AimsTakotsubo 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... Show moreAimsTakotsubo 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 resultsA 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.ConclusionA 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. Show less
BackgroundBody wasting in patients with cancer can affect the heart.ObjectivesThe frequency, extent, and clinical and prognostic importance of cardiac wasting in cancer patients is unknown... Show moreBackgroundBody wasting in patients with cancer can affect the heart.ObjectivesThe frequency, extent, and clinical and prognostic importance of cardiac wasting in cancer patients is unknown.MethodsThis study prospectively enrolled 300 patients with mostly advanced, active cancer but without significant cardiovascular disease or infection. These patients were compared with 60 healthy control subjects and 60 patients with chronic heart failure (ejection fraction <40%) of similar age and sex distribution.ResultsCancer patients presented with lower left ventricular (LV) mass than healthy control subjects or heart failure patients (assessed by transthoracic echocardiography: 177 ± 47 g vs 203 ± 64 g vs 300 ± 71 g, respectively; P < 0.001). LV mass was lowest in cancer patients with cachexia (153 ± 42 g; P < 0.001). Importantly, the presence of low LV mass was independent of previous cardiotoxic anticancer therapy. In 90 cancer patients with a second echocardiogram after 122 ± 71 days, LV mass had declined by 9.3% ± 1.4% (P < 0.001). In cancer patients with cardiac wasting during follow-up, stroke volume decreased (P < 0.001) and resting heart rate increased over time (P = 0.001). During follow-up of on average 16 months, 149 patients died (1-year all-cause mortality 43%; 95% CI: 37%-49%). LV mass and LV mass adjusted for height squared were independent prognostic markers (both P < 0.05). Adjustment of LV mass for body surface area masked the observed survival impact. LV mass below the prognostically relevant cutpoints in cancer was associated with reduced overall functional status and lower physical performance.ConclusionsLow LV mass is associated with poor functional status and increased all-cause mortality in cancer. These findings provide clinical evidence of cardiac wasting–associated cardiomyopathy in cancer. Show less