Key result
InterTAK model differentiates Takotsubo syndrome from ACS with a 0.89 c-statistic.
Why the study?
Differentiation between Takotsubo syndrome and acute coronary syndrome remains a clinical challenge, and diagnostic predictive models require evaluation.
Do multivariable diagnostic models accurately differentiate Takotsubo syndrome from acute coronary syndrome?
Meta-Analysis (n=4,552)
Do multivariable diagnostic models accurately differentiate Takotsubo syndrome from acute coronary syndrome?
Effect estimate: c-statistic 0.89 (95% CI 0.73-0.96)
Diagnostic models like InterTAK show acceptable-to-good discriminative performance for differentiating Takotsubo syndrome from ACS, but are limited by high risk of bias and lack of calibration reporting.
Models discriminate Takotsubo from ACS acceptably yet carry high bias and unreported calibration; leaves routine adoption uncertain pending better validation.
AIMS: Differentiation between patients with Takotsubo syndrome and acute coronary syndrome (ACS) remains a challenge. We performed a systematic review to identify and evaluate diagnostic predictive models to distinguish both conditions. METHODS AND RESULTS: We performed an electronic search in PubMed, EMBASE, and Scopus until January 2024. Observational studies that developed and/or validated multivariable diagnostic models to differentiate Takotsubo syndrome from ACS were included. The risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We conducted a narrative synthesis of the performance measures of the diagnostic models evaluated in each study. In addition, a random-effects meta-analysis of the c-statistic with its 95% confidence interval (CI) of the InterTAK model was performed. Of 1015 articles, a total of 11 studies (n = 4552) were included. We identified eight new diagnostic models and eight were external validation of existing models. The most frequent model was InterTAK (n = 4). The reported c-statistic ranged from 0.77 to 0.97 across all models. Calibration plots were reported only for two models. The summary c-statistic was 0.89 (95% confidence interval 0.73-0.96) for the InterTAK model. The risk of bias was high for all models and the applicability was of low (50%) or unclear (50%) concern. CONCLUSIONS: 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.
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Diaz‐Arocutipa et al. (2025) conducted a meta-analysis in Takotsubo syndrome and acute coronary syndrome (n=4,552). InterTAK diagnostic model was evaluated on Discriminative performance (c-statistic) of the InterTAK model (c-statistic 0.89, 95% CI 0.73-0.96). The InterTAK diagnostic model differentiated Takotsubo syndrome from acute coronary syndrome with a summary c-statistic of 0.89 (95% CI 0.73-0.96).
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