Avram, Robert

Abstract Valvular heart disease (VHD) is emerging as the next major epidemic in cardiovascular care, driven by the ageing population. The advent of less invasive alternatives to surgery—namely, transcatheter therapies—has revolutionized patient management, while simultaneously introducing new layers of complexity. Artificial intelligence (AI) holds significant promise to transform the care of patients with VHD across all dimensions, including diagnosis, prognostication, disease progression, and therapeutic decision-making. In this review, we provide a comprehensive overview of the current evidence, and discuss the challenges, risks, and future directions associated with the integration of AI in the management of VHD.

Background Elective percutaneous coronary intervention (PCI) is performed to relieve symptoms of angina. Identifying patients who will benefit symptomatically after PCI would be clinically advantageous but robust predictors of symptom resolution are ill-defined. Methods Prospective indexing of baseline angina status, clinical, and procedural characteristics were collected over a 5-year period in a regional revascularization registry. At 1-year follow-up, angina resolution was assessed. We performed a stepwise selection algorithm to identify predictors of persistent angina at 1 year. Results A total of 777 patients were included in the analysis and the median follow-up was 387 days. Mean age of the cohort was 66.6 years, 23.8% were female and 23.3% had baseline Canadian Cardiovascular Society class 3 or 4 angina. Overall, 13.1% had persistent angina. The only predictor of persistent angina was the presence of a residual chronic total occlusion after PCI with odds ratio of 3.06 (95% confidence interval, 1.81–5.17). Residual stenoses 50–69%, 70–89%, and 90–99% were not associated with residual angina after PCI. Conclusion Most patients achieved symptom resolution with PCI and optimal medical therapy. A residual chronic total occlusion after PCI was associated with persistent angina. Other degrees of stenoses were not associated with persistent angina.

Abstract Background/Introduction TAILOR-PCI is the largest cardiovascular genotype-based randomized trial (NCT#01742117) investigating whether genotype-guided selection of oral P2Y12 inhibitor therapy improves ischemic outcomes after percutaneous coronary intervention (PCI). The TAILOR-PCI Digital Sub-Study tests the feasibility of extending original follow-up of 1 year to 2 years using state-of-the-art digital solutions. Deep phenotyping acquired during a clinical trial can be leveraged by extending follow-up in an efficient and cost-effective manner using digital technology. Purpose Our objective is to describe onboarding and engagement of participants initially recruited in a large, pragmatic, international, multi-center clinical trial to a digital registry. Methods TAILOR-PCI participants, within 23 months of their index PCI, were invited by letters containing a URL to the Digital Sub-Study website (http://tailorpci.eurekaplatform.org). These invitations were followed by phone calls, if no response to the letter, to determine reason for non-participation. A NIH-funded direct-to-participant digital research platform (the Eureka Research Platform) was used to onboard, consent and enroll participants for the digital follow-up. Participants were asked to answer health-related surveys at fixed intervals using the Eureka mobile app and desktop platform. To capture hospitalizations, participants could enable geofencing to allow background location tracking, which triggered surveys if a hospitalization was detected. Result(s) Letters were mailed to 893 of 929 eligible participants across 22 sites in the United States and Canada leading to 226 homepage visits and 118 registrations. There were 107 consents (12.0% of invited; mean age: 66.4±9.0; 19 females [18%]): 47 (44%) participants consented after the letter, 36 (34%) consented after the 1st call and 24 (22%) consented after a 2nd call. Among those who consented, 100 were eligible (7 did not have a smartphone) 81 downloaded the study mobile app and 73 agreed for geofencing (Figure 1). Among the 722 invited participants who were surveyed, 354 declined participation: due to lack of time (146; 20.2%), lack of smartphone (125; 17.3%), difficulty understanding (41; 5.7%), concern about using smartphone (34; 4.7%), concern of data privacy (14; 1.9%), concerns of location tracking (6; 0.8%) and other reasons (57; 7.9%). Conclusion Extended follow-up of a clinical trial using a digital platform is feasible but uptake in this study population was limited largely due to lack of time or a smartphone among participants. Based on data from other digital studies, uptake may also have been limited since digital follow-up consent was not incorporated at the time of consent for the main trial. Figure 1. Onboarding of the digital substudy Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI)