Niederseer, David

AbstractBackgroundThe clinical relevance of cardiac troponin (cTn) elevation in takotsubo syndrome (TTS) remains uncertain. The present study sought to investigate the role of cardiac troponin (cTn) elevations in mortality prediction of patients with Takotsubo syndrome (TTS).MethodsPatients enrolled in the International Takotsubo (InterTAK) Registry from January 2011 to February 2020 with available data on peak cTn levels were included in the analysis. Peak cTn levels during the index hospitalization were used to define clinically relevant myocardial injury. The threshold at which clinically relevant myocardial injury drives mortality at 1 year was identified using restricted cubic spline analysis.ResultsOut of 2′938 patients, 222 (7.6%) patients died during 1‐year follow‐up. A more than 28.8‐fold increase of cTn above the upper reference limit was identified as threshold for clinically relevant myocardial injury. The presence of clinically relevant myocardial injury was significantly associated with an increased risk of mortality at 5 years (adjusted HR 1.58, 95% CI 1.18–2.12, p =.002). Clinically relevant myocardial injury was related to an increased 5‐year mortality risk in patients with apical TTS (adjusted HR 1.57, 95% CI 1.21–2.03, p =.001), in presence of physical stressors (adjusted HR 1.60, 95% CI 1.22–2.11, p =.001), and in absence of emotional stressors (adjusted HR 1.49, 95% CI, 1.17–1.89, p =.001).ConclusionThis study for the first time determined a troponin threshold for the identification of TTS patients at excess risk of mortality. These findings advance risk stratification in TTS and assist in identifying patients in need for close monitoring and follow‐up.

ABSTRACT 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.

Abstract Aims Current European heart failure (HF) guidelines suggest the use of risk score: among them, the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score has demonstrated to be one of the most accurate. However, the risk scores are still poorly implemented in clinical practice, also due to the lack of strong evidence regarding their external validation in different populations. Thus, the current study was designed as an external validation test of the MECKI score in an international multicentre setting. Methods and results The study cohort consisted of patients diagnosed with HF with reduced ejection fraction (HFrEF) across international centres (not Italian), retrospectively recruited. Collected data included demographics, HF aetiology, laboratory testing, electrocardiogram (ECG), echocardiographic findings, and cardiopulmonary exercise testing (CPET) results as described in the original MECKI score publication. A total of 1042 patients across 8 international centres (7 European and 1 Asian) were included and followed up from 1998 till 2019. Patients were divided according to the calculated MECKI scores into three subgroups: (i) MECKI score <10%, (ii) 10–20%, and (iii) ≥ 20%. Survival analysis comparison among the three MECKI score subgroups showed a worse prognosis in patients with higher MECKI score value: median event-free survival times were 4396 days for MECKI score <10%, 3457 days for 10–20%, and 1022 days for ≥20% (P < 0.0001). Receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) were like those reported in the original internal validation studies. Conclusion In patients diagnosed with HFrEF, the power of the MECKI score was confirmed in terms of prognosis and risk stratification, supporting its implementation as advised by the HF guidelines.