Frailty Indices and Preoperative Clinical Factors for Predicting Myocardial Injury After Non-Cardiac Surgery (MINS) in High-Risk Geriatric Orthopedic Patients: A Machine-Learning Study
4 affiliations
- 1Department of Anesthesiology and Reanimation, Dr. Abdurrahman Yurtarslan Ankara Oncology Training and Research Hospital, University of Health Sciences, 06200 Ankara, Turkey
- 2Department of Cardiology, Dr. Abdurrahman Yurtarslan Ankara Oncology Training and Research Hospital, University of Health Sciences, 06200 Ankara, Turkey
- 3Department of Anesthesiology and Reanimation, Republic of Turkey Ministry of Health Muğla Training and Research Hospital, 48000 Muğla, Turkey
- 4Department of Orthopedics and Traumatology, Dr. Abdurrahman Yurtarslan Ankara Oncology Training and Research Hospital, University of Health Sciences, 06200 Ankara, Turkey
Abstract
Background/Objectives: Myocardial injury after non-cardiac surgery (MINS) is a common, often asymptomatic complication in geriatric orthopedic patients that increases postoperative mortality. The Revised Cardiac Risk Index (RCRI) may not adequately capture the age-related loss of the physiological reserve. We investigated whether frailty indices (mFI-5, mFI-11) improve machine-learning prediction of preoperative MINS risk, and compared an exploratory perioperative model with ASA and RCRI. Methods: In this single-center retrospective study, 656 patients aged ≥ 65 years who underwent major orthopedic surgery with postoperative ICU monitoring and troponin surveillance were analyzed; MINS occurred in 82 (12.5%). The models were as follows: Model 1 (preoperative baseline), Model 2 (+mFI-5), Model 3 (+mFI-11), and an exploratory perioperative model, Model 4 (+intraoperative vasopressor use and postoperative mechanical ventilation). AUCs on a 30% hold-out set were compared with ASA/RCRI using the paired DeLong test, with Bonferroni adjustment across 15 comparisons. A sensitivity analysis with an identical classifier and predictor set isolated the contribution of the frailty indices. Results: Frailty indices did not provide significant incremental value: adding mFI-5 or mFI-11 changed the AUC only marginally (0.5744 to 0.5828 and 0.5881; all p > 0.5). Preoperative models (hold-out AUC 0.6005–0.6935) did not differ significantly from ASA or RCRI after Bonferroni correction. Model 4, incorporating intra- and postoperative variables, reached an AUC of 0.7584, significantly higher than ASA (adjusted p = 0.002) but not RCRI (adjusted p = 0.268). Conclusions: With the algorithm and predictor set held constant, no incremental value of frailty indices was demonstrable, and no model significantly outperformed RCRI after multiplicity correction. The numerically higher AUC of Model 3 versus Model 2 cannot be attributed to frailty indices, as the models differed in both predictors and algorithm. Power was limited; these findings are hypothesis-generating. Whether frailty indices improve prediction needs to be tested in external cohorts large enough to detect it.
Related
- Performance and incremental value of preoperative multimodal imaging and perioperative clinical characteristics in predicting postoperative major adverse events in type A aortic dissection
- Evaluation of Clinical SYNTAX Score in Predicting New-Onset Atrial Fibrillation and Long-Term Prognosis in Patients Hospitalized with Non-ST Segment Elevation Myocardial Infarction
- Association of preoperative systemic immune-inflammation index and geriatric nutritional risk index with prolonged postoperative length of stay in older patients with hip fracture : A retrospective cohort study
- Development and validation of a machine learning-based prediction model for emergence agitation in geriatric patients undergoing orthopedic surgery: a prospective cohort study.
- Habitat radiomics combined with clinical features for predicting tertiary lymphoid structure status in pancreatic ductal adenocarcinoma: development and validation of a preoperative CECT-based model.
BibTeX
@article{kalaycı2026frailty,
title = {{Frailty Indices and Preoperative Clinical Factors for Predicting Myocardial Injury After Non-Cardiac Surgery (MINS) in High-Risk Geriatric Orthopedic Patients: A Machine-Learning Study}},
author = {Dilek Kalaycı and Edanur Genç Turan and İmran Ceren and Fadime Bozduman Habip and Özlem Şen and Tuğba Aşkın and Çoşkun Ulucaköy},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15218285},
url = {https://www.mdpi.com/2077-0383/15/21/8285},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Frailty Indices and Preoperative Clinical Factors for Predicting Myocardial Injury After Non-Cardiac Surgery (MINS) in High-Risk Geriatric Orthopedic Patients: A Machine-Learning Study
AU - Dilek Kalaycı
AU - Edanur Genç Turan
AU - İmran Ceren
AU - Fadime Bozduman Habip
AU - Özlem Şen
AU - Tuğba Aşkın
AU - Çoşkun Ulucaköy
PY - 2026
DA - 2026/10/24
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218285
UR - https://www.mdpi.com/2077-0383/15/21/8285
AB - Background/Objectives: Myocardial injury after non-cardiac surgery (MINS) is a common, often asymptomatic complication in geriatric orthopedic patients that increases postoperative mortality. The Revised Cardiac Risk Index (RCRI) may not adequately capture the age-related loss of the physiological reserve. We investigated whether frailty indices (mFI-5, mFI-11) improve machine-learning prediction of preoperative MINS risk, and compared an exploratory perioperative model with ASA and RCRI. Methods: In this single-center retrospective study, 656 patients aged ≥ 65 years who underwent major orthopedic surgery with postoperative ICU monitoring and troponin surveillance were analyzed; MINS occurred in 82 (12.5%). The models were as follows: Model 1 (preoperative baseline), Model 2 (+mFI-5), Model 3 (+mFI-11), and an exploratory perioperative model, Model 4 (+intraoperative vasopressor use and postoperative mechanical ventilation). AUCs on a 30% hold-out set were compared with ASA/RCRI using the paired DeLong test, with Bonferroni adjustment across 15 comparisons. A sensitivity analysis with an identical classifier and predictor set isolated the contribution of the frailty indices. Results: Frailty indices did not provide significant incremental value: adding mFI-5 or mFI-11 changed the AUC only marginally (0.5744 to 0.5828 and 0.5881; all p > 0.5). Preoperative models (hold-out AUC 0.6005–0.6935) did not differ significantly from ASA or RCRI after Bonferroni correction. Model 4, incorporating intra- and postoperative variables, reached an AUC of 0.7584, significantly higher than ASA (adjusted p = 0.002) but not RCRI (adjusted p = 0.268). Conclusions: With the algorithm and predictor set held constant, no incremental value of frailty indices was demonstrable, and no model significantly outperformed RCRI after multiplicity correction. The numerically higher AUC of Model 3 versus Model 2 cannot be attributed to frailty indices, as the models differed in both predictors and algorithm. Power was limited; these findings are hypothesis-generating. Whether frailty indices improve prediction needs to be tested in external cohorts large enough to detect it.
SN - 2077-0383
ER -