PREDICTING MINS WITH FRAILTY AND BIOMARKERS IN GERIATRIC SURGERY

NCT07566013 | Active Not Recruiting

• 1 other identifier: 2026-04/85
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Geriatric Patients; Postoperative Complications; Frailty; Myocardial Ischemia; Hip Surgeries

Keywords

Machine Learning; XGBoost; Random Forest; Predictive Model; Modified Frailty Index (mFI-5); High-Risk Patient; Perioperative Medicine; MINS

Outcome Measures

Primary Outcomes (1)

• Incidence of Myocardial Injury after Non-cardiac Surgery (MINS)

  The area under the receiver operating characteristic curve (AUC-ROC) ,Percentage of participants)

  30 days postoperatively

Secondary Outcomes (3)

• Comparison of Machine Learning Models vs. Traditional Risk Scores (RCRI).

  Up to 30 days post-surgery

• Identification and ranking of the most significant preoperative predictors for MINS.

  Through study completion, an average of 6 months

• Identification and ranking of the most significant preoperative predictors for MINS

  Through study completion, an average of 1 year

Study Arms (1)

Geriatric Orthopedic Surgery Patients

Geriatric patients aged 65 years and older who undergo major orthopedic surgery and are followed in the postoperative intensive care unit. This cohort includes patients evaluated for myocardial injury after non-cardiac surgery (MINS) using preoperative clinical data, biomarkers, and frailty indices.

Other: Preoperative Risk Assessment and Machine Learning Modeling

Interventions

Preoperative Risk Assessment and Machine Learning Modeling OTHER

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.

Geriatric Orthopedic Surgery Patients

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• DİLEK KALAYCI: lead

Study Sites (1)

Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital

Ankara, Ankara, 06630, Turkey (Türkiye)

Location

Related Links

• Reference study for the machine learning methodology and predictive modeling framework used in this research
• Key prospective study evaluating the association between frailty levels and MINS incidence in geriatric orthopedic patients

MeSH Terms

Conditions

Postoperative Complications; Frailty; Myocardial Ischemia

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms; Heart Diseases; Cardiovascular Diseases; Vascular Diseases

Study Officials

• Dilek Kalaycı

  Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Anesthesiology and Reanimation Specialist

Study Record Dates

• First Submitted: April 23, 2026
• First Posted: May 4, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 5, 2026
• Last Updated: May 4, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)