Van Aelst, Lucas N L

Introduction: Diastolic dysfunction is a hallmark of heart failure with preserved ejection fraction, yet echocardiographic grading is challenging in patients with hypertrophic or amyloid cardiomyopathies due to variable structural and functional remodeling. Consequently, there is a need for additional diagnostic tools to identify patients at increased risk of diastolic dysfunction and enhanced filling pressure. Research Question: What is the accuracy of an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to predict diastolic dysfunction in hypertrophic and amyloid cardiomyopathy? Methods: We included patients with diverse phenotypes of hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis and validated Mayo Clinic's AI-enabled ECG model to grade diastolic function (normal: grade 0/1; abnormal: grade 2/3). Echocardiographic analysis of systolic (LVEF) and diastolic function was performed within 14 days of ECG recording. Diastolic function was graded based on E/e’ (>14), left atrial volume index (>34 mL/m2), pulmonary venous atrial reversal flow duration (≥30 ms), and peak tricuspid regurgitation velocity (>2.8 m/s). Filling pressure was considered increased if ≥3 parameters were abnormal, inconclusive if 2 were abnomal, and normal if ≥3 were normal. Echocardiograms with severe mitral regurgitation were excluded. Results: We identified and matched 840 ECGs from non-obstructive HCM patients (n=125, age 58±19y, 70% male), 145 ECGs from obstructive HCM patients (n=29, age 56±18y, 66% male), 225 ECGs from apical HCM patients (n=33, age 66±14y, 61% male), and 382 ECGs from cardiac amyloidosis patients (n=49, age 70±13y, 57% male). The AI-enabled ECG model predicted diastolic dysfunction with an accuracy of 65% (non-obstructive HCM), 67% (obstructive HCM), 65% (apical HCM), and 75% (cardiac amyloidosis), with sensitivities of 94% and 97% for obstructive HCM and cardiac amyloidosis, respectively, and high positive and negative predictive values for cardiac amyloidosis. Systolic dysfunction (LVEF≤40%) was more common in cardiac amyloidosis (29%) than in all HCM patients (5%). Conclusion: The AI-enabled ECG model demonstrates moderate accuracy but high sensitivity for detecting diastolic dysfunction and increased filling pressure, especially in obstructive HCM and cardiac amyloidosis. The AI-enabled ECG is a useful, widely scalable, and low cost tool to identify patients at increased risk for diastolic dysfunction.

Introduction: Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (LVEF) on cardiac imaging. Cost-effective alternatives to cardiac imaging are currently not available. Artificial intelligence (AI) models have been developed to detect a reduced LVEF from 12-lead electrocardiograms (ECGs) in the general population. Hypothesis: AI-ECG model can detect a reduced LVEF after anthracycline therapy (i.e. cardiotoxicity). Methods: We included an unbiased oncological population of 989 female breast cancer patients receiving (neo)adjuvant anthracycline chemotherapy. Average follow-up time was 9.83±4.2 years. Patients with incomplete transthoracic echocardiography (TTE) data, HER-2-directed therapy, significant comorbidities, upfront metastatic disease, second primary malignancy or pre-existing cardiovascular disease were excluded from the analyses. Patients with LVEF decline for reasons other than anthracycline-induced cardiotoxicity were also excluded. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for various levels of LVEF <50%. Results: Using 1,877 ECGs from 703 unique patients, the AI-ECG model detected an LVEF <50% and ≤35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively. Conclusions: These data support the use of AI-ECG for cardiomyopathy (cardiotoxicity) screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly testing and could enable patients to monitor themselves over long periods of time.

Abstract Aims Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (EF). Early recognition and treatment have been advocated, but robust, convenient, and cost-effective alternatives to cardiac imaging are missing. Recent developments in artificial intelligence (AI) techniques applied to electrocardiograms (ECGs) may fill this gap, but no study so far has demonstrated its merit for the detection of an abnormal EF after anthracycline therapy. Methods and results Single centre consecutive cohort study of all breast cancer patients with ECG and transthoracic echocardiography (TTE) evaluation before and after (neo)adjuvant anthracycline chemotherapy. Patients with HER2-directed therapy, metastatic disease, second primary malignancy, or pre-existing cardiovascular disease were excluded from the analyses as were patients with EF decline for reasons other than anthracycline-induced cardiotoxicity. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for EFs &lt; 50%. Of 989 consecutive female breast cancer patients, 22 developed a decline in EF attributed to anthracycline therapy over a follow-up time of 9.8 ± 4.2 years. After exclusion of patients who did not have ECGs within 90 days of a TTE, 20 cases and 683 controls remained. The AI-ECG model detected an EF &lt; 50% and ≤ 35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively. Conclusion These data support the use of AI-ECG for cardiotoxicity screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly cardiac imaging and could enable patients to monitor themselves over long periods of time.

Background: Anthracyclines are a class of highly effective chemotherapeutics, yet their use is regularly complicated by anthracycline-induced cardiotoxicity (AIC). The advent of induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs) offers the potential for disease modeling and prediction of genetic cardiomyopathies. Purpose: We aim to use iPSC-CMs to assess susceptibility to AIC. Methods: Five AIC heart transplant patients and five anthracycline treated patients without AIC were included in our study. We reprogrammed lymphocytes from both groups toward iPSCs using Sendai Virus, followed by differentiation into CM-like cells. Cytotoxicity following chemotherapy exposure was assessed using flow cytometry (FC), electron microscopy (EM) and reactive oxygen species generation (ROS). Furthermore, whole genome sequencing (WGS) was performed and variants were filtered based on allele frequency, pathogenicity using HumVar trained variant effect predictors and presence in patients versus controls. Results: The cytotoxic effect of increasing concentrations of doxorubicin was quantifiable using FC on SIRPA positive iPSC-CMs ( A ). There was a higher rate of cell death at any concentration of doxorubicin in patients with AIC as opposed to controls ( B ). On the ultrastructural level, doxorubicin treated iPSC-CMs manifested darker and more condensed mitochondria as seen with EM ( C ). Increased ROS production was observed following doxorubicin exposure ( D ). WGS analysis showed 2 pathogenic variants in the PRAMEF6 gene present in cases and not in controls ( E ). Conclusion: iPSC-CMs represent a robust disease model to study mechanisms of and susceptibility to AIC.

Background Treatment for breast cancer (BC) frequently involves radiotherapy. Guidelines recommend screening for cardiac adverse events starting 10 years after radiotherapy. The rationale for this interval is unclear. Methods and Results We aimed to study cardiovascular event rates in the first decade following curative radiotherapy for BC. We compared mortality and cardiovascular event rates with an age‐ and risk factor‐matched control population. We included 1095 patients with BC (mean age 56±12 years). Two hundred and eighteen (19.9%) women died. Cancer and cardiovascular mortality caused 107 (49.1%) and 22 (10.1%) deaths, respectively. A total of 904 cases were matched to female FLEMENGHO (Flemish Study on Environment, Genes and Health Outcomes) participants. Coronary artery disease incidence was similar (risk ratio [RR], 0.75 [95% CI, 0.48–1.18]), yet heart failure (RR, 1.97 [95% CI, 1.19–3.25]) and atrial fibrillation/flutter (RR, 1.82 [95% CI, 1.07–3.08]) occurred more often in patients with BC. Age (hazard ratio [HR], 1.033 [95% CI, 1.006–1.061], P =0.016), tumor grade (HR, 1.739 [95% CI, 1.166–2.591], P =0.007), and neoadjuvant treatment setting (HR, 2.782 [95% CI, 1.304–5.936], P =0.008) were risk factors for mortality. Risk factors for major adverse cardiac events were age (HR, 1.053 [95% CI, 1.013–1.093]; P =0.008), mean heart dose (HR, 1.093 [95% CI, 1.025–1.167]; P =0.007), history of cardiovascular disease (HR, 2.386 [95% CI, 1.096–6.197]; P =0.029) and Mayo Clinic Cardiotoxicity Risk Score (HR, 2.664 [95% CI, 1.625–4.367]; P <0.001). Conclusions Ten‐year mortality following curative treatment for unilateral BC was mainly cancer related, but heart failure and atrial fibrillation/flutter were already common in the first decade following irradiation. Mean heart dose, pre‐existing cardiovascular diseases, and Mayo Clinic Cardiotoxicity Risk Score were risk factors for cardiac adverse events. These results suggest a need for early dedicated cardio‐oncological follow‐up after radiotherapy.