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Background: Two million patients per year are referred to urologists for hematuria, or blood in the urine. The American Urological Association recently adopted a risk-stratified hematuria evaluation guideline to limit multi-phase computed tomography to individuals at highest risk of occult malignancy., Objectives: To understand population-level hematuria evaluations, we developed an algorithm to accurately identify hematuria cases from electronic health records (EHRs)., Methods: We used International Classification of Diseases (ICD)-9/ICD-10 diagnosis codes, urine color, and urine microscopy values to identify hematuria cases and to differentiate between gross and microscopic hematuria. Using an iterative process, we refined the ICD-9 algorithm on a gold standard, chart-reviewed cohort of 3,094 hematuria cases, and the ICD-10 algorithm on a 300 patient cohort. We applied the algorithm to Geisinger patients ≥35 years ( n  = 539,516) and determined performance by conducting chart review ( n  = 500)., Results: After applying the hematuria algorithm, we identified 51,500 hematuria cases and 488,016 clean controls. Of the hematuria cases, 11,435 were categorized as gross, 26,658 as microscopic, 12,562 as indeterminate, and 845 were uncategorized. The positive predictive value (PPV) of identifying hematuria cases using the algorithm was 100% and the negative predictive value (NPV) was 99%. The gross hematuria algorithm had a PPV of 100% and NPV of 99%. The microscopic hematuria algorithm had lower PPV of 78% and NPV of 100%., Conclusion: We developed an algorithm utilizing diagnosis codes and urine laboratory values to accurately identify hematuria and categorize as gross or microscopic in EHRs. Applying the algorithm will help researchers to understand patterns of care for this common condition., Competing Interests: M.E.N. serves as a paid consultant to the American College of Physicians High Value Care Task Force and as a consultant and advisor to Grand Rounds for which he is paid via stock options. T.G. served as a paid consultant to WebMD and reports an immediate family member was an employee of DRPLZ and was a stockholder. All other authors report no conflict of interest., (Thieme. All rights reserved.)

Study Objectives: We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States., Methods: Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ≥ 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated., Results: The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ≥ 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ≥ 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases., Conclusions: A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research., (© 2020 American Academy of Sleep Medicine.)

Lipodystrophies are a group of disorders characterized by absence or loss of adipose tissue and abnormal fat distribution, commonly accompanied by metabolic dysregulation. Although considered rare disorders, their prevalence in the general population is not well understood. We aimed to evaluate the clinical and genetic prevalence of lipodystrophy disorders in a large clinical care cohort. We interrogated the electronic health record (EHR) information of >1.3 million adults from the Geisinger Health System for lipodystrophy diagnostic codes. We estimate a clinical prevalence of disease of 1 in 20,000 individuals. We performed genetic analyses in individuals with available genomic data to identify variants associated with inherited lipodystrophies and examined their EHR for comorbidities associated with lipodystrophy. We identified 16 individuals carrying the p.R482Q pathogenic variant in LMNA associated with Dunnigan familial partial lipodystrophy. Four had a clinical diagnosis of lipodystrophy, whereas the remaining had no documented clinical diagnosis despite having accompanying metabolic abnormalities. We observed a lipodystrophy-associated variant carrier frequency of 1 in 3,082 individuals in our cohort with substantial burden of metabolic dysregulation. We estimate a genetic prevalence of disease of ∼1 in 7,000 in the general population. Partial lipodystrophy is an underdiagnosed condition. and its prevalence, as defined molecularly, is higher than previously reported. Genetically guided stratification of patients with common metabolic disorders, like diabetes and dyslipidemia, is an important step toward precision medicine., (© 2019 by the American Diabetes Association.)

Background: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with variants in desmosome genes. Secondary findings of pathogenic/likely pathogenic variants, primarily loss-of-function (LOF) variants, are recommended for clinical reporting; however, their prevalence and associated phenotype in a general clinical population are not fully characterized., Methods: From whole-exome sequencing of 61 019 individuals in the DiscovEHR cohort, we screened for putative loss-of-function variants in PKP2 , DSC2 , DSG2 , and DSP . We evaluated measures from prior clinical ECG and echocardiograms, manually over-read to evaluate ARVC diagnostic criteria, and performed a PheWAS (phenome-wide association study). Finally, we estimated expected penetrance using Bayesian inference., Results: One hundred forty individuals (0.23%; 59±18 years old at last encounter; 33% male) had an ARVC variant (G + ). None had an existing diagnosis of ARVC in the electronic health record, nor significant differences in prior ECG or echocardiogram findings compared with matched controls without variants. Several G + individuals satisfied major repolarization (n=4) and ventricular function (n=5) criteria, but this prevalence matched controls. PheWAS showed no significant associations of other heart disease diagnoses. Combining our best genetic and disease prevalence estimates yields an estimated penetrance of 6.0%., Conclusions: The prevalence of ARVC loss-of-function variants is ≈1:435 in a general clinical population of predominantly European descent, but with limited electronic health record-based evidence of phenotypic association in our population, consistent with a low penetrance estimate. Prospective deep phenotyping and longitudinal follow-up of a large sequenced cohort is needed to determine the true clinical relevance of an incidentally identified ARVC loss-of-function variant.

Background: Asthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome., Objective: We propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population., Methods: We performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset., Results: There were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms., Conclusion: We were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes., Competing Interests: Agnes Sundaresan reported previous grant funding from Teva Pharmaceutical Industries, outside the submitted work. Other authors declare that they have no conflict of interest relevant to this research., (Georg Thieme Verlag KG Stuttgart · New York.)

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery., (Copyright © 2016, American Association for the Advancement of Science.)

Purpose: Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007, Geisinger launched MyCode, a system-wide biobanking program to link samples and EHR data for broad research use., Methods: Patient-centered input into MyCode was obtained using participant focus groups. Participation in MyCode is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR and, since 2013, the return of clinically actionable results to participants. MyCode leverages Geisinger's technology and clinical infrastructure for participant tracking and sample collection., Results: MyCode has a consent rate of >85%, with more than 90,000 participants currently and with ongoing enrollment of ~4,000 per month. MyCode samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations., Conclusion: The MyCode project has created resources that enable a new model for translational research that is faster, more flexible, and more cost-effective than traditional clinical research approaches. The new model is scalable and will increase in value as these resources grow and are adopted across multiple research platforms.Genet Med 18 9, 906-913.

Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCodeTM biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.

Phenome-Wide Association Studies (PheWAS) comprehensively investigate the association between genetic variation and a wide array of outcome traits. Electronic health record (EHR) based PheWAS uses various abstractions of International Classification of Diseases, Ninth Revision (ICD-9) codes to identify case/control status for diagnoses that are used as the phenotypic variables. However, there have not been comparisons within a PheWAS between results from high quality derived phenotypes and high-throughput but potentially inaccurate use of ICD-9 codes for case/control definition. For this study we first developed a group of high quality algorithms for five phenotypes. Next we evaluated the association of these "gold standard" phenotypes and 4,636,178 genetic variants with minor allele frequency > 0.01 and compared the results from high-throughput associations at the 3 digit, 5 digit, and PheWAS codes for defining case/control status. We found that certain diseases contained similar patient populations across phenotyping methods but had differences in PheWAS.

Objective: Electronic health records (EHRs) are not optimized for chronic disease management. To improve the quality of care for patients with rheumatic disease, we developed electronic data capture, aggregation, display, and documentation software., Methods: The software integrated and reassembled information from the patient (via a touchscreen questionnaire), nurse, physician, and EHR into a series of actionable views. Core functions included trends over time, rheumatology-related demographics, and documentation for patient and provider. Quality measures collected included patient-reported outcomes, disease activity, and function. The software was tested and implemented in 3 rheumatology departments, and integrated into routine care delivery. Post-implementation evaluation measured adoption, efficiency, productivity, and patient perception., Results: Over 2 years, 6,725 patients completed 19,786 touchscreen questionnaires. The software was adopted for use by 86% of patients and rheumatologists. Chart review and documentation time trended downward, and productivity increased by 26%. Patient satisfaction, activation, and adherence remained unchanged, although pre-implementation values were high. A strong correlation was seen between use of the software and disease control (weighted Pearson's correlation coefficient 0.5927, P = 0.0095), and a relative increase in patients with low disease activity of 3% per quarter was noted., Conclusion: We describe innovative software that aggregates, stores, and displays information vital to improving the quality of care for patients with chronic rheumatic disease. The software was well-adopted by patients and providers. Post-implementation, significant improvements in quality of care, efficiency of care, and productivity were demonstrated., (Copyright © 2015 by the American College of Rheumatology.)

Background: Using abdominal aortic aneurysm (AAA) as a model, this case-control study used electronic medical record (EMR) data to assess known risk factors and identify new associations., Methods: The study population consisted of cases with AAA (n =888) and controls (n =10,523) from the Geisinger Health System EMR in Central and Northeastern Pennsylvania. We extracted all clinical and diagnostic data for these patients from January 2004 to December 2009 from the EMR. From this sample set, bootstrap replication procedures were used to randomly generate 2,500 iterations of data sets, each with 500 cases and 2000 controls. Estimates of risk factor effect sizes were obtained by stepwise logistic regression followed by bootstrap aggregation. Variables were ranked using the number of inclusions in iterations and P values., Results: The benign neoplasm diagnosis was negatively associated with AAA, a novel finding. Similarly, type 2 diabetes, diastolic blood pressure, weight and myelogenous neoplasms were negatively associated with AAA. Peripheral artery disease, smoking, age, coronary stenosis, systolic blood pressure, age, height, male sex, pulmonary disease and hypertension were associated with an increased risk for AAA., Conclusions: This study utilized EMR data, retrospectively, for risk factor assessment of a complex disease. Known risk factors for AAA were replicated in magnitude and direction. A novel negative association of benign neoplasms was identified. EMRs allow researchers to rapidly and inexpensively use clinical data to expand cohort size and derive better risk estimates for AAA as well as other complex diseases.

Background: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed. Aims: We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis. Methods: Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged ≥ 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites. Results: Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range. Conclusions: We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups. [ABSTRACT FROM AUTHOR]

Key Points: Question: Is sex chromosome aneuploidy associated with venous thromboembolism (VTE)? Findings: In this retrospective multicohort study that included 642 544 adult participants, the incidence of a VTE diagnosis among those with an additional sex chromosome compared with those with 2 sex chromosomes was 1.3% per person-year compared with 0.25% per person-year, respectively, in one cohort, and 0.42% per person-year compared with 0.11% per person-year, respectively, in the other cohort. These differences were statistically significant. Meaning: The presence of a supernumerary sex chromosome aneuploidy was associated with a small but statistically significant increased risk of VTE, but further research is needed to understand the clinical implications of this association. Importance: An increased risk of venous thromboembolism (VTE) has been reported in men with an additional sex chromosome. The association between other sex chromosome aneuploidies and VTE is not well characterized. Objective: To determine if sex chromosome aneuploidy is associated with VTE. Design, Setting, and Participants: Retrospective cohort study of sex chromosome aneuploidy and VTE, performed by analyzing X- and Y-chromosome dosage and VTE incidence in 642 544 individuals from 2 population-scale biobanks: the US Geisinger MyCode Community Health Initiative (N = 154 519) and the UK Biobank (N = 488 025); analysis was limited to participants self-identified as White because of inadequate sample sizes for other race and ethnicity groups. A total of 108 461 unrelated MyCode participants with electronic health record follow-up ranging from September 1996 to December 2020 and 418 725 unrelated British and Irish UK Biobank participants who attended the baseline assessment between March 2006 and October 2010, with follow-up extending to November 2020, were included in analyses of VTE. Exposures: Sex chromosome aneuploidies. Main Outcomes and Measures: Individuals with 1 primary inpatient VTE diagnosis, 2 primary outpatient VTE diagnoses, or a self-reported VTE diagnosis were defined as VTE cases. P values were adjusted for multiple comparisons. Results: Identification of sex chromosome aneuploidy was undertaken among 642 544 individuals aged 18 to 90 years. Identification of a diagnosis of VTE was undertaken among 108 461 unrelated MyCode participants (65 565 [60.5%] female; mean age at last visit, 58.0 [SD, 17.6] years; median follow-up, 15.3 [IQR, 9.7] years) and among 418 725 unrelated UK Biobank participants (224 695 [53.7%] female; mean age at baseline interview, 56.9 [SD, 8.0] years; median follow-up, 12.0 [IQR, 1.6] years). Among MyCode participants, during 10 years of follow-up, 17 incident VTE events per 1353 person-years were detected among those with supernumerary sex chromosome aneuploidy (1.3% per person-year) compared with 2060 per 816 682 person-years among those with 46,XX or 46,XY (0.25% per person-year) (hazard ratio, 5.4 [95% CI, 3.4-8.7]; 10-year risk difference, 8.8% [95% CI, 4.2%-14.0%]; P <.001). Among UK Biobank participants, during 10 years of follow-up, 16 incident VTE events per 3803 person-years were detected among those with supernumerary sex chromosome aneuploidy (0.42% per person-year) compared with 4491 per 3 970 467 person-years among those with 46,XX or 46,XY (0.11% per person-year) (hazard ratio, 4.1 [95% CI, 2.5-6.7]; 10-year risk difference, 3.7% [95% CI, 1.4%-5.9%]; P <.001). Conclusions and Relevance: Adults with supernumerary sex chromosome aneuploidies compared with 2 sex chromosomes had a small but statistically significant increased risk of VTE. Further research is needed to understand the clinical implications of this association. This study assesses the association of sex chromosome aneuploidies with incidence of venous thromboembolism (VTE) in 2 large biobank populations. [ABSTRACT FROM AUTHOR]

Background: Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization. Methods: Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization). Results: Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10-4), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10-8), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10-5), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10-5), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10-5). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48). Conclusions: This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system. [ABSTRACT FROM AUTHOR]

Background: Exome and genome sequencing are routinely used in clinical care and research. These technologies allow for the detection of pathogenic/likely pathogenic variants in clinically actionable genes. However, fueled in part by a lack of empirical evidence, controversy surrounds the provision of genetic results for adult-onset conditions to minors and their parents. We have designed a mixed-methods, longitudinal cohort study to collect empirical evidence to advance this debate.Methods: Pediatric participants in the Geisinger MyCode® Community Health Initiative with available exome sequence data will have their variant files assessed for pathogenic/likely pathogenic variants in 60 genes designated as actionable by MyCode. Eight of these genes are associated with adult-onset conditions (Hereditary Breast and Ovarian Cancer Syndrome (HBOC), Lynch syndrome, MUTYH-associated polyposis, HFE-Associated Hereditary Hemochromatosis), while the remaining genes have pediatric onset. Prior to clinical confirmation of results, pediatric MyCode participants and their parents/legal guardians will be categorized into three study groups: 1) those with an apparent pathogenic/likely pathogenic variant in a gene associated with adult-onset disease, 2) those with an apparent pathogenic/likely pathogenic variant in a gene associated with pediatric-onset disease or with risk reduction interventions that begin in childhood, and 3) those with no apparent genomic result who are sex- and age-matched to Groups 1 and 2. Validated and published quantitative measures, semi-structured interviews, and a review of electronic health record data conducted over a 12-month period following disclosure of results will allow for comparison of psychosocial and behavioral outcomes among parents of minors (ages 0-17) and adolescents (ages 11-17) in each group.Discussion: These data will provide guidance about the risks and benefits of informing minors and their family members about clinically actionable, adult-onset genetic conditions and, in turn, help to ensure these patients receive care that promotes physical and psychosocial health.Trial Registration: ClinicalTrials.gov Identifier: NCT03832985. Registered 6 February 2019. [ABSTRACT FROM AUTHOR]

Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia.Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity.Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems. [ABSTRACT FROM AUTHOR]

Purpose We describe age, multiple chronic condition profiles and health system contact in patients with urological cancer. Materials and Methods Using Geisinger Health System electronic health records we identified adult primary care patients and a subset with at least 1 urology encounter between 2001 and 2015. The Agency for Health Care Research and Quality Chronic Condition Indicator and Clinical Classifications Software tools were applied to ICD-9 codes to identify chronic conditions. Multiple chronic conditions were defined as 2 or more chronic conditions. Patients with urological cancer were identified using ICD-9 codes for prostate, bladder, kidney, testis and penile cancer. Inpatient and outpatient visits in the year prior to the most recent encounter were counted to document health system contact. Results We identified 357,100 primary care and 33,079 urology patients, of whom 4,023 had urological cancer. Patients with urological cancer were older than primary care patients (71 vs 46 years) and they had more median chronic conditions (7 vs 4). Kidney and bladder cancer were the most common chronic conditions (median 8 patients each). Coronary artery disease and chronic kidney disease were common in urological cancer cases compared to mental health conditions in primary care cases. Patients with urological cancer who had multiple chronic conditions had the most health system contact, including 32% with at least 1 hospitalization and 68% with more than 5 outpatient visits during 1 year. Conclusions Urology patients are older and more medically complex, especially those with urological cancer than primary care patients. These data may inform care redesign to reduce the treatment burden and improve care coordination in urological cancer cases. [ABSTRACT FROM AUTHOR]

Familial hypercholesterolemia (FH) remains underdiagnosed despite widespread cholesterol screening. Exome sequencing and electronic health record (EHR) data of 50,726 individuals were used to assess the prevalence and clinical impact of FH-associated genomic variants in the Geisinger Health System. The estimated FH prevalence was 1:256 in unselected participants and 1:118 in participants ascertained via the cardiac catheterization laboratory. FH variant carriers had significantly increased risk of coronary artery disease. Only 24% of carriers met EHR-based presequencing criteria for probable or definite FH diagnosis. Active statin use was identified in 58% of carriers; 46% of statin-treated carriers had a low-density lipoprotein cholesterol level below 100 mg/dl. Thus, we find that genomic screening can prompt the diagnosis of FH patients, most of whom are receiving inadequate lipid-lowering therapy. [ABSTRACT FROM AUTHOR]