 hours after surgery.

Exclusion Criteria:

• Patients with a documented history of acute myocardial infarction or elevated baseline troponin levels in the preoperative period (to differentiate acute injury from surgical causes).
• Patients with end-stage renal disease (ESRD) requiring dialysis (as chronic kidney dysfunction persistently elevates baseline troponin levels).
• Patients with missing critical preoperative data or incomplete postoperative troponin follow-up.

The study population consists of geriatric patients (aged 65 years) undergoing major orthopedic surgery and requiring postoperative intensive care unit follow-up. Eligible participants must have at least one cardiac troponin level measured within the first 72 hours postoperatively. Patients on chronic dialysis due to end-stage renal disease and those with insufficient preoperative laboratory data will be excluded. The population is selected to represent high-risk geriatric patients in a tertiary training and research hospital setting

Individual participant data will not be shared to ensure patient confidentiality and to comply with institutional data protection policies. However, study results and the final analysis will be made available through peer-reviewed publication