Risk prediction models for cardiac rupture after acute myocardial infarction: a systematic review and meta-analysis
Yijun Mao1, Qiang Liu2, Hui Fan1
1Department of Nursing, Xianyang Central Hospital, Xianyang, Shaanxi, China.
Insights
Cardiac rupture after acute myocardial infarction is a serious risk. Existing prediction models show promise but have flaws. Robust predictors like age and sex should guide future risk stratification tools.
Area of Science:
- Cardiovascular Medicine
- Medical Statistics
- Clinical Epidemiology
Background:
- Cardiac rupture (CR) is a severe complication of acute myocardial infarction (AMI), contributing significantly to mortality.
- Existing risk prediction models for CR post-AMI have uncertain methodological quality and clinical applicability.
Purpose of the Study:
- To systematically review and quantitatively synthesize existing risk prediction models for CR following AMI.
- To assess the methodological quality and clinical applicability of current CR prediction models.
Main Methods:
- Comprehensive literature search of multiple databases (PubMed, Embase, Web of Science, etc.) up to August 2025.
- Data extraction using CHARMS checklist and methodological quality assessment with PROBAST.
- Meta-analysis of model discrimination (C-statistic) and predictor evaluation, with subgroup analyses for heterogeneity.
Main Results:
- Ten studies (2017-2024) were included, with nine suitable for meta-analysis.
- The pooled C-statistic for CR prediction models was 0.83 (95% CI: 0.78-0.89), indicating good discriminatory ability but high heterogeneity.
- Robust predictors identified include advanced age, female sex, higher Killip grade, elevated heart rate, lower LVEF, and absence of emergency PCI. Methodological flaws were common.
Conclusions:
- Current CR prediction models show promising discriminatory power but suffer from significant methodological limitations.
- Key predictors such as age, sex, Killip grade, LVEF, and PCI status should inform the development of future consensus-based models.
- There is an urgent need for large-scale, prospective, and externally validated studies to create reliable clinical risk stratification tools for CR prevention.
Background:
Cardiac rupture (CR) is a catastrophic complication of acute myocardial infarction (AMI), accounting for 10%-20% of AMI-related deaths despite its low incidence. Several risk prediction models have been developed, but their methodological quality and clinical applicability remain uncertain. This study systematically reviewed and quantitatively synthesized existing prediction models for CR post-AMI.
Methods:
We searched PubMed, Embase, Web of Science, Cochrane Library, CNKI, and Wanfang databases from inception to August 2025. Studies developing or validating risk prediction models for CR after AMI were eligible. Data extraction followed the CHARMS checklist, and methodological quality was assessed with PROBAST. A meta-analysis was performed to pool model discrimination (C-statistic) and evaluate predictors of CR. Subgroup analyses explored heterogeneity by publication period, study design, population, sample size, and validation approach.
Results:
Ten studies (2017-2024) involving 74-11,603 patients were included. Among them, nine studies reported C-statistics (AUC) along with their confidence intervals (CIs), which were suitable for quantitative synthesis (Meta-analysis). The pooled C-statistic of CR prediction models was 0.83 (95% CI: 0.78-0.89), though with high heterogeneity (I 2 = 88%). Consistently robust predictors included advanced age (OR = 2.26), female sex (OR = 2.43), higher Killip grade (OR = 3.58), elevated heart rate (OR = 2.29), lower LVEF (OR = 1.46), and absence of emergency PCI (OR = 0.37, protective). Most studies exhibited methodological flaws, including small events-per-variable ratios, univariate-based predictor selection, inadequate handling of missing data, and limited external validation.
Conclusion:
Existing models demonstrate promising discriminatory ability for predicting CR after AMI but are undermined by substantial methodological limitations. Age, sex, Killip grade, LVEF, and PCI status represent robust predictors that should inform future consensus-based models. Large-scale, prospective, and externally validated studies are urgently needed to develop reliable tools for clinical risk stratification and targeted prevention of this lethal complication.
Systematic Review Registration:
https://www.crd.york.ac.uk/PROSPERO/view/CRD420251105703, PROSPERO CRD420251105703.
