Citations

Abstract Background Atrial fibrillation (AF) is diagnosed in up to 50% of patients with heart failure. However, the prevalence of AF among patients with peripartum cardiomyopathy (PPCM) ranges from only 2–10%, with the lowest rates in Black women. An artificial intelligence enhanced electrocardiogram (AI-ECG) has previously been shown to be effective in detecting AF while in sinus rhythm, and for AF risk prediction in a population-based study. Purpose Our objective was to evaluate the use of an AI-ECG for AF risk stratification among women of reproductive age (18 to 49 years) with PPCM compared to other forms of cardiomyopathy. Methods We identified 59 reproductive age women with a diagnosis of PPCM between January 2007, and October 2018 and included matched controls in a 3:1 fashion. Matching was performed based on sex, age, race, and left ventricular ejection fraction. We excluded patients with a diagnosis of AF prior to cardiomyopathy diagnosis date. AI-ECG prediction probabilities were generated for ECGs performed within a 30-day window prior to the patient's first cardiomyopathy diagnosis date for the entire study cohort. Results A total of 236 patients were included in the final analysis (59 cases, 177 controls). Overall, the median age at cardiomyopathy diagnosis was 31.7 years (IQR: 18.5, 49.4), 76.3% were White, 8.5% were Black, and 15.3% represented other or unknown race. Over the period studied, 3.4% of women with PPCM developed AF compared to 5.6% of women with other cardiomyopathies. The frequency of positive AI-ECG predictions for AF was more common among women with other cardiomyopathies (40.7%) compared to women with PPCM (20.3%). The predicted odds ratio for AF development following a cardiomyopathy diagnosis based on AI-ECG results was 0.37 (95% CI: 0.18, 0.73) for PPCM compared to other cardiomyopathies (p=0.006). Conclusion We demonstrated that an AI-ECG model for AF prediction may play a potential role in arrhythmia risk stratification/prediction among young women with PPCM who have a demonstrable lower risk for AF compared to women with other cardiomyopathies. Mechanisms for lower AF risk among patients with PPCM remain unknown. Further studies evaluating mechanistic pathways will be essential. Funding Acknowledgement Type of funding sources: None.

Abstract Background Screening for left ventricular (LV) systolic dysfunction (defined as ejection fraction ≤35%) based on data from a standard 12-lead electrocardiogram (ECG) has become well established when standard digital ECGs are available–8 independent leads sampled at least 250 hertz for 10 seconds. As the algorithm has been incorporated into various clinical scenarios and ancillary research projects, a limitation of the binary classification at 35% has become apparent. Purpose The objective of this study was to develop and validate a deep learning-based algorithm that would classify LVEF into three categories based on only the digital ECG input. Methods After IRB approval, native digital resting ECGs acquired between 1/1/2010 and 12/31/2021 on patients seen in Mayo Clinic in Jacksonville were extracted from the institutional electronic ECG database management system (MUSE, GE Healthcare). These ECGs were matched with transthoracic echocardiograms obtained up to four days prior or 30 days after the ECGs acquisition. A convolutional neural network consisting of 8 layers of convolutions, batch normalization and pooling was trained using Keras and Tensorflow with hyper-parameter optimization for L1 and L2 regularization, learning rate adjustments, and class weights to predict three classes of LVEF: ≤35%, 36–51%, and ≥52% based on clinical relevance. The primary measure of overall performance was the detection of LVEF ≤35%; however, the triad of model predictions was also considered in translating the model output to human interpretable findings. Results A total of 30,153 patients (60,169 ECG pairings; mean age 63 years; 48% male) were randomly split at the patient level into training (24,172 patients), validation (2,973 patients) and testing (3,008 patients). The trained model provided robust discrimination in the withheld testing data – AUROC of 0.941 (95% CI: 0.931 to 0.950). Using the optimal model threshold based on Youden's index from the validation data (0.186), sensitivity and specificity were estimated to be 87.9% (95% CI: 83.8% to 91.2%) and 86.3% (95% CI: 85.4% to 87.2%) in the testing data. In instances where discordant predictions were observed, the posterior distribution of model probabilities provide additional insights into the possible underlying value of LVEF (Figure 1). Conclusions The utilization of a multi-category deep learning classification model for the detection of reduced ejection fraction adds new dimensions to the use of AI technologies on digital ECGs. This work shows high discrimination can still be obtained when using three classes of LVEF. Funding Acknowledgement Type of funding sources: None.

Abstract Background Current approaches utilizing non-invasive methods to screen for cardiac allograft rejection (gene expression profiling and cell free DNA) have yet to be broadly integrated into heart transplant management and have shown limited discrimination (AUCs of 0.68 and 0.77, respectively). Changes in the electrocardiogram (ECG) have been reported at the time of severe cardiac rejection, including low voltages and conduction abnormalities. It remains unknown if subtle ECG changes correlating with cardiac allograft rejection can be detected earlier using machine learning methods. Purpose We sought to develop an artificial intelligence (AI) model to detect cardiac allograft rejection based on the 12 lead ECG. Methods We identified all patients who underwent a heart transplant at 3 hospital sites within a single health system from Jan 1998 through Apr 2021 and extracted digital 12-lead ECG data as well as endomyocardial biopsy pathology results from the electronic medical record. We partitioned our data into a training (80%), validation (10%), and test set (10%) based on a group stratification sampling. Each patient was present in only one set and each set had a positivity rate of 2.6% with 6,074/758/758 ECGs belonging to 1,146/140/141 unique patients in each set respectively. Cardiac allograft rejection was defined as moderate or severe acute cellular rejection based on International Society for Heart and Lung Transplantation (ISHLT) guidelines. A convolutional neural network, using the 12-lead ECG data as input, was trained with hyperparameter optimization for regularization, learning rate adjustments, and class weights. Model performance metrics were based on the test data and estimated using the final model architecture. Results 1,587 heart transplant recipients who had at least one endomyocardial biopsy were evaluated for inclusion. We limited our sample to ECGs performed within 30 days of the biopsy date (7,590 ECGs, representing 1,425 unique patients). Our study population had a median age of 55.8 years and 28.7% were female. The median number of ECG-biopsy pairs per patient was 5. The majority of endomyocardial biopsy results were classified as none or mild rejection (97.1%), and 2.9% had moderate/severe rejection. The ECG-based AI model detected cardiac allograft rejection with an area under the receiver operative curve (AUC) of 0.84 in the test set. The sensitivity, specificity, positive and negative predictive values were 95%, 52.6%. 5.2% and 99.7% respectively. Conclusions An AI-ECG model appears to outperform novel non-invasive laboratory tests (gene expression profiling and cell free DNA) for detecting cardiac allograft rejection and does not require a blood draw or the additional complexities surrounding sample processing. This model relies on a readily available and relatively inexpensive test, the ECG. In addition, AI predictions can be made available within a few minutes following ECG acquisition. Funding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic

Background: Transcatheter aortic valve replacement (TAVR) is now an approved alternative to surgical aortic valve replacement for the treatment of severe aortic stenosis. As the clinical adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to evaluate the impact of sex differences in aortic valve calcium score (AVCS) on long-term mortality following TAVR in a large patient sample. Methods: We included consecutive patients who successfully underwent TAVR for treatment of severe native aortic valve stenosis from June 2010 to May 2021 across all US Mayo Clinic sites with follow-up through July 2021. AVCS values were obtained from preoperative computed tomography of the chest. Additional clinical data were abstracted from medical records. Kaplan-Meier curves and Cox-proportional hazard regression models were employed to evaluate the effect of AVCS on long-term mortality. Results: A total of 2543 patients were evaluated in the final analysis. Forty-one percent were women, median age was 82 years (Q1: 76, Q3: 86), 18.4% received a permanent pacemaker following TAVR, and 88.5% received a balloon expandable valve. We demonstrate an increase in mortality risk with higher AVCS after multivariable adjustment ( P <0.001). When stratified by sex, every 500-unit increase in AVCS was associated with a 7% increase in mortality risk among women (adjusted hazard ratio, 1.07 [95% CI, 1.02–1.12]) but not in men. Conclusions: We demonstrate a notable sex difference in the association between AVCS and long-term mortality in a large TAVR patient sample. This study highlights the potential value of AVCS in preprocedural risk stratification, specifically among women undergoing TAVR. Additional studies are needed to validate this finding.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Introduction: Conduction disturbance after transcatheter aortic valve replacement (TAVR) remains a common complication. Predictive tools to identify conduction disturbance requiring pacing are limited. Our main objective was to develop a deep learning model to predict the need for a permanent pacemaker. Hypothesis: An electrocardiogram (ECG) based deep learning algorithm can predict the need for permanent pacemaker post TAVR. Methods: We evaluated patients who had TAVR at Mayo Clinic between June 2010 and May 2021. Digital ECGs done on the TAVR date or within 24 hours post procedure were partitioned into training, validation, and testing datasets. We trained a deep learning algorithm on the training data and optimized hyperparameters against the validation data. All model performance metrics are based on the reserved test data that were estimated once model architecture was optimized. Need for permanent pacemaker, defined as pacemaker placement due to development of conduction disturbance post TAVR and at least 1% ventricular pacing on device interrogation done at least 2 weeks post pacemaker implantation, was the training target. Results: A total of 3021 patients were evaluated for inclusion and 2,407 cases were available for modeling (exclusions: prior pacemaker (n=419), unsuccessful TAVR (n=27) and no ECG within the pre-specified period (n=168)). Overall, the median age was 82 (Q1: 76, Q3: 86) and 41% were female. In the withheld test data, 15% had a permanent pacemaker with at least 1% ventricular pacing. The algorithm detected the need for pacemaker in the test set with an AUC of 0.83 (95% CI: 0.73 to 0.92). Accuracy, sensitivity, specificity, positive and negative predictive were 80%, 72%, 81%, 41% and 94%, respectively. Conclusions: This study demonstrates the ability of a deep learning model based on ECG data to assist in making the decision to implant a pacemaker following TAVR with a high negative predictive value. Larger validation studies are warranted.

Background: Transcatheter aortic valve replacement (TAVR) is an approved alternative to surgical aortic valve replacement. However, as the clinical indication and adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to study the impact of aortic valve calcium score on long term mortality following TAVR in a large patient population. Methods: We included consecutive patients who successfully underwent TAVR from June 2010 – June 2019 across all Mayo Clinic sites. Patients were followed through February 2021. Aortic valve calcium scores were obtained from preoperative computed tomography angiography of the chest. Additional data was abstracted from medical records. Cox-proportional hazard regression models were employed to evaluate the independent effect of aortic valve calcium score on mortality. Results: A total of 1988 patients were evaluated in the final analysis. Forty-two percent were women, median age was 83 years (Q1: 77, Q3: 87), 16% received a permanent pacemaker following TAVR and 83% received an Edwards Sapien valve. We demonstrate an incremental increase in mortality risk as aortic valve calcium score values increased after adjusting for age, gender, pacemaker implantation, left bundle branch block, and Charlson Comorbidity Index. Selected calcium score cut-points with adjusted hazard ratios (aHR) are displayed: 1200 AU (aHR: 1.13, 95% CI 1.06, 1.20); 2000 AU (aHR: 1.22, 95% CI 1.09, 1.35); 3000 AU (aHR: 1.34, 95% CI: 1.14, 1.58). Conclusion: This study demonstrates an association between aortic valve calcium score and long-term mortality in a large patient population. This highlights the potential predictive value of the calcium score in preoperative planning and may provide clinicians with an additional measure for risk stratification prior to TAVR.

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.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the 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. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, &lt;45% and &lt;50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF &lt;45% and 0.87 for LVEF &lt;50%. For LVEF ≤35%, 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 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.