Young, Kendra A.

Abstract Background Cardiomyopathy is a leading cause of pregnancy-related mortality and represents the number one cause of death in the late postpartum period. Purpose The goal of this study was to prospectively evaluate the effectiveness of an artificial intelligence-based electrocardiogram (AI-ECG) and a point-of-care artificial intelligence-enhanced digital stethoscope for cardiomyopathy screening among pregnant and postpartum women. Methods We conducted a single-arm prospective study of pregnant and postpartum women (up to 12 months following delivery) enrolled at three sites between October 2021 and October 2022. Study participants completed a standard 12-lead ECG, ECG + phonocardiogram recordings with a digital stethoscope, a demographic questionnaire, and a transthoracic echocardiogram (TTE) within a 24-hour period. Diagnostic performance of the AI-ECG and digital stethoscope for cardiomyopathy detection, defined as left ventricular ejection fraction (LVEF) < 45%, was evaluated using the area under the curve (AUC) and standard measures of diagnostic performance. Results We included 100 consecutive pregnant and postpartum women who provided informed consent and had at least one ECG recorded, completed the questionnaire, and had an echocardiogram performed. All participants self-identified as female, and 41% were recruited from a federally qualified health center. The median age of participants was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, 21% as Hispanic or Latino, 6% as Asian, and 3% as Other (1 Native Hawaiian and 2 multiracial). Five percent (5/100) had LVEF <45%. The 12-lead AI-ECG model demonstrated close to perfect discrimination (accuracy 99%; AUC 1.00, sensitivity 100%) for detection of cardiomyopathy with 1 false positive screen (LVEF 49%), Figure 1A. The digital stethoscope (angled position) had an AUC of 0.99 (95% CI: 0.97, 1.00) for detection of LVEF <45%. Sensitivity, specificity, positive predictive value, and negative predictive value for the digital stethoscope were 60%, 98%, 60% and 98%, respectively for detection of LVEF <45% (Figure 1B & 1C). Conclusions In this small prospective study, we found that 5% of pregnant and postpartum women had cardiomyopathy, a potentially life-threatening condition, and that a rapid, inexpensive, ubiquitous, massively scalable point-of-care test - the ECG - showed promising results, identifying it with an AUC > 0.98 with the application of artificial intelligence.Figure 1

Abstract Background Across complex traits, common variants explain only a modest amount of variance, with SNP-heritability consistently below heritability estimates from close relatives. Here, we examined the contribution of rare variant to tobacco use risk in up to 26,000 individuals of European ancestry in the Trans-Omics for Precision Medicine (TOPMed) program with whole genome sequence (WGS;~30X coverage).Method We grouped about 35 million genetic variants by their minor allele frequencies (MAF) and linkage disequilibrium (LD) and estimated SNP-heritability for age of smoking initiation (N = 14,747), cigarettes smoked per day (N = 15,425), smoking cessation (N = 17,871) and initiation (N = 26,340) using linear mixed model. Rare variant population structure is detected and adjusted for by permutation procedure. We estimated an upper bound for narrow-sense heritability for tobacco use using available pedigrees consisting of close relatives in TOPMed.Results Rare variants with MAF 0.1–0.01%, mostly from non-protein altering region, accounted for 26% of variation in age of initiation and 15% for cessation. Follow-up analysis indicated that about one-third of these rare variants contribtion is potentially confounded with rare variants structure even after adjusting for principal components. After further conservative adjustment of population structure, we estimated SNP-based heritability to be 0.21 (SE = 0.08) for age of initiation, 0.15 (0.06) for cigarettes per day, 0.21 (0.09) for cessation, and 0.24 (0.07) for initiation, 1.8–4.5 times higher than previous SNP-based estimates. Our pedigree-based upper-bound for SNP-based heritability ranged from 0.18–0.35.Conclusion The substantial contribution of rare variants for several smoking phenotypes sheds light on the missing heritability and genetic etiology of tobacco use. It also informs fine-mapping strategies since the majority of the rare variant contribution was located in non-coding regulatory regions.

ObjectiveSystemic lupus erythematosus (SLE) and other autoimmune diseases cause significant morbidity. Identifying populations at risk of developing SLE is essential for curtailing irreversible inflammatory damage. The aim of this study was to identify factors associated with transition to classified disease that would inform our understanding of the risk of SLE.MethodsPreviously identified blood relatives of patients with SLE, who had <4 American College of Rheumatology (ACR) classification criteria for SLE at baseline, were enrolled in this follow‐up study (n = 409 unaffected relatives). Participants provided detailed family, demographic, and clinical information, including the SLE‐specific portion of the Connective Tissue Disease Screening Questionnaire (SLE‐CSQ). Serum and plasma samples were tested for the presence of lupus‐associated autoantibodies and 52 soluble mediators. Generalized estimating equations (GEEs) were applied to identify factors predictive of disease transition.ResultsOf the 409 unaffected relatives of SLE patients, 45 (11%) had transitioned to classified SLE at follow‐up (mean time to follow‐up 6.4 years). Relatives who transitioned to SLE displayed more lupus‐associated autoantibody specificities and higher SLE‐CSQ scores (P < 0.0001) at baseline than did relatives who did not transition. Importantly, those who had developed SLE during the follow‐up period also had elevated baseline plasma levels of inflammatory mediators, including B lymphocyte stimulator, stem cell factor (SCF), and interferon‐associated chemokines (P ≤ 0.02), with concurrent decreases in the levels of regulatory mediators, transforming growth factor β (TGFβ), and interleukin‐10 (P ≤ 0.03). GEE analyses revealed that baseline SLE‐CSQ scores or ACR scores (number of ACR criteria satisfied) and plasma levels of SCF and TGFβ, but not autoantibodies, were significant and independent predictors of SLE transition (P ≤ 0.03).ConclusionPreclinical alterations in levels of soluble mediators may predict transition to classified disease in relatives of SLE patients. Thus, immune perturbations precede SLE classification and can help identify high‐risk relatives for rheumatology referral and potential enrollment in prevention trials.