Daily Cardiology Research Analysis

BACKGROUND: Suboptimal left ventricular (LV) and right ventricular lead positioning has been associated with a lesser response to cardiac resynchronization therapy. The MAPIT-CRT (MRI Allocation of Pacing Targets in Cardiac Resynchronization Therapy) randomized controlled trial evaluated a novel, cardiac magnetic resonance-generated 4-dimensional phenomics cardiac magnetic resonance imaging (4DPcmr) lead placement strategy. METHODS: A total of 202 participants with New York Heart Association class II to IV heart failure on optimal medical therapy, LV ejection fraction ≤35%, and QRS duration ≥120 ms were enrolled from 7 Canadian sites. Participants were randomized to 4DPcmr-guided lead placement using a web-based application or standard lead placement. 4DPcmr-recommended LV and right ventricular lead locations were generated using the combined consideration of (1) regional scar distribution and burden, (2) maximal regional delay in LV peak systolic strain, and (3) maximal interlead distance. RESULTS: The primary end point, an increase in LV ejection fraction ≥5% at 6 months, was reached in 69 of 105 4DPcmr-guided patients (65.7%) versus 50 of 96 control patients (52.1%; risk ratio, 1.80 [95% CI, 1.02-3.17]; CONCLUSIONS: 4DPcmr-guided LV/right ventricular cardiac resynchronization therapy lead implantation using a practical web application was clinically feasible, safe, and was associated with greater LV ejection fraction improvement at 6 months versus standard of care with no increase in procedural times or complications. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01640769.

BACKGROUND: Early discontinuation of dual antiplatelet therapy (DAPT) within 3 months after implantation of drug-eluting stents may reduce bleeding risk without increasing ischaemic events. This study aimed to assess whether 3-month DAPT is as safe as the conventional 12-month regimen in patients treated with Firehawk stents, which have demonstrated 99.9% reendothelialisation at 3 months by optical coherence tomography. METHODS: From January 1, 2019, to April 30, 2022, 2445 patients were randomly assigned to a 3-month (n = 1222) or 12-month DAPT regimen (n = 1223) in this randomised, open-label, non-inferiority trial, patients from 36 centres in China who underwent percutaneous coronary intervention with Firehawk stents. The primary endpoint was a composite of all-cause death, myocardial infarction, cerebrovascular accident, and major bleeding (Bleeding Academic Research Consortium type 2, 3, or 5) at 18 months. The co-equal secondary endpoints were major adverse cardiovascular events, defined as a composite of all-cause death, myocardial infarction and ischaemia-driven target lesion revascularisation, and major bleeding. RESULTS: From January 1, 2019, to April 30, 2022, 2445 patients were randomly assigned to 3-month (N = 1222) or 12-month DAPT regimen (N = 1223). Adherence to the protocol-defined DAPT duration was 71.0% and 95.5%, respectively. Rates of the primary endpoint were comparable between both groups (10.1% vs 10.9%). Non-inferiority of 3-month DAPT was established (absolute rate difference, - 0.76%; upper limit of 1-sided 97.5% CI, 1.70%; p non-inferiority = 0.0003, with a predefined non-inferiority margin of 3.5%). Rates of co-secondary endpoints showed no significant difference (both p > 0.05). Landmark analysis (3-18 months) showed significant lower rate of major bleeding (2.7% vs 4.4%; p = 0.03) with 3-month DAPT. CONCLUSIONS: In patients treated with Firehawk stents, 3-month DAPT was non-inferior to 12-month DAPT for the primary composite endpoint of all-cause death, myocardial infarction, cerebrovascular accident, and major bleeding at 18 months. TRIAL REGISTRATION: NCT03008083 (clinicaltrials.gov).

BACKGROUND: The wide availability of labeled electrocardiogram (ECG) data has driven major advances in artificial intelligence (AI)-based detection of structural and functional cardiac abnormalities and thus ECG-based diagnosis. However, many critical, high value clinical diagnostic applications, such as assessing myocardial ischemia and coronary microvascular dysfunction, remain underserved due to the limited availability of labeled datasets. We developed a self-supervised ECG foundation model and demonstrate how this approach can overcome this limitation. METHODS: A modified vision transformer model was pretrained using a large database of unlabeled ECG waveforms (MIMIC-IV-ECG, N=800,035). The model was then fine-tuned using smaller databases that included high-quality labels derived from positron emission tomography (N=3,126) and clinical reports (N=13,704) for 12 clinical, demographic, and traditional ECG prediction tasks. Diagnostic accuracy and model generalizability were evaluated across five additional cohorts including the publicly available PTB-XL and UK Biobank databases and labels from cardiac magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). RESULTS: Diagnostic performance varied across tasks with area under the receiver operating characteristic curve (AUROC) ranging from 0.763 for detection of impaired myocardial flow reserve (MFR < 2) to 0.955 for impaired left ventricular ejection fraction (LVEF < 35%). Self-supervised learning (SSL) pretraining greatly improved diagnostic accuracy in 11 of the 12 prediction tasks compared to conventional CONCLUSION: This versatile ECG foundation model demonstrates that SSL pretraining enhances diagnostic accuracy and generalizability across diverse cardiac diagnostic applications. By enabling effective learning from limited labeled data, this approach supports AI development for complex but clinically critical tasks, such as detecting myocardial ischemia and coronary microvascular dysfunction, where high-quality labels are costly and scarce.