Introduction Recent studies have shown that the baseline QRS area is associated with the clinical response after cardiac resynchronization therapy (CRT). In this study, we investigated the association of QRS area reduction (∆QRS area) after CRT with the outcome. We hypothesize that a larger ∆QRS area is associated with a better survival and echocardiographic response. Methods and Results Electrocardiograms (ECG) obtained before and 2–12 months after CRT from 1299 patients in a multi‐center CRT‐registry were analyzed. The QRS area was calculated from vectorcardiograms that were synthesized from 12‐lead ECGs. The primary endpoint was a combination of all‐cause mortality, heart transplantation, and left ventricular (LV) assist device implantation. The secondary endpoint was the echocardiographic response, defined as LV end‐systolic volume reduction ≥ of 15%. Patients with ∆QRS area above the optimal cut‐off value (62 µVs) had a lower risk of reaching the primary endpoint (hazard ratio: 0.43; confidence interval [CI] 0.33–0.56, p < .001), and a higher chance of echocardiographic response (odds ratio [OR] 3.3;CI 2.4–4.6, p < .0001). In multivariable analysis, ∆QRS area was independently associated with both endpoints. In patients with baseline QRS area ≥109 µVs, survival, and echocardiographic response were better when the ∆QRS area was ≥62 µVs (p < .0001). Logistic regression showed that in patients with baseline QRS area ≥109 µVs, ∆QRS area was the only significant predictor of survival (OR: 0.981; CI: 0.967–0.994, p = .006). Conclusion ∆QRS area is an independent determinant of CRT response, especially in patients with a large baseline QRS area. Failure to achieve a large QRS area reduction with CRT is associated with a poor clinical outcome.

Aims Pacing remote from the latest electrically activated site (LEAS) in the left ventricle (LV) may diminish response to cardiac resynchronization therapy (CRT). We tested whether proximity of LV pacing site (LVPS) to LEAS, determined by non-invasive three-dimensional electrical activation mapping [electrocardiographic Imaging (ECGI)], increased likelihood of CRT response. Methods and results Consecutive CRT patients underwent ECGI and chest/heart computed tomography 6–24 months of post-implant. Latest electrically activated site and the distance to LVPS (dp) were assessed. Left ventricular end-systolic volume (LVESV) reduction of ≥15% at clinical follow-up defined response. Logistic regression probabilistically modelled non-response; variables included demographics, heart failure classification, left bundle branch block (LBBB), ischaemic heart disease (IHD), atrial fibrillation, QRS duration, baseline ejection fraction (EF) and LVESV, comorbidities, use of CRT optimization algorithm, angiotensin-converting enzyme inhibitor(ACE)/angiotensin-receptor blocker (ARB), beta-blocker, diuretics, and dp. Of 111 studied patients [64 ± 11 years, EF 28 ± 6%, implant duration 12 ± 5 months (mean ± SD), 98% had LBBB, 38% IHD], 67% responded at 10 ± 3 months post CRT-implant. Latest electrically activated sites were outside the mid-to-basal lateral segments in 35% of the patients. dp was 42 ± 23 mm [31 ± 14 mm for responders vs. 63 ± 24 mm non-responders (P &lt; 0.001)]. Longer dp and the lack of use of CRT optimization algorithm were the only independent predictors of non-response [area under the curve (AUC) 0.906]. dp of 47 mm delineated responders and non-responders (AUC 0.931). Conclusion The distance between LV pacing site and latest electrical activation is a strong independent predictor for CRT response. Non-invasive electrical evaluation to characterize intrinsic activation and guide LV lead deployment may improve CRT efficacy.

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In coronavirus disease 2019 , cardiovascular risk factors and myocardial injury relate to increased mortality. We evaluated the extent of cardiac sequelae 6 months after hospital discharge in patients surviving ICU hospitalization for COVID-19.Methods: All survivors of Maastricht-ICU were invited for comprehensive cardiovascular evaluation 6 months after discharge from ICU. Cardiac screening included an electrocardiogram, cardiac biomarkers, echocardiography, cardiac magnetic resonance (CMR) and, wherever indicated, cardiac computed tomography or coronary angiogram.Results: Out of 52 survivors, 81% (n ¼ 42) participated to the cardiovascular follow-up [median follow-up of 6 months, interquartile range (IQR) 6.1-6.7]. Eight patients (19%) had newly diagnosed coronary artery disease (CAD), of which two required a percutaneous intervention. Echocardiographic global longitudinal strain (GLS) was abnormal in 24% and CMR-derived GLS was abnormal in 12%, despite normal left ventricular ejection fraction in all. None of the patients showed elevated T 1 relaxation times and five patients (14%) had an elevated T 2 relaxation time. Late gadolinium enhancement (LGE) reflecting regional myocardial fibrosis was increased in eight patients (21%), of which three had myocarditis and three had pericarditis. Conclusion:Cardiovascular follow-up at 6 months after ICU-admission for severe COVID-19 revealed that one out of five invasively mechanically ventilated survivors had CAD, a quarter had subclinical left ventricular dysfunction defined as reduced echocardiographic GLS, and 42% of the patients had CMR abnormalities (reduced LVEF, reduced GLS, LGE presence, and elevated T 2 ). On the basis of these findings, long-term cardiovascular follow-up is strongly recommended in all post-IC COVID-19 patients.Clinical Trial Registration: Trial Register number [NL8613]) https://www.trialregister.nl/trial/8613

Aims This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning–based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA. Methods and results A deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66–0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58–0.64) and 0.57 (95% CI 0.54–0.60), P &lt; 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai). Conclusion Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.