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
Aims Permanent pacemaker implantation (PPI) may be required after transcatheter aortic valve implantation (TAVI). Evidence on PPI prediction has largely been gathered from high-risk patients... Show moreAims Permanent pacemaker implantation (PPI) may be required after transcatheter aortic valve implantation (TAVI). Evidence on PPI prediction has largely been gathered from high-risk patients receiving first-generation valve implants. We undertook a meta-analysis of the existing literature to examine the incidence and predictors of PPI after TAVI according to generation of valve, valve type, and surgical risk.Methods and results We made a systematic literature search for studies with >= 100 patients reporting the incidence and adjusted predictors of PPI after TAVI. Subgroup analyses examined these features according to generation of valve, specific valve type, and surgical risk. We obtained data from 43 studies, encompassing 29 113 patients. Permanent pacemaker implantation rates ranged from 6.7% to 39.2% in individual studies with a pooled incidence of 19% (95% CI 16-21). Independent predictors for PPI were age [odds ratio (OR) 1.05, 95% confidence interval (CI) 1.01-1.09], left bundle branch block (LBBB) (OR 1.45, 95% CI 1.12-1.77), right bundle branch block (RBBB) (OR 4.15, 95% CI 3.23-4.88), implantation depth (OR 1.18, 95% CI 1.11-1.26), and self-expanding valve prosthesis (OR 2.99, 95% CI 1.39-4.59). Among subgroups analysed according to valve type, valve generation and surgical risk, independent predictors were RBBB, self-expanding valve type, first-degree atrioventricular block, and implantation depth.Conclusions The principle independent predictors for PPI following TAVI are age, RBBB, LBBB, self-expanding valve type, and valve implantation depth. These characteristics should be taken into account in pre-procedural assessment to reduce PPI rates. Show less
