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Cardiac alterations are frequently observed after acute neurological disorders. Takotsubo syndrome (TTS) represents an acute heart failure syndrome and is increasingly recognized as part of the spectrum of cardiac complications observed after neurological disorders. A systematic investigation of TTS patients with neurological disorders has not been conducted yet. The aim of the study was to expand insights regarding neurological disease entities triggering TTS and to investigate the clinical profile and outcomes of TTS patients after primary neurological disorders. The International Takotsubo Registry is an observational multicenter collaborative effort of 45 centers in 14 countries (ClinicalTrials.gov, identifier NCT01947621). All patients in the registry fulfilled International Takotsubo Diagnostic Criteria. For the present study, patients were included if complete information on acute neurological disorders were available. 2402 patients in whom complete information on acute neurological status were available were analyzed. In 161 patients (6.7%) an acute neurological disorder was identified as the preceding triggering factor. The most common neurological disorders were seizures, intracranial hemorrhage, and ischemic stroke. Time from neurological symptoms to TTS diagnosis was ≤ 2 days in 87.3% of cases. TTS patients with neurological disorders were younger, had a lower female predominance, fewer cardiac symptoms, lower left ventricular ejection fraction, and higher levels of cardiac biomarkers. TTS patients with neurological disorders had a 3.2-fold increased odds of in-hospital mortality compared to TTS patients without neurological disorders. In this large-scale study, 1 out of 15 TTS patients had an acute neurological condition as the underlying triggering factor. Our data emphasize that a wide spectrum of neurological diseases ranging from benign to life-threatening encompass TTS. The high rates of adverse events highlight the need for clinical awareness.

Background Atrial fibrillation (AF) is a major risk factor for mortality. The prevalence, clinical correlates, and prognostic impact of AF in Takotsubo syndrome (TTS) have not yet been investigated in a large patient cohort. This study aimed to investigate the prevalence, clinical correlates, and prognostic impact of AF in patients with TTS. Methods and Results Patients with TTS were enrolled from the International Takotsubo Registry, which is a multinational network with 26 participating centers in Europe and the United States. Patients were dichotomized according to the presence or absence of AF at the time of admission. Of 1584 patients with TTS, 112 (7.1%) had AF. The mean age was higher ( P <0.001), and there were fewer women ( P =0.046) in the AF than in the non‐AF group. Left ventricular ejection fraction was significantly lower ( P =0.001), and cardiogenic shock was more often observed ( P <0.001) in the AF group. Both in‐hospital ( P <0.001) and long‐term mortality ( P <0.001) were higher in the AF group. Multivariable Cox regression analysis revealed that AF was independently associated with higher long‐term mortality (hazard ratio, 2.31; 95% CI, 1.50–3.55; P <0.001). Among patients with AF on admission, 42% had no known history of AF before the acute TTS event, and such patients had comparable in‐hospital and long‐term outcomes compared with those with a history of AF. Conclusions In patients presenting with TTS, AF on admission is significantly associated with increased in‐hospital and long‐term mortality rates. Whether antiarrhythmics and/or cardioversion are beneficial in TTS with AF should thus be tested in a future trial. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01947621.

Persistent post-partum glucose metabolism disorders are frequent in women with GDM and associated with lower maternal educational level. Interventions aimed at this risk group may contribute towards a decrease in prevalence of post-partum glucose metabolism disorders.

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Aims Acute pulmonary disorders are known physical triggers of takotsubo syndrome (TTS). This study aimed to investigate prevalence of acute pulmonary triggers in patients with TTS and their impact on outcomes. Methods and results Patients with TTS were enrolled from the International Takotsubo Registry and screened for triggering factors and comorbidities. Patients were categorized into three groups (acute pulmonary trigger, chronic lung disease, and no lung disease) to compare clinical characteristics and outcomes. Of the 1670 included patients with TTS, 123 (7%) were identified with an acute pulmonary trigger, and 194 (12%) had a known history of chronic lung disease. The incidence of cardiogenic shock was highest in patients with an acute pulmonary trigger compared with those with chronic lung disease or without lung disease (17% vs. 10% vs. 9%, P = 0.017). In-hospital mortality was also higher in patients with an acute pulmonary trigger than in the other two groups, although not significantly (5.7% vs. 1.5% vs. 4.2%, P = 0.13). Survival analysis demonstrated that patients with an acute pulmonary trigger had the worst long-term outcome (P = 0.002). The presence of an acute pulmonary trigger was independently associated with worse long-term mortality (hazard ratio 2.12, 95% confidence interval 1.33-3.38; P = 0.002). Conclusions The present study demonstrates that TTS is related to acute pulmonary triggers in 7% of all TTS patients, which accounts for 21% of patients with physical triggers. The presence of acute pulmonary trigger is associated with a severe in-hospital course and a worse long-term outcome.

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 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 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). 31 clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the receiver‐operating characteristic curve (AUC), Sensitivity and Specificity. As secondary endpoint, a K‐Medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the ten 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), Sensitivity 0.85 (0.78‐0.95) and Specificity 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 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.This article is protected by copyright. All rights reserved.

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