Morales-Lara, C A

Abstract Background Pregnancy related cardiomyopathy is a significant cause of maternal morbidity and mortality globally. A presumed overlap between normal pregnancy-associated symptoms and clinical symptoms of cardiomyopathy contributes to delays in diagnosis and increased risk of maternal mortality. Purpose We sought to evaluate the association between patient-reported cardiovascular symptoms and the presence of cardiomyopathy among pregnant and postpartum patients. We hypothesize that individual cardiovascular symptoms are unrelated to the presence of cardiomyopathy. We also evaluated the use of a novel adaptation of a validated health status questionnaire in relation to cardiomyopathy. Methods We enrolled 48 pregnant (>13 weeks) and postpartum (up to 12 months) participants in a prospective study between October 2021 and February 2022. All study participants completed a baseline questionnaire, which included current cardiovascular symptoms, an assessment of health status using an adapted version of the Kansas City Cardiomyopathy Questionnaire (KCCQ-12), followed by a resting transthoracic echocardiogram on the same day. We defined cardiomyopathy as a left ventricular ejection fraction (LVEF) <50% based on 2-D echocardiography. Fisher's exact and Wilcoxon rank-sum tests were employed to evaluate the association between reported cardiovascular symptoms, the adapted KCCQ-12 (KCC-A) score, and cardiomyopathy. Results At the time of enrollment, 67% were pregnant and 33% postpartum. Forty-eight percent identified as White, 31% as Black, 10% as Asian, and 10% as other race. The median age was 31 years (Q1: 28, Q3: 35) and 6% had an LVEF <50%. We found no statistically significant association between four reported cardiovascular symptoms (shortness of breath, orthopnea, fast breathing, and episodes of “asthma” that did not improve with inhalers or other treatment) and cardiomyopathy or medial E/e' ratio. KCC-A scores were low in the study population overall (median 52; Q1:40, Q3: 61). We demonstrated a significantly lower KCC-A score among women with LVEF <50% (median 24; Q1: 15, Q3: 44) compared to women with LVEF ≥50% (median 54; Q1: 44, Q3: 61) p=0.02. Conclusions We showed no significant association between individual cardiovascular symptoms and cardiomyopathy in an obstetric population. However, we demonstrate for the first time that an adapted KCCQ-12 questionnaire for health status assessment could potentially identify women with a high-likelihood of cardiomyopathy during the peripartum period who may benefit from additional evaluation including echocardiography. Larger studies are needed to validate this finding. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This study was funded by a research grant from the Miami Heart Research Institute, Florida Heart Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

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