Key result
Admission clinical model differentiates emotional vs. physical Takotsubo syndrome with ~74% accuracy.
Why the study?
Distinguishing between emotional and physical aetiologies of takotsubo syndrome is essential for optimizing patient care but remains challenging.
Can an interpretable machine learning model based on admission clinical markers accurately differentiate between emotional and physical aetiologies in patients with Takotsubo syndrome?
Observational (n=441)
Can an interpretable machine learning model based on admission clinical markers accurately differentiate between emotional and physical aetiologies in patients with Takotsubo syndrome?
Effect estimate: AUC 0.78
An interpretable machine learning model based on routine admission clinical features demonstrates modest ability to differentiate between emotional and physical triggers of Takotsubo syndrome.
Machine learning may aid differentiation of primary versus secondary TTS; leaves open whether this refines risk stratification or alters management.
BACKGROUND: Takotsubo syndrome (TTS) is an acute coronary syndrome characterized by a reversible, mostly apical dysfunction of the left ventricle. Based on the triggering event, TTS has been classified as primary due to emotional causes and secondary due to physical stress. Using a comprehensive machine-learning approach, we aimed to distinguish between these two types of TTS, an essential task for optimizing patient care. METHODS: Based on a dataset of 320 TTS patients from a research group in Rome, a logistic regression model was trained to develop an interpretable predictive model capable of accurately classifying the aetiology of TTS in individual patients using admission-based clinical markers. RESULTS: The developed model achieved 74 % accuracy, 75 % precision and recall, 72 % specificity, and an area under the curve (AUC) of 0.78. Based on the studies conducted, chest pain, dyspnoea, atrial fibrillation, sex, chronic obstructive pulmonary disease, heart rate, and cancer were identified as key clinical features for differentiating between the two TTS types. An external validation cohort of 121 TTS patients has been employed further to assess the performance of the trained classification model, obtaining 74 % accuracy, 77 % precision, 91 % recall, 27 % specificity, and an AUC of 0.62. CONCLUSIONS: An interpretable machine learning model has been developed, demonstrating the ability to distinguish between emotional versus physical aetiologies in TTS, highlighting the most impactful clinical factors. As built considering clinical variables recorded at admission, the model may serve as an immediate tool that can guide clinicians in their practice.
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Scuppa et al. (2025) conducted an observational in Takotsubo syndrome (n=441). Logistic regression predictive model was evaluated on Accuracy and AUC for differentiating emotional versus physical aetiologies of Takotsubo syndrome (AUC 0.78). An interpretable logistic regression model using admission clinical variables differentiated emotional from physical Takotsubo syndrome aetiologies with 74% accuracy and an AUC of 0.78.
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