Diagnostic models to differentiate Takotsubo syndrome from acute coronary syndrome: A systematic review and meta-analysis

• Autores: Diaz-Arocutipa, Carlos, Hernandez, Adrian V., Benites-Moya, Cesar Joel, Gamarra-Valverde, Norma Nicole, Yrivarren-Cespedes, Rafael, Torres-Valencia, Javier, Vicent, Lourdes
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/684192
• Enlace del recurso: http://hdl.handle.net/10757/684192
• Nivel de acceso: acceso embargado
• Materia: Acute coronary syndrome; Diagnostic model; Systematic review; Takotsubo syndrome

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. 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.