Adedinsewo, Demilade A

Abstract 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.

AbstractBackgroundChange in left atrial pressure (LAP) has been shown to be associated with symptom improvement post‐MitraClip; however, the association between acute procedural changes in transmitral diastolic mean gradient (MG) compared to LAP and symptom improvement is not well established.Methods164 consecutive patients undergoing MitraClip at Mayo Clinic between June 2014 and May 2018 were included. Preclip and postclip MG and LAP were recorded. Baseline demographics, clinical, and echocardiographic outcomes, including 30‐day New York Heart Association (NYHA) functional status were obtained from patient charts.ResultsMedian age was 81.5 years (IQR: 76.3, 87), 34% were female and 94.5% had NYHA class III and IV functional status at baseline. At baseline, median MG was 4 mmHg (IQR: 3, 5) and LAP was 19 mmHg (IQR: 16, 23.5). Following MitraClip deployment, the median MG was 4 mmHg (IQR: 3, 6) and the median LAP was 17 mmHg (IQR: 14, 21), 69.5% of patients had less than moderate MR. There was no statistically significant association between change in MG and NYHA functional class at 30 days (OR = 0.95, 95% CI: 0.76–1.20). However, a reduction in LAP following MitraClip deployment was significantly associated with improvement in NYHA functional status at 30 days following adjustments for age and sex (aOR 3.36, 95% CI: 1.34–8.65). There was no significant correlation between change in mean LAP and change in MG (p = .98).ConclusionUnlike change in left atrial pressure, change in MG post‐MitraClip was not associated with patient reported outcomes at 30 days and did not correlate with change in left atrial pressure. Long‐term follow up is needed to evaluate the impact of LA pressure on symptoms.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.