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BackgroundAtrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new onset AF could be predicted, targeted population screening could be used to find it early. We hypothesized that a deep neural network could predict new onset AF from the resting 12-lead electrocardiogram (ECG) and that this prediction may help prevent AF-related stroke.MethodsWe used 1.6M resting 12-lead ECG voltage-time traces from 430k patients collected from 1984-2019 in this study. Deep neural networks were trained to predict new onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC). We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs prior to 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We used standard metrics to explore different prediction thresholds for the model and also calculated how many AF-related strokes might be potentially prevented.ResultsThe AUROC and AUPRC were 0.83 and 0.21, respectively, for predicting new onset AF within 1 year of an ECG. Adding age and sex improved the AUROC to 0.85 and the AUPRC to 0.23. The hazard ratio for the predicted high- vs. low-risk groups over a 30-year span was 7.2 [95% confidence interval: 6.9 – 7.6]. In a simulated deployment scenario, using the F2 score to select the risk prediction threshold, the model predicted new onset AF at 1 year with a sensitivity of 69%, specificity of 81%, and positive predictive value (PPV) of 12%. This model correctly predicted new onset AF in 62% of all patients that experienced an AF-related stroke within 3 years of the ECG.ConclusionsDeep learning can predict new onset AF from the 12-lead ECG in patients with no prior history of AF. This prediction may prove useful in preventing AF-related strokes.

Type 2 diabetes mellitus (T2DM) is a major health and economic burden because of the seriousness of the disease and its complications. Improvements in short- and long-term glycemic control is the goal of diabetes treatment. To investigate the longitudinal management of T2DM at Geisinger, we interrogated the electronic health record (EHR) information and identified a T2DM cohort including 125,477 patients using the Electronic Medical Records and Genomics Network (eMERGE) T2DM phenotyping algorithm. We investigated the annual anti-diabetic medication usage and the overall glycemic control using hemoglobin A1c (HbA1c). Metformin remains the most frequently medication despite the availability of the new classes of anti-diabetic medications. Median value of HbA1c decreased to 7% in 2002 and since remained stable, indicating a good glycemic management in Geisinger population. Using metformin as a pilot study, we identified three groups of patients with distinct HbA1c trajectories after metformin treatment. The variabilities in metformin response is mainly explained by the baseline HbA1c. The pharmacogenomic analysis of metformin identified a missense variant rs75740279 (Leu/Val) for STAU2 associated with the metformin response. This strategy can be applied to study other anti-diabeticmedications. Such research will facilitate the translational healthcare for better T2DM management.

BackgroundPolycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting women of reproductive age. Previous studies have identified genetic variants associated with PCOS identified by different diagnostic criteria. The Rotterdam Criteria is the broadest and able to identify the most PCOS cases.ObjectivesTo identify novel associated genetic variants, we extracted PCOS cases and controls from the electronic health records (EHR) based on the Rotterdam Criteria and performed a genome-wide association study (GWAS).Study DesignWe developed a PCOS phenotyping algorithm based on the Rotterdam criteria and applied it to three EHR-linked biobanks to identify cases and controls for genetic study. In discovery phase, we performed individual GWAS using the Geisinger’s MyCode and the eMERGE cohorts, which were then meta-analyzed. We attempted validation of the significantly association loci (P−6) in the BioVU cohort. All association analyses used logistic regression, assuming an additive genetic model, and adjusted for principal components to control for population stratification. An inverse-variance fixed effect model was adopted for meta-analyses. Additionally, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used STRING to identify protein-protein interaction network.ResultsWe identified 2,995 PCOS cases and 53,599 controls in total (2,742cases and 51,438 controls from the discovery phase; 253 cases and 2,161 controls in the validation phase). GWAS identified one novel genome-wide significant variant rs17186366 (OR=1.37 [1.23,1.54], P=2.8×10−8) located nearSOD2. Additionally, two loci with suggestive association were also identified: rs113168128 (OR=1.72 [1.42,2.10], P=5.2 x10−8), an intronic variant ofERBB4that is independent from the previously published variants, and rs144248326 (OR=2.13 [1.52,2.86], P=8.45×10−7), a novel intronic variant inWWTR1. In the further association tests of the top 3 SNPs with each criterion in the PCOS algorithm, we found that rs17186366 was associated with polycystic and hyperandrogenism, while rs11316812 and rs144248326 were mainly associated with oligomenorrhea or infertility. Besides ERBB4, we also validated the association withDENND1A1.ConclusionThrough a discovery-validation GWAS on PCOS cases and controls identified from EHR using an algorithm based on Rotterdam criteria, we identified and validated a novel association with variants withinERBB4. We also identified novel associations nearbySOD2andWWTR1. These results suggest the eGFR and Hippo pathways in the disease etiology. With previously identified PCOS-associated lociYAP1, theERBB4-YAP1-WWTR1network implicates the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of PCOS.