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Multivariable risk prediction models for postoperative cardiac injury in adults undergoing non-cardiac surgery: a systematic review and meta-analysis protocol

  • 0Department of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Bmj Open +

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October 3, 2025

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October 3, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This systematic review evaluates prediction models for postoperative cardiac injury (myocardial infarction or MINS) after non-cardiac surgery. It aims to clarify model performance, calibration, and methodological rigor for improved patient outcomes.

Area Of Science

  • Cardiology
  • Perioperative Medicine
  • Health Services Research

Background

  • Postoperative cardiac injury, including myocardial infarction (MI) and myocardial injury after non-cardiac surgery (MINS), is a significant cause of morbidity and mortality.
  • Existing risk prediction models for these events lack clear comparative performance, calibration, and methodological rigor assessments.
  • This uncertainty hinders the optimal clinical application of these models.

Purpose Of The Study

  • To systematically review and meta-analyze multivariable risk prediction models for postoperative cardiac injury (MI/MINS) in adults undergoing non-cardiac surgery.
  • To comprehensively evaluate the performance, calibration, and methodological quality of these prediction models.
  • To identify factors influencing model performance and guide future model development.

Main Methods

  • A systematic review and meta-analysis following PRISMA-P guidelines, registered with PROSPERO.
  • Searches of multiple databases (PubMed, Embase, Web of Science, Cochrane, Scopus) and grey literature for relevant studies.
  • Independent data extraction and quality assessment using CHARMS and PROBAST tools, with meta-analysis of performance metrics.

Main Results

  • Performance metrics, including discrimination (AUC), calibration, and diagnostic accuracy, will be pooled and analyzed.
  • Heterogeneity will be explored via subgroup analyses and meta-regression based on model methodology, predictors, and validation contexts.
  • Sensitivity analyses will assess the robustness of the findings.

Conclusions

  • This systematic review will provide a rigorous evaluation of existing risk prediction models for postoperative cardiac injury.
  • Findings will clarify the comparative effectiveness and limitations of different modeling approaches.
  • The study aims to inform clinical practice and guide the development of more accurate and reliable prediction tools for perioperative cardiac risk.

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