Rosenberg, Yves

Background: Ischemic event rates are low after percutaneous coronary intervention (PCI), conversely second generation P2Y12 inhibitors increase bleeding therefore identifying patients at risk to enable precision in prescribing dual anti-platelet therapy (DAPT) is of clinical interest. Prior machine-learning predictive analyses have been limited by use of older generation stents and lack of genetic data. We utilized clinical and genetic data of patients with new generation stents enrolled in TAILOR-PCI, a 40-center international trial to predict ischemic events after PCI. Methods: There were 4398 eligible post PCI patients with acute coronary syndromes or stable coronary artery disease who were randomly split into training (n=3299) and validation (n=1099) cohorts. The primary endpoint comprising of cardiovascular death, myocardial infarction, stroke, or stent thrombosis within 12 months of PCI was predicted with patient characteristics prior to hospital discharge. First, random forests were used for feature selection with 1,000 bootstraps comprising equal numbers of those with and without ischemic events to minimize the bias of sample imbalance. Features were selected by a consensus on variable-importance over bootstraps. Second, using these features in the training cohort, support vector machines (SVM) with a polynomial kernel was trained with repeated 5-fold cross validation to choose a model with highest area under the receiver operating characteristic curve (AUC) which was then used to predict ischemic events in the validation cohort. Results: The primary endpoint occurred in 2.4% of patients. Using 89 clinical and genetic variables in the random-forest feature selection step, variable importance-based consensus identified 50 top predictors. These 50 predictors achieved AUC=0.703 in the SVM training cohort and AUC=0.744 (specificity=0.84; sensitivity =0.43) in the SVM validation cohort. Conclusions: Machine learning methods using baseline clinical and genetic data enables prediction of infrequent ischemic events in post PCI patients receiving new generation stents. The role of such artificial intelligence algorithms in individualization of DAPT by identifying high or low risk patients needs to be explored.

AbstractRandomized controlled trials (RCTs) remain the gold standard to evaluate clinical interventions, producing the highest level of evidence while minimizing potential bias. Inadequate recruitment is a commonly encountered problem that undermines the completion and generalizability of RCTs—and is even more challenging when enrolling amidst a pandemic. Here, we reflect on our experiences with virtual recruitment of non‐hospitalized patients in the United States for ColCorona, an international, multicenter, randomized, placebo‐controlled coronavirus disease 2019 (COVID‐19) drug trial. Recruitment challenges during a pandemic include constraints created by shelter‐in‐place policies and targeting enrollment according to national and local fluctuations in infection rate. Presenting a study to potential participants who are sick with COVID‐19 and may be frightened, overwhelmed, or mistrusting of clinical research remains a challenge. Strategies previously reported to improve recruitment include transparency, patient and site education, financial incentives, and person‐to‐person outreach. Active measures taken during ColCorona to optimize United States recruitment involved rapid expansion of sites, adjustment of recruitment scripts, assessing telephone calls versus text messages for initial contact with participants, institutional review board‐approved financial compensation, creating an infrastructure to systematically identify potentially eligible patients, partnering with testing sites, appealing to both self‐interest and altruism, and large‐scale media efforts with varying degrees of success.

The interrelationships between atrial fibrillation (AF) and heart failure (HF) are complex and poorly understood, yet the number of patients with AF and HF continues to increase worldwide. Thus, there is a need for initiatives that prioritize research on the intersection between AF and HF. This article summarizes the proceedings of a virtual workshop convened by the US National Heart, Lung, and Blood Institute to identify important research opportunities in AF and HF. Key knowledge gaps were reviewed and research priorities were proposed for characterizing the pathophysiological overlap and deleterious interactions between AF and HF; preventing HF in people with AF; preventing AF in individuals with HF; and addressing symptom burden and health status outcomes in AF and HF. These research priorities will hopefully help inform, encourage, and stimulate innovative, cost-efficient, and transformative studies to enhance the outcomes of patients with AF and HF.

Catheter ablation has brought major advances in the management of patients with atrial fibrillation (AF). As evidenced by multiple randomized trials, AF catheter ablation can reduce the risk of recurrent AF and improve quality of life. In some studies, AF ablation significantly reduced cardiovascular hospitalizations. Despite the existing data on AF catheter ablation, numerous knowledge gaps remain concerning this intervention. This report is based on a recent virtual workshop convened by the National Heart, Lung, and Blood Institute to identify key research opportunities in AF ablation. We outline knowledge gaps related to emerging technologies, the relationship between cardiac structure and function and the success of AF ablation in patient subgroups in whom clinical benefit from ablation varies, and potential platforms to advance clinical research in this area. This report also considers the potential value and challenges of a sham ablation randomized trial. Prioritized research opportunities are identified and highlighted to empower relevant stakeholders to collaborate in designing and conducting effective, cost-efficient, and transformative research to optimize the use and outcomes of AF ablation.

Common genetic variation in CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19) *2 and *3 alleles leads to a loss of functional protein, and carriers of these loss-of-function alleles when treated with clopidogrel have significantly reduced clopidogrel active metabolite levels and high on-treatment platelet reactivity resulting in increased risk of major adverse cardiovascular events, especially after percutaneous coronary intervention. The Food and Drug Administration has issued a black box warning advising practitioners to consider alternative treatment in CYP2C19 poor metabolizers who might receive clopidogrel and to identify such patients by genotyping. However, routine clinical use of genotyping for CYP2C19 loss-of-function alleles in patients undergoing percutaneous coronary intervention is not recommended by clinical guidelines because of lack of prospective evidence. To address this critical gap, TAILOR-PCI (Tailored Antiplatelet Initiation to Lessen Outcomes due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention) is a large, pragmatic, randomized trial comparing point-of-care genotype-guided antiplatelet therapy with routine care to determine whether identifying CYP2C19 loss-of-function allele patients prospectively and prescribing alternative antiplatelet therapy is beneficial.