Key result
Adding frailty indices (mFI-5 or mFI-11) to machine-learning models did not significantly improve MINS prediction over baseline (AUC 0.5828 and 0.5881 vs 0.5744; p>0.5) or outperform RCRI.
Why the study?
Myocardial injury after non-cardiac surgery is a common complication in geriatric orthopedic patients, and the Revised Cardiac Risk Index may not adequately capture age-related loss of physiological reserve. Whether frailty indices improve machine-learning prediction of preoperative MINS risk compared with ASA and RCRI was investigated.
Do frailty indices (mFI-5, mFI-11) and perioperative variables improve machine-learning prediction of myocardial injury after non-cardiac surgery (MINS) compared to RCRI and ASA in patients aged ≥ 65 years undergoing major orthopedic surgery?
Observational (n=656)
No
Do frailty indices (mFI-5, mFI-11) and perioperative variables improve machine-learning prediction of myocardial injury after non-cardiac surgery (MINS) compared to RCRI and ASA in patients aged ≥ 65 years undergoing major orthopedic surgery?
In high-risk geriatric orthopedic patients, adding frailty indices (mFI-5, mFI-11) to preoperative machine-learning models did not significantly improve the prediction of myocardial injury after non-cardiac surgery compared to standard clinical risk indices like the RCRI.
MINS incidence in elderly orthopedic patients may warrant closer postoperative surveillance; retrospective data leaves open prospective validation of risk models.
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.
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KALAYCI et al. (2026) conducted an observational in Myocardial injury after non-cardiac surgery (MINS) in geriatric orthopedic patients (n=656). Machine-learning prediction models incorporating frailty indices (mFI-5, mFI-11) and perioperative factors vs. Preoperative baseline model, ASA, and Revised Cardiac Risk Index (RCRI) was evaluated on Prediction (AUC) of myocardial injury after non-cardiac surgery (MINS) (AUC 0.5828 (mFI-5) and 0.5881 (mFI-11) vs 0.5744 (baseline); Model 4 AUC 0.7584, p=>0.5). Adding frailty indices (mFI-5 or mFI-11) to machine-learning models did not significantly improve MINS prediction over baseline (AUC 0.5828 and 0.5881 vs 0.5744; p>0.5) or outperform RCRI.
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