Diagnostic models to differentiate Takotsubo syndrome from acute coronary syndrome: A systematic review and...

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.

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.

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.