Key result
This protocol outlines a planned systematic review and meta-analysis to evaluate the discriminative performance and calibration of multivariable risk prediction models for postoperative cardiac injury.
Why the study?
While numerous prediction models exist for postoperative cardiac injury, their comparative performance, calibration quality, and methodological rigour remain unclear.
Systematic Review
This protocol outlines a planned systematic review and meta-analysis to evaluate the performance and methodological rigor of multivariable risk prediction models for postoperative cardiac injury in adults undergoing non-cardiac surgery.
Planned review of cardiac risk models after non-cardiac surgery; will clarify performance for perioperative decisions and guide future model research.
INTRODUCTION: Postoperative cardiac injury, encompassing myocardial infarction (MI) and myocardial injury after non-cardiac surgery (MINS), is a major perioperative complication associated with substantial morbidity and mortality. While numerous prediction models have been developed using traditional statistical and machine learning approaches, their comparative performance, calibration quality and methodological rigour remain unclear. This protocol outlines a systematic review and meta-analysis to comprehensively evaluate multivariable risk prediction models for postoperative cardiac injury in adults undergoing non-cardiac surgery. METHODS AND ANALYSIS: Following Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines, this PROSPERO-registered systematic review will search PubMed, Embase, Web of Science, Cochrane Library, Scopus, grey literature and trial registries for studies developing, validating or updating multivariable prediction models for postoperative cardiac injury (MI or MINS) occurring within 72 hours of non-cardiac surgery. Two reviewers will independently extract data and assess quality using CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) and PROBAST (Prediction Model Risk of Bias Assessment Tool) tools. The primary outcome is discriminative performance (area under the receiver operating characteristic curve), with calibration metrics and diagnostic accuracy measures as secondary outcomes. Random-effects meta-analyses will pool performance estimates for models validated in multiple cohorts. Heterogeneity will be explored through subgroup analyses and meta-regression, examining factors including model methodology (regression-based vs machine learning), predictor types and validation contexts. Sensitivity analyses will test the robustness of findings. ETHICS AND DISSEMINATION: Ethical approval is not required for this study, as it is a systematic review and meta-analysis based on previously published data. PROSPERO REGISTRATION NUMBER: CRD420251041628.
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Zhang et al. (2025) conducted a systematic review in Postoperative cardiac injury (MI or MINS) after non-cardiac surgery. Multivariable risk prediction models was evaluated on Discriminative performance (area under the receiver operating characteristic curve). This protocol outlines a planned systematic review and meta-analysis to evaluate the discriminative performance and calibration of multivariable risk prediction models for postoperative cardiac injury.
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