Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.

Cardiac defects of hypermobile Ehlers-Danlos syndrome and hypermobility spectrum disorders: a retrospective cohort study.

Background: Defective connective tissue structure may cause individuals with hypermobile Ehlers-Danlos syndrome (hEDS) or hypermobility spectrum disorders (HSD) to develop cardiac defects. Methods: We conducted a retrospective chart review of adult patients treated in the EDS Clinic from November 1, 2019, to June 20, 2022 to identify those with cardiac defects. Echocardiogram data were collected using a data collection service. All EDS Clinic patients were evaluated by a single physician and diagnosed according to the 2017 EDS diagnostic criteria. Patient demographic, family and cardiac history were extracted from self-reported responses from a REDCap clinical intake questionnaire. Patients with at least 1 available echocardiogram (ECHO) were selected for the study (n = 568). Results: The prevalence of aortic root dilation in patients with hEDS was 2.7% and for HSD was 0.6%, with larger measurements for males than females and with age. Based on self-reported cardiac history that was verified from the medical record, patients with hEDS with bradycardia (p = 0.034) or brain aneurysm (p = 0.015) had a significantly larger average adult aortic root z-score. In contrast, patients with HSD that self-reported dysautonomia (p = 0.019) had a significantly larger average aortic root z-score. The prevalence of diagnosed mitral valve prolapse in patients with hEDS was 3.5% and HSD was 1.8%. Variants of uncertain significance were identified in 16 of 84 patients that received genetic testing based on family history. Conclusions: These data reveal a low prevalence of cardiac defects in a large cohort of well-characterized hEDS and HSD patients. Differences in cardiovascular issues were not observed between patients with hEDS vs. HSD; and our findings suggest that cardiac defects in patients with hEDS or HSD are similar to the general population.

Screening for peripartum cardiomyopathies using artificial intelligence in Nigeria (SPEC-AI Nigeria): Clinical trial rationale and design.

BACKGROUND: Artificial intelligence (AI), and more specifically deep learning, models have demonstrated the potential to augment physician diagnostic capabilities and improve cardiovascular health if incorporated into routine clinical practice. However, many of these tools are yet to be evaluated prospectively in the setting of a rigorous clinical trial-a critical step prior to implementing broadly in routine clinical practice. OBJECTIVES: To describe the rationale and design of a proposed clinical trial aimed at evaluating an AI-enabled electrocardiogram (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria. DESIGN: The protocol will enroll 1,000 pregnant and postpartum women who reside in Nigeria in a prospective randomized clinical trial. Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. Women aged 18 and older, seen for routine obstetric care at 6 sites (2 Northern and 4 Southern) in Nigeria will be included. Participants will be randomized to the study intervention or control arm in a 1:1 fashion. This study aims to enroll participants representative of the general obstetric population at each site. The primary outcome is a new diagnosis of cardiomyopathy, defined as left ventricular ejection fraction (LVEF) < 50% during pregnancy or within 12 months postpartum. Secondary outcomes will include the detection of impaired left ventricular function (at different LVEF cut-offs), and exploratory outcomes will include the effectiveness of AI-ECG tools for cardiomyopathy detection, new diagnosis of cardiovascular disease, and the development of composite adverse maternal cardiovascular outcomes. SUMMARY: This clinical trial focuses on the emerging field of cardio-obstetrics and will serve as foundational data for the use of AI-ECG tools in an obstetric population in Nigeria. This study will gather essential data regarding the utility of the AI-ECG for cardiomyopathy detection in a predominantly Black population of women and pave the way for clinical implementation of these models in routine practice. TRIAL REGISTRATION: Clinicaltrials.gov: NCT05438576.

Atrial Arrhythmia Ablation in Patients With D-Transposition of the Great Arteries and Atrial Switch.

BACKGROUND: Patients with D-transposition of the great arteries and atrial switch have a high incidence of atrial arrhythmias. We sought to analyze the arrhythmia substrate, ablation strategies, and outcomes for catheter ablation in this population. METHODS: An in-depth analysis of all clinical and procedural data in patients with D-transposition of the great arteries, atrial baffles, and atrial arrhythmia ablation was performed. RESULTS: A cohort of 32 patients (72% male, mean age 38±7 years) underwent ablation for non-AV nodal reentrant tachycardia atrial arrhythmias, and 4 patients underwent AV nodal reentrant tachycardia ablation. Cavotricuspid isthmus flutter (CTI-flutter) was the most common arrhythmia, encountered in 75% of patients, followed by scar-related intraatrial reentrant tachycardia (non-CTI intraatrial reentrant tachycardia, 53%) and focal atrial tachycardia (focal atrial tachycardia, 6%). Among the 32 patients, 26 underwent 31 procedures at our institution. For patients with prior outside intervention, the index ablation at our institution revealed CTI-dependent flutter in 3/5 cases. However, redo ablation after an index ablation with demonstrated bidirectional CTI block revealed different/new arrhythmia substrates (80% non-CTI intraatrial reentrant tachycardia, 40% focal atrial tachycardia). Achieving bidirectional block across the CTI often required ablating on both sides of the baffle (retroaortic access, 81%; using a baffle leak, 11.5%; or transbaffle puncture, 7.7%). Combined approaches were necessary in 19% to reach the critical tissue. Acute procedural success was 81%, and recurrence was documented in 58% of patients. Despite recurrence, clinical arrhythmia burden was significantly reduced post-ablation (P<0.001), with rare episodes, amenable to antiarrhythmic therapy. Redo ablation was required in 5 (19%) patients and uncovered new arrhythmia substrates. AV nodal reentrant tachycardia ablation also required transbaffle approaches in 3/4 patients. CONCLUSIONS: CTI-dependent flutter was the most common arrhythmia in patients with Dextro-Transposition of the Great Arteries and atrial switch. Transbaffle approaches were often necessary, and, provided that bidirectional CTI block was achieved at the index ablation, late recurrence was due to different arrhythmia mechanisms. Despite recurrence, ablation was associated with significant clinical improvement.

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model.

AIMS: Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. METHODS AND RESULTS: We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, <45%, and <50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively. CONCLUSIONS: An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

Effect of Earlier Atrioventricular Valve Intervention on Survival After the Fontan Operation.

Whereas the prevalence and impact of atrioventricular valve (AVV) regurgitation in patients with single ventricle physiology has become increasingly apparent, the optimal timing for valve intervention is unclear. To investigate this, we performed a retrospective review of all 1,167 patients from the Mayo Clinic Fontan database. Thirteen percent (153 patients) had AVV repair or replacement during their staged single ventricle palliation. We found that patients with right ventricular morphology and common AVV were at increased risk for AVV intervention. Patients who underwent AVV intervention had increased risk of death/transplant compared with those who did not (hazards ratio [HR] = 1.75, 95% CI 1.37 to 2.23, p <0.001). With respect to valve intervention timing, whereas AVV intervention before Fontan presented similar risk for death/transplant compared with no AVV intervention (HR = 0.85, 95% CI 0.32 to 2.27, p = 0.74), intervention at time of Fontan had a significantly higher risk (HR = 1.46, 95% CI 1.09 to 1.97, p = 0.01), and intervention after Fontan had a much more substantial risk (HR = 3.83, 95% CI 2.54 to 5.79, p <0.001). AVV repair failure occurred in 11% of patients. In terms of relative risk of valve repair versus replacement, in post-Fontan AVV intervention patients, AVV replacement carried a 2.9 fold risk of death/transplant compared with AVV repair. In conclusion, AVV disease remains a considerable challenge for durable Fontan physiology. This data demonstrates that earlier intervention on valve pathology improves survival with the Fontan circulation. Continued surveillance of single ventricle patients and prompt referral of those with valve pathology can improve outcomes in this challenging population.

Determinants of Sudden Cardiac Death in Adult Patients With Eisenmenger Syndrome.

Background Patients with Eisenmenger syndrome are known to have a high incidence of sudden cardiac death (SCD), yet the underlying causes are not well understood. We sought to define the predictors of SCD in this population. Methods and Results A retrospective analysis of all patients with Eisenmenger syndrome from 2 large tertiary referral centers was performed. ECGs, prolonged ambulatory recordings, echocardiograms, and clinical histories were reviewed; and the cause of death was identified. A total of 246 patients (85 [34.6%] men) with a mean age of 37.3 (±14.2) years were followed up for a median of 7 years. Over the study period, 136 patients died, with 40 experiencing SCD and 74 experiencing cardiac death (sudden and nonsudden). Age, atrial fibrillation, prolonged QRS duration, complete heart block, right atrial enlargement, right bundle branch block, increased right atrial pressure, impaired biventricular function, and the presence of a pacemaker were associated with increased risk of SCD, whereas advanced pulmonary hypertension therapies were protective. Atrial fibrillation (11.45-fold increased risk; P<0.001) and QRS duration ≥120 ms (2.06-fold increased risk; P=0.034) remained significant predictors of SCD in the multivariate analysis, whereas advanced pulmonary hypertension therapies were strongly protective against SCD (P<0.001). Conclusions Atrial arrhythmias, impaired ventricular function, and conduction system disease were associated with increased risk of SCD in this cohort of patients with Eisenmenger syndrome, providing an opportunity for early risk stratification and potential intervention. Clinical heart failure symptoms (New York Heart Association class ≥II) were predictive of increased mortality but not of SCD, suggesting a potential arrhythmic cause behind SCD.