Citations

AbstractIntroductionA current limitation of single chamber implantable cardioverter defibrillators (ICDs) is the lack of an atrial lead to reliably detect atrial fibrillation (AF) episodes. A novel ventricular based atrial fibrillation (VBAF) detection algorithm was created for single chamber ICDs to assess R‐R variability for detection of AF.MethodsPatients implanted with Visia AF™ ICDs were prospectively enrolled in the Medtronic Product Surveillance Registry from December 15, 2015 to January 23, 2019 and followed with at least 30 days of monitoring with the algorithm. Time to device‐detected daily burden of AF ≥ 6 min, ≥6 h, and ≥23 h were reported. Clinical actions after device‐detected AF were recorded.ResultsA total of 291 patients were enrolled with a mean follow‐up of 22.5 ± 7.9 months. Of these, 212 (73%) had no prior history of AF at device implant. However, 38% of these individuals had AF detected with the VBAF algorithm with daily burden of ≥6 min within two years of implant. In these 80 patients with newly detected AF by their ICD, 23 (29%) had a confirmed clinical diagnosis of AF by their provider. Of patients with a clinical diagnosis of AF, nine (39%) were newly placed on anticoagulation, including five of five (100%) patients having a burden >23 h.ConclusionsContinuous AF monitoring with the new VBAF algorithm permits early identification and actionable treatment for patients with undiagnosed AF that may improve patient outcomes.

Abstract Aims Our goal was to evaluate a previously validated artificial intelligence–augmented electrocardiography (AI-ECG) screening tool for left ventricular systolic dysfunction (LVSD) in patients undergoing high-sensitivity-cardiac troponin T (hs-cTnT). Methods and results Retrospective application of AI-ECG for LVSD in emergency department (ED) patients undergoing hs-cTnT. AI-ECG scores (0–1) for probability of LVSD (left ventricular ejection fraction ≤ 35%) were obtained. An AI-ECG score ≥0.256 indicates a positive screen. The primary endpoint was a composite of post-discharge major adverse cardiovascular events (MACEs) at two years follow-up. Among 1977 patients, 248 (13%) had a positive AI-ECG. When compared with patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE [48 vs. 21%, P < 0.0001, adjusted hazard ratio (HR) 1.39, 95% confidence interval (CI) 1.11–1.75]. This was largely because of a higher rate of deaths (32 vs. 14%, P < 0.0001; adjusted HR 1.26, 95% 0.95–1.66) and heart failure hospitalizations (26 vs. 6.1%, P < 0.001; adjusted HR 1.75, 95% CI 1.25–2.45). Together, hs-cTnT and AI-ECG resulted in the following MACE rates and adjusted HRs: hs-cTnT < 99th percentile and negative AI-ECG: 116/1176 (11%; reference), hs-cTnT < 99th percentile and positive AI-ECG: 28/107 (26%; adjusted HR 1.54, 95% CI 1.01–2.36), hs-cTnT > 99th percentile and negative AI-ECG: 233/553 (42%; adjusted HR 2.12, 95% CI 1.66, 2.70), and hs-cTnT > 99th percentile and positive AI-ECG: 91/141 (65%; adjusted HR 2.83, 95% CI 2.06, 3.87). Conclusion Among ED patients evaluated with hs-cTnT, a positive AI-ECG for LVSD identifies patients at high risk for MACE. The conjoint use of hs-cTnT and AI-ECG facilitates risk stratification.

Background This study aimed to compare percutaneous left atrial appendage occlusion (LAAO) with non‐vitamin K antagonist oral anticoagulants among patients with atrial fibrillation. Methods and Results Using a US administrative database, 562 850 patients with atrial fibrillation were identified, among whom 8397 were treated with LAAO and 554 453 were treated with non‐vitamin K antagonist oral anticoagulants between March 13, 2015 and December 31, 2018. Propensity score overlap weighting was used to balance baseline characteristics. The primary outcome was a composite end point of ischemic stroke or systemic embolism, major bleeding, and all‐cause mortality. The mean age was 76.4±7.6 years; 280 097 (49.8%) were female. Mean follow‐up was 1.5±1.0 years. LAAO was associated with no significant difference in the risk of the primary composite end point (hazard ratio [HR], 0.93 [0.84–1.03]), or the secondary outcomes including ischemic stroke/systemic embolism (HR, 1.07 [0.81–1.41]), and intracranial bleeding (HR, 1.08 [0.72–1.61]). LAAO was associated with a higher risk of major bleeding (HR, 1.22 [1.05–1.42], P =0.01) and a lower risk of mortality (HR, 0.73 [0.64–0.84], P <0.001). The lower risk of mortality associated with LAAO was most pronounced in patients with a prior history of intracranial bleeding. Conclusions In comparison to non‐vitamin K antagonist oral anticoagulants, LAAO was associated with no significant difference in the risk of the composite outcome and a lower risk of mortality, which suggests LAAO might be a reasonable option in select patients with atrial fibrillation. The observation of higher bleeding risk associated with LAAO highlights the need to optimize postprocedural antithrombotic regimens as well as systematic efforts to assess and address bleeding predispositions.

AbstractAimsAn artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect ‘physiological age’. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).Methods and resultsA total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, R = 0.63, P &lt; 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, R = 0.45, P &lt; 0.0001) and the recipient ages (R = 0.38, P &lt; 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), P = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002].ConclusionElectrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.

Abstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age as recent work has found differences from chronological age ("delta age") to be associated with mortality and co-morbidities. However, the genetic underpinning of delta age is unknown, but crucial for understanding underlying individual risk. By performing a genome-wide association study using UK Biobank data (n=34,432), we identified eight loci associated with delta age (p ≤ 5 × 10−8 ), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.

Background: Previous percutaneous coronary intervention risk models were focused on single outcome, such as mortality or bleeding, etc, limiting their applicability. Our objective was to develop contemporary percutaneous coronary intervention risk models that not only determine in-hospital mortality but also predict postprocedure bleeding, acute kidney injury, and stroke from a common set of variables. Methods: We built risk models using logistic regression from first percutaneous coronary intervention for any indication per patient (n=19 322, 70.6% with acute coronary syndrome) using the Mayo Clinic registry from January 1, 2000 to December 31, 2016. Approval for the current study was obtained from the Mayo Foundation Institutional Review Board. Patients with missing outcomes (n=4183) and those under 18 (n=10) were removed resulting in a sample of 15 129. We built both models that included procedural and angiographic variables (Models A) and precatheterization model (Models B). Results: Death, bleeding, acute kidney injury, and stroke occurred in 247 (1.6%), 650 (4.3%), 1184 (7.8%), and 67 (0.4%), respectively. The C statistics from the test dataset for models A were 0.92, 0.70, 0.77, and 0.71 and for models B were 0.90, 0.67, 0.76, and 0.71 for in-hospital death, bleeding, acute kidney injury, and stroke, respectively. Bootstrap analysis indicated that the models were not overfit to the available dataset. The probabilities estimated from the models matched the observed data well, as indicated by the calibration curves. The models were robust across many subgroups, including women, elderly, acute coronary syndrome, cardiogenic shock, and diabetes. Conclusions: The new risk scoring models based on precatheterization variables and models including procedural and angiographic variables accurately predict in-hospital mortality, bleeding, acute kidney injury, and stroke. The ease of its application will provide useful prognostic and therapeutic information to both patients and physicians.