Kong, William

Abstract Frailty is highly prevalent in cardiovascular disease and associates with poor clinical outcomes. Several cardiovascular conditions benefit from invasive interventions, but the impact of these procedures in frail patients remains unclear. We thus performed a systematic review and meta-analysis to summarize evidence and quantify the pooled effect of clinical outcomes of invasive cardiovascular interventions among frail vs. non-frail patients. Systematic search of two electronic databases from January 2015 to April 2025 was performed including all randomized controlled trials (RCTs) and prospective cohort studies examining outcomes of invasive cardiovascular interventions stratified by frailty. Outcomes included all-cause mortality, hospitalizations, and other adverse outcomes. Risk ratios (RRs) and their 95% confidence intervals (CI) were assessed using random-effects models. Thirty-one studies (10 RCTs, 21 prospective, 3 retrospective studies) with a total of 27 534 participants were included. Compared with non-frail patients, frail patients after cardiovascular procedures were at higher risks of all-cause mortality (RR 3.14, 95% CI: 2.29-4.30), and hospitalizations (RR 1.88, 95% CI: 1.29-2.73). Frail patients undergoing transcatheter aortic-valve implantation and percutaneous mitral-valve interventions did not have significant increased mortality risks compared with non-frail patients (P > 0.05). Pre-frailty predicted lower mortality risks amongst patients undergoing transcatheter vs. surgical aortic valve replacement (HR 0.78, 95% CI: 0.67-0.92). Frail patients with non-ST elevation myocardial infarction derived similar mortality benefits from an invasive vs. conservative approach (HR 1.13, 95% CI: 0.64-2.01). In conclusion, frailty prognosticates adverse outcomes following invasive cardiovascular interventions and influences treatment efficacy, highlighting its significance in treatment selection

Abstract Infective endocarditis (IE) is a relatively rare but life-threatening systemic infection, which remains associated with high morbidity and mortality. The epidemiology of IE has shifted to involve an increasing numbers of older patients with both cardiovascular and other types of prosthetic devices, multiple comorbid conditions often requiring invasive procedures, increasingly virulent pathogens, in particular Staphylococcus aureus, or that can harbour anti-microbial resistance, and an escalation of injection drug use in many areas of the world. In parallel, advancements in diagnostic and therapeutic options have led to complex strategies in patients’ management. Despite these epidemiologic shifts, clinical trials have been rare and most of the evidence guiding IE management derives from expert consensus or analysis of large registries. Because of this, a multi-disciplinary IE team-based approach has been recommended as the standard of care. The aim of this review is to explore the rationale for a multi-disciplinary team-based approach to the management of IE. This approach has proved to be potentially beneficial based on multiple investigations that have evaluated patient outcomes. In addition, implementation strategies, feasibility and options of the team approach have also been highlighted.

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.