Key result
InterTAK-ML predicts in-hospital mortality with an AUC of ~0.89, outperforming traditional scores.
Why the study?
Takotsubo syndrome is associated with a substantial rate of adverse events, and prediction of in-hospital death risk remains challenging.
Observational (n=4,519)
Yes
Effect estimate: AUC 0.89 (95% CI 0.85-0.92)
Absolute Event Rate: 0.89% vs 0.78%
The machine learning-based InterTAK-ML model demonstrated high discriminative capacity for predicting in-hospital death in Takotsubo syndrome and allows for the identification of clinically distinct risk phenotypes.
May aid in-hospital risk stratification in Takotsubo syndrome; leaves open prospective validation and outcome impact.
Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.
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FILIPPO et al. (2026) conducted an observational in Takotsubo syndrome (n=4,519). InterTAK-ML Model vs. InterTAK traditional model and GEIST score was evaluated on Area under the curve (AUC) for in-hospital death prediction (AUC 0.89, 95% CI 0.85-0.92). The InterTAK-ML model successfully predicted in-hospital death with an AUC of 0.89 in the internal validation cohort and 0.82 in the external validation cohort, outperforming traditional scores.
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