Diagnostic Models to Differentiate Takotsubo Syndrome from Acute Coronary Syndrome: A Systematic Review and Meta-Analysis
Authors' conclusion
Our review identified multiple diagnostic models to diagnose Takotsubo syndrome. Although most models showed acceptable-to-good discriminative performance, calibration measures were almost unreported and the risk of bias was a concern in most studies.
Study context
- Population
- Patients with Takotsubo syndrome or acute coronary syndrome (ACS); observational studies developing and/or validating multivariable diagnostic models
- Participants
- 4,552 patients11 studies
- Comparison
- Multivariable diagnostic models to differentiate Takotsubo syndrome from ACS
- InterTAKn = 4
- Outcome
- Diagnostic performance of models (c-statistic, calibration) distinguishing Takotsubo syndrome from ACS
Methodological detailsrandom-effects meta-analysis, PROBAST risk-of-bias assessment, narrative synthesis, Search of PubMed, EMBASE and Scopus until January 2024; 1015 articles screened
- Random-effects meta-analysis
- PROBAST risk-of-bias assessment
- Narrative synthesis
- Search of PubMed, EMBASE and Scopus until January 2024; 1015 articles screened
Pooled result
As reported in the abstract
Discrimination (c-statistic)
| Group | Summary c-statistic | Studies |
|---|---|---|
| InterTAK | 0.89 (95% CI 0.73-0.96) | 4 |
InterTAK
Summary c-statistic0.89 (95% CI 0.73-0.96)
Studies4
- Reported c-statistic range across all models
- 0.77 to 0.97
- New diagnostic models identified
- 8
- External validations of existing models
- 8
- Models with calibration plots reported
- 2
Further pooled results
Risk of bias and applicability (PROBAST)
- Applicability of low concern
- 50%
- Applicability of unclear concern
- 50%
- Risk of bias high for all models
Original publication
Diagnostic Models to Differentiate Takotsubo Syndrome from Acute Coronary Syndrome: A Systematic Review and Meta-Analysis. Eur J Heart Fail. Published 14 January 2025. doi:10.
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