Lüscher, Thomas F.

Abstract Vaccination is increasingly acknowledged as an effective preventive measure not only against specific infections, but also for the prevention of cardiovascular disease in high-risk patients. Specifically, a growing body of evidence suggests that vaccines against influenza, SARS-CoV-2, respiratory syncytial virus, herpes zoster, and other viruses significantly reduce infection and for influenza the incidence of major adverse cardiovascular events in vaccinated individuals. This clinical consensus statement examines the existing literature and accumulated evidence and offers practical clinical advice on vaccination timing and target demographics, specifically addressing complex clinical scenarios with a focus on cardiovascular conditions. It includes guidelines for vaccinating vulnerable populations such as immunosuppressed individuals, patients with congenital heart disease, and pregnant women as well as safety and potential complications of the procedure.

Abstract Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk–benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Abstract Cardiac sarcoidosis (CS) is a form of inflammatory cardiomyopathy associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, and heart failure as well as sudden cardiac death. It is therefore important to provide an expert consensus statement summarizing the role of different available diagnostic tools and emphasizing the importance of a multidisciplinary approach. By integrating clinical information and the results of diagnostic tests, an accurate, validated, and timely diagnosis can be made, while alternative diagnoses can be reasonably excluded. This clinical expert consensus statement reviews the evidence on the management of different CS manifestations and provides advice to practicing clinicians in the field on the role of immunosuppression and the treatment of cardiac complications based on limited published data and the experience of international CS experts. The monitoring and risk stratification of patients with CS is also covered, while controversies and future research needs are explored.

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 Over 10 million doses of COVID-19 vaccines based on RNA technology, viral vectors, recombinant protein, and inactivated virus have been administered worldwide. Although generally very safe, post-vaccine myocarditis can result from adaptive humoral and cellular, cardiac-specific inflammation within days and weeks of vaccination. Rates of vaccine-associated myocarditis vary by age and sex with the highest rates in males between 12 and 39 years. The clinical course is generally mild with rare cases of left ventricular dysfunction, heart failure and arrhythmias. Mild cases are likely underdiagnosed as cardiac magnetic resonance imaging (CMR) is not commonly performed even in suspected cases and not at all in asymptomatic and mildly symptomatic patients. Hospitalization of symptomatic patients with electrocardiographic changes and increased plasma troponin levels is considered necessary in the acute phase to monitor for arrhythmias and potential decline in left ventricular function. In addition to evaluation for symptoms, electrocardiographic changes and elevated troponin levels, CMR is the best non-invasive diagnostic tool with endomyocardial biopsy being restricted to severe cases with heart failure and/or arrhythmias. The management beyond guideline-directed treatment of heart failure and arrhythmias includes non-specific measures to control pain. Anti-inflammatory drugs such as non-steroidal anti-inflammatory drugs, and corticosteroids have been used in more severe cases, with only anecdotal evidence for their effectiveness. In all age groups studied, the overall risks of SARS-CoV-2 infection-related hospitalization and death are hugely greater than the risks from post-vaccine myocarditis. This consensus statement serves as a practical resource for physicians in their clinical practice, to understand, diagnose, and manage affected patients. Furthermore, it is intended to stimulate research in this area.

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.

Abstract Mechanism-based therapy centred on the molecular understanding of disease-causing pathways in a given patient is still the exception rather than the rule in medicine, even in cardiology. However, recent successful drug developments centred around the second messenger cyclic guanosine-3′-5′-monophosphate (cGMP), which is regulating a number of cardiovascular disease modulating pathways, are about to provide novel targets for such a personalized cardiovascular therapy. Whether cGMP breakdown is inhibited or cGMP synthesis is stimulated via guanylyl cyclases or their upstream regulators in different cardiovascular disease phenotypes, the outcomes seem to be so far uniformly protective. Thus, a network of cGMP-modulating drugs has evolved that act in a mechanism-based, possibly causal manner in a number of cardiac conditions. What remains a challenge is the detection of cGMPopathy endotypes amongst cardiovascular disease phenotypes. Here, we review the growing clinical relevance of cGMP and provide a glimpse into the future on how drugs interfering with this pathway may change how we treat and diagnose cardiovascular diseases altogether.

Abstract 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.