Machine learning electrocardiography model to differentiate takotsubo syndrome from myocardial infarction.

Aims: Machine learning (ML) algorithms applied to the electrocardiography (ECG) have been successful in several cardiac diagnoses, however, rarely been used for the diagnostics of takotsubo syndrome (TTS). Our aim was to develop ML-based ECG-models to differentiate TTS from patients with myocardial infarction (MI). Methods and results: Cross-sectional study in Stockholm. A neural network with UNet architecture was trained and validated on 507 TTS cases and 14 978 controls with suspected and verified MI, identified from the Swedish coronary angiography and angioplasty register. Cross-validation was performed. The models were compared with cardiologists using previously proposed ECG criteria. Receiver operating characteristics (ROC) area under the curve (AUC) for discriminating TTS from patients with ST-elevation and non-ST-elevation MI ROC AUC 0.88 (cross-validation: 0.85-0.92) and 0.86 (cross-validation: 0.82-0.91), respectively. ROC AUC for discriminating TTS from verified MI [non-ST-elevation MI (NSTEMI) and ST-elevation MI (STEMI)] was 0.87 (cross-validation: 0.83-0.91) with sensitivity (0.75) and specificity (0.83) with low positive predictive value (PPV) and high negative predictive value (NPV). Results for suspected MI was ROC AUC 0.85 (cross validation: 0.81-0.91) with sensitivity (0.75) and specificity (0.79) with low PPV (0.11) and high NPV (0.99). The committee of two cardiologists using a combination of ECG criteria achieved an ROC AUC of 0.71. Conclusion: Machine learning models could discriminate TTS from MI (NSTEMI and STEMI) and suspected MI with high sensitivity and NPV, outperforming cardiologists using conventional criteria. The models require further refinement to increase PPV, precision-recall and external validation, but it holds promise for TTS screening aiding the clinician in ruling out TTS.