Polydoros N. Kampaktsis

Background: Despite its use in patients awaiting heart transplant (HT), the impact of continuous inotropic support on short-term complications and long-term transplant outcomes remains unclear. This study evaluated inotrope use at the time of HT on perioperative complications and post-transplant survival, comparing outcomes at 30 days, 1 year, and 10 years with mechanical circulatory support (MCS) strategies including ECMO, IABP, and VADs. Methods: A retrospective analysis of the United Network for Organ sharing (UNOS) registry was performed, stratifying patients based on bridge strategy at the time of transplant: inotropes, ECMO, IABP, or VADs. Baseline characteristics, perioperative complications, and 30-day, 1-year, and 10-year post-transplant survival outcomes were analyzed across groups. Survival was assessed using Kaplan–Meier and Cox proportional hazards models. Results: Among the 11,801 heart transplant patients included, 9330 were on inotropes, 372 were on ECMO, 1072 received an IABP, and 1027 had VADs. Inotrope-bridged patients had significantly lower 30-day and 1-year mortality rates compared to the ECMO, IABP, and VAD groups. They also experienced reduced incidences of post-transplant dialysis and stroke. At 10 years, the inotrope group demonstrated superior long-term survival, with significantly lower mortality risk compared to ECMO (HR: 1.81; CI: 1.49–2.20, p < 0.001), IABP (HR: 1.19; CI: 1.06–1.32, p = 0.005), and VAD (HR: 1.18; CI: 1.10–1.27, p < 0.001). Conclusions: Continuous use of inotropes after waitlisting is associated with lower short, intermediate, and long-term mortality and does not lead to worse outcomes compared to ECMO, IABP, and VAD support. When mechanical support is not an option, inotropic therapy remains a viable and effective strategy.

Background: Patients with prior history of chest or mediastinal radiation are deemed high risk for surgical AVR. Transcatheter aortic valve replacement (TAVR) has emerged as a promising alternative for these patients, however, this patient population was underrepresented in prior TAVR trials.Aims: To compare the outcomes of TAVR in patients with versus without a history of prior chest or mediastinal radiation.Methods: This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. Systematic search of electronic databases was conducted up to September 2023. We compared early and late mortality as well as complications. A meta‐analysis was conducted with the use of a random effects model. The I‐square statistic was used to assess heterogeneity.Results: Seven studies comprising 6358 patients were included in this meta‐analysis. Patients undergoing TAVR in the radiation group had a higher risk for heart failure exacerbation (OR: 2.06; 95% CI: 1.18–3.59) and aortic valve reintervention (OR: 5.68; 95% CI: 1.83–17.67) in the early postoperative period compared to the nonradiation group. Analysis revealed similar short‐term (in‐hospital or 30‐day) all‐cause‐mortality (OR: 1.63; 95% CI: 0.89–2.98) between the two groups. Other perioperative complications including myocardial infarction (MI), stroke, pacemaker insertion requirement, major bleeding as well as access‐related complications were not significantly different between the two groups. TAVR in the radiation group was not associated with increased all‐cause mortality compared to the nonradiation group (OR: 1.40; 95% CI: 0.93–2.11) after a mean follow‐up of 17.6 months. Other endpoints including MI, stroke, need for pacemaker insertion, heart failure readmission rate, and need for aortic valve reintervention were similar in the mid‐term follow‐up between the two groups.Conclusion: TAVR in patients with a history of prior chest or mediastinal radiation was associated with similar short‐term and mid‐term mortality compared to patients without radiation. The history of chest or mediastinal radiation was associated with more frequent heart failure hospitalizations and aortic valve reintervention in the postoperative period. No difference was found in mid‐term complications. Future studies are warranted to validate our findings.

Purpose of Review To summarize the current use of cardiac computed tomography (CT) technologies as well as their pertinent evidence in regards to prevention of coronary artery disease (CAD). Recent Findings Cardiac CTA has now become a main non-invasive method for the evaluation of symptomatic CAD. In addition to coronary calcium score, other CT technologies such as atherosclerotic plaque analysis, fractional flow reserve estimation by CT, pericoronary fat attenuation, and endothelial wall shear stress have emerged. Whether the use of CT modalities can enhance risk prediction and prevention in CAD has not been fully answered. Summary We discuss the evidence for coronary artery calcium scoring and coronary CT angiography in primary prevention and the current barriers to their use. We attempt to delineate what can be done to expand use and what studies are needed to broaden adoption in the future. We also examine the potential roles of emerging CT technologies. Finally, we describe potential clinical approaches to prevention that would incorporate cardiac CT technologies.

Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.

Aim: To test the feasibility and accuracy of a new attention‐based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference.Methods and Results: We retrospectively analyzed images from 50 adult patients (median age 51, interquartile range 32–62 42% women) who had undergone CMR within 1 month of 2DE. RV planimetry of the myocardial border was performed in end‐diastole (ED) and end‐systole (ES) for eight standardized 2DE RV views with calculation of areas. The DL model comprised a Feature Tokenizer module and a stack of Transformer layers. Age, gender and calculated areas were used as inputs, and the output was RV volume in ED/ES. The dataset was randomly split into training, validation and testing subsets (35, 5 and 10 patients respectively).Mean RVEDV, RVESV and RV ejection fraction (EF) were 163 ± 70 mL, 82 ± 42 mL and 51% ± 8% respectively without differences among the subsets. The proposed method achieved good prediction of RV volumes (R² = .953, absolute percentage error [APE] = 9.75% ± 6.23%) and RVEF (APE = 7.24% ± 4.55%). Per CMR, there was one patient with RV dilatation and three with RV dysfunction in the testing dataset. The DL model detected RV dilatation in 1/1 case and RV dysfunction in 4/3 cases.Conclusions: An attention‐based DL method for 2DE RV quantification showed feasibility and promising accuracy. The method requires validation in larger cohorts with wider range of RV size and function. Further research will focus on the reduction of the number of required 2DE to make the method clinically applicable.