Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions

BACKGROUND

Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs.

OBJECTIVE

This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs.

METHODS

ADER uses natural language processing to extract patients' medications, findings, and past diagnoses from admission notes. It compares excerpted information to a database of known medication adverse effects and promptly warns clinicians about potential ongoing ADRs and potential confounders via alerts placed in patients' electronic health records (EHRs). A 3-month intervention trial evaluated ADER's impact on antihypertensive medication ordering behaviors. At the time of patient admission, ADER warned providers on the Internal Medicine wards of Vanderbilt University Hospital about potential ongoing preadmission antihypertensive medication ADRs. A retrospective control group, comprised similar physicians from a period prior to the intervention, received no alerts. The evaluation compared ordering behaviors for each group to determine if preadmission medications changed during hospitalization or at discharge. The study also analyzed intervention group participants' survey responses and user comments.

RESULTS

ADER identified potential preadmission ADRs for 30% of both groups. Compared with controls, intervention providers more often withheld or discontinued suspected ADR-causing medications during the inpatient stay (p < 0.001). Intervention providers who responded to alert-related surveys held or discontinued suspected ADR-causing medications more often at discharge (p < 0.001).

CONCLUSION

Results indicate that ADER helped physicians recognize ADRs and reduced ordering of suspected ADR-causing medications. In hospitals using EHRs, ADER-like systems could improve clinicians' recognition and elimination of ongoing ADRs.

Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors

OBJECTIVE

A prolonged QTc (LQT) is a surrogate for the risk of torsade de pointes (TdP). QTc interval duration is influenced by sex hormones: oestradiol prolongs and testosterone shortens QTc. Drugs used in the treatment of breast cancer have divergent effects on hormonal status.

METHODS

We performed a disproportionality analysis using the European database of suspected adverse drug reaction (ADR) reports to evaluate the reporting OR (ROR χ²) of LQT, TdP and ventricular arrhythmias associated with selective oestrogen receptor modulators (SERMs: tamoxifen and toremifene) as opposed to aromatase inhibitors (AIs: anastrozole, exemestane and letrozole). When the proportion of an ADR is greater in patients exposed to a drug (SERMs) compared with patients exposed to control drug (AIs), this suggests an association between the specific drug and the reaction and is a potential signal for safety. Clinical and demographic characterisation of patients with SERMs-induced LQT and ventricular arrhythmias was performed.

RESULTS

SERMs were associated with higher proportion of LQT reports versus AIs (26/8318 vs 11/14851, ROR: 4.2 (2.11-8.55), p<0.001). SERMs were also associated with higher proportion of TdP and ventricular arrhythmia reports versus AIs (6/8318 vs 2/14851, ROR: 5.4 (1.29-26.15), p:0.02; 16/8318 vs 12/14851, ROR: 2.38 (1.15-4.94), p:0.02, respectively). Mortality was 38% in patients presenting ventricular arrhythmias associated with SERMs.

CONCLUSIONS

SERMs are associated with more reports of drug-induced LQT, TdP and ventricular arrhythmias compared with AIs. This finding is consistent with oestradiol-like properties of SERMs on the heart as opposed to effects of oestrogen deprivation and testosterone increase induced by AIs.

TRIAL REGISTRATION NUMBER

NCT03259711.

Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome

The development of new knowledge around the genetic determinants of variable drug action has naturally raised the question of how this new knowledge can be used to improve the outcome of drug therapy. Two broad approaches have been taken: a point-of-care approach in which genotyping for specific variant(s) is undertaken at the time of drug prescription, and a preemptive approach in which multiple genetic variants are typed in an individual patient and the information archived for later use when a drug with a "pharmacogenetic story" is prescribed. This review addresses the current state of implementation, the rationale for these approaches, and barriers that must be overcome. Benefits to pharmacogenetic testing are only now being defined and will be discussed.

Defining Phenotypes from Clinical Data to Drive Genomic Research

The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.

Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms

Significant advances in cancer treatment have resulted in decreased cancer related mortality for many malignancies with some cancer types now considered chronic diseases. Despite these improvements, there is increasing recognition that many cancer patients or cancer survivors can develop cardiovascular diseases, either due to the cancer itself or as a result of anticancer therapy. Much attention has focused on heart failure; however, other cardiotoxicities, notably cardiac rhythm disorders, can occur without underlying cardiomyopathy. Supraventricular tachycardias occur in cancer patients treated with cytotoxic chemotherapy (anthracyclines, gemcitabine, cisplatin and alkylating-agents) or kinase-inhibitors (KIs) such as ibrutinib. Ventricular arrhythmias, with a subset of them being torsades-de-pointes (TdP) favored by QTc prolongation have been reported: this may be the result of direct hERG-channel inhibition or a more recently-described mechanism of phosphoinositide-3-kinase inhibition. The major anticancer drugs responsible for QTc prolongation in this context are KIs, arsenic trioxide, anthracyclines, histone deacetylase inhibitors, and selective estrogen receptor modulators. Anticancer drug-induced cardiac rhythm disorders remain an underappreciated complication even by experienced clinicians. Moreover, the causal relationship of a particular anticancer drug with cardiac arrhythmia occurrence remains challenging due in part to patient comorbidities and complex treatment regimens. For example, any cancer patient may also be diagnosed with common diseases such as hypertension, diabetes or heart failure which increase an individual's arrhythmia susceptibility. Further, anticancer drugs are generally usually used in combination, increasing the challenge around establishing causation. Thus, arrhythmias appear to be an underappreciated adverse effect of anticancer agents and the incidence, significance and underlying mechanisms are now being investigated.

Genome-wide and candidate gene approaches of clopidogrel efficacy using pharmacodynamic and clinical end points-Rationale and design of the International Clopidogrel Pharmacogenomics Consortium (ICPC)

RATIONALE

The P2Y12 receptor inhibitor clopidogrel is widely used in patients with acute coronary syndrome, percutaneous coronary intervention, or ischemic stroke. Platelet inhibition by clopidogrel shows wide interpatient variability, and high on-treatment platelet reactivity is a risk factor for atherothrombotic events, particularly in high-risk populations. CYP2C19 polymorphism plays an important role in this variability, but heritability estimates suggest that additional genetic variants remain unidentified. The aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify genetic determinants of clopidogrel pharmacodynamics and clinical response.

STUDY DESIGN

Based on the data published on www.clinicaltrials.gov, clopidogrel intervention studies containing genetic and platelet function data were identified for participation. Lead investigators were invited to share DNA samples, platelet function test results, patient characteristics, and cardiovascular outcomes to perform candidate gene and genome-wide studies.

RESULTS

In total, 17 study sites from 13 countries participate in the ICPC, contributing individual patient data from 8,829 patients. Available adenosine diphosphate-stimulated platelet function tests included vasodilator-stimulated phosphoprotein assay, light transmittance aggregometry, and the VerifyNow P2Y12 assay. A proof-of-principle analysis based on genotype data provided by each group showed a strong and consistent association between CYP2C19*2 and platelet reactivity (P value=5.1 × 10-40).

CONCLUSION

The ICPC aims to identify new loci influencing clopidogrel efficacy by using state-of-the-art genetic approaches in a large cohort of clopidogrel-treated patients to better understand the genetic basis of on-treatment response variability.

Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.

Inhibition of the α-Subunit of Phosphoinositide 3-Kinase in Heart Increases Late Sodium Current and Is Arrhythmogenic

Although inhibition of phosphoinositide 3-kinase (PI3K) is an emerging strategy in cancer therapy, we and others have reported that this action can also contribute to drug-induced QT prolongation and arrhythmias by increasing cardiac late sodium current (I_NaL). Previous studies in mice implicate the PI3K-α isoform in arrhythmia susceptibility. Here, we have determined the effects of new anticancer drugs targeting specific PI3K isoforms on I_NaL and action potentials (APs) in mouse cardiomyocytes and Chinese hamster ovary cells (CHO). Chronic exposure (10-100 nM; 5-48 hours) to PI3K-α-specific subunit inhibitors BYL710 (alpelisib) and A66 and a pan-PI3K inhibitor (BKM120) increased I_NaL in SCN5A-transfected CHO cells and mouse cardiomyocytes. The specific inhibitors (10-100 nM for 5 hours) markedly prolonged APs and generated triggered activity in mouse cardiomyocytes (9/12) but not in controls (0/6), and BKM120 caused similar effects (3/6). The inclusion of water-soluble PIP3, a downstream effector of the PI3K signaling pathway, in the pipette solution reversed these arrhythmogenic effects. By contrast, inhibition of PI3K-β, -γ, and -δ isoforms did not alter I_NaL or APs. We conclude that inhibition of cardiac PI3K-α is arrhythmogenic by increasing I_NaL and this effect is not seen with inhibition of other PI3K isoforms. These results highlight a mechanism underlying potential cardiotoxicity of PI3K-α inhibitors.

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development

Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.

Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants

Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights.

Calcium channel blockers as drug repurposing candidates for gestational diabetes: Mining large scale genomic and electronic health records data to repurpose medications

New therapeutic approaches are needed for gestational diabetes mellitus (GDM), but must show safety and efficacy in a historically understudied population. We studied associations between electronic medical record (EMR) phenotypes and genetic variants to uncover drugs currently considered safe in pregnancy that could treat or prevent GDM. We identified 129 systemically active drugs considered safe in pregnancy targeting the proteins produced from 196 genes. We tested for associations between GDM and/or type 2 diabetes (DM2) and 306 SNPs in 130 genes represented on the Illumina Infinium Human Exome Bead Chip (DM2 was included due to shared pathophysiological features with GDM). In parallel, we tested the association between drugs and glucose tolerance during pregnancy as measured by the glucose recorded during a routine 50-g glucose tolerance test (GTT). We found an association between GDM/DM2 and the genes targeted by 11 drug classes. In the EMR analysis, 6 drug classes were associated with changes in GTT. Two classes were identified in both analyses. L-type calcium channel blocking antihypertensives (CCBs), were associated with a 3.18 mg/dL (95% CI -6.18 to -0.18) decrease in glucose during GTT, and serotonin receptor type 3 (5HT-3) antagonist antinausea medications were associated with a 3.54 mg/dL (95% CI 1.86-5.23) increase in glucose during GTT. CCBs were identified as a class of drugs considered safe in pregnancy could have efficacy in treating or preventing GDM. 5HT-3 antagonists may be associated with worse glucose tolerance.

Exome-wide association study of plasma lipids in >300,000 individuals

We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.

A Missense Variant in PLEC Increases Risk of Atrial Fibrillation

BACKGROUND

Genome-wide association studies (GWAS) have yielded variants at >30 loci that associate with atrial fibrillation (AF), including rare coding mutations in the sarcomere genes MYH6 and MYL4.

OBJECTIVES

The aim of this study was to search for novel AF associations and in doing so gain insights into the mechanisms whereby variants affect AF risk, using electrocardiogram (ECG) measurements.

METHODS

The authors performed a GWAS of 14,255 AF cases and 374,939 controls, using whole-genome sequence data from the Icelandic population, and tested novel signals in 2,002 non-Icelandic cases and 12,324 controls. They then tested the AF variants for effect on cardiac electrical function by using measurements in 289,297 ECGs from 62,974 individuals.

RESULTS

The authors discovered 2 novel AF variants, the intergenic variant rs72700114, between the genes LINC01142 and METTL11B (risk allele frequency = 8.1%; odds ratio [OR]: 1.26; p = 3.1 × 10^-18), and the missense variant p.Gly4098Ser in PLEC (frequency = 1.2%; OR: 1.55; p = 8.0 × 10^-10), encoding plectin, a cytoskeletal cross-linking protein that contributes to integrity of cardiac tissue. The authors also confirmed 29 reported variants. p.Gly4098Ser in PLEC significantly affects various ECG measurements in the absence of AF. Other AF variants have diverse effects on the conduction system, ranging from none to extensive.

CONCLUSIONS

The discovery of a missense variant in PLEC affecting AF combined with recent discoveries of variants in the sarcomere genes MYH6 and MYL4 points to an important role of myocardial structure in the pathogenesis of the disease. The diverse associations between AF variants and ECG measurements suggest fundamentally different categories of mechanisms contributing to the development of AF.

Erratum to: A multi-stage genome-wide association study of uterine fibroids in African Americans

The article "A multi-stage genome-wide association study of uterine fibroids in African Americans", written by Jacklyn N. Hellwege, was originally published Online First without open access. After publication in volume 136, issue 10, page 1363-1373 the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to

A multi-stage genome-wide association study of uterine fibroids in African Americans

Uterine fibroids are benign tumors of the uterus affecting up to 77% of women by menopause. They are the leading indication for hysterectomy, and account for $34 billion annually in the United States. Race/ethnicity and age are the strongest known risk factors. African American (AA) women have higher prevalence, earlier onset, and larger and more numerous fibroids than European American women. We conducted a multi-stage genome-wide association study (GWAS) of fibroid risk among AA women followed by in silico genetically predicted gene expression profiling of top hits. In Stage 1, cases and controls were confirmed by pelvic imaging, genotyped and imputed to 1000 Genomes. Stage 2 used self-reported fibroid and GWAS data from 23andMe, Inc. and the Black Women's Health Study. Associations with fibroid risk were modeled using logistic regression adjusted for principal components, followed by meta-analysis of results. We observed a significant association among 3399 AA cases and 4764 AA controls at rs739187 (risk-allele frequency = 0.27) in CYTH4 (OR (95% confidence interval) = 1.23 (1.16-1.30), p value = 7.82 × 10-9). Evaluation of the genetic association results with MetaXcan identified lower predicted gene expression of CYTH4 in thyroid tissue as significantly associated with fibroid risk (p value = 5.86 × 10-8). In this first multi-stage GWAS for fibroids among AA women, we identified a novel risk locus for fibroids within CYTH4 that impacts gene expression in thyroid and has potential biological relevance for fibroids.

The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

Influence of Human Leukocyte Antigen (HLA) Alleles and Killer Cell Immunoglobulin-Like Receptors (KIR) Types on Heparin-Induced Thrombocytopenia (HIT)

Heparin-induced thrombocytopenia (HIT) is an unpredictable, life-threatening, immune-mediated reaction to heparin. Variation in human leukocyte antigen (HLA) genes is now used to prevent immune-mediated adverse drug reactions. Combinations of HLA alleles and killer cell immunoglobulin-like receptors (KIR) are associated with multiple autoimmune diseases and infections. The objective of this study is to evaluate the association of HLA alleles and KIR types, alone or in the presence of different HLA ligands, with HIT. HIT cases and heparin-exposed controls were identified in BioVU, an electronic health record coupled to a DNA biobank. HLA sequencing and KIR type imputation using Illumina OMNI-Quad data were performed. Odds ratios for HLA alleles and KIR types and HLA*KIR interactions using conditional logistic regressions were determined in the overall population and by race/ethnicity. Analysis was restricted to KIR types and HLA alleles with a frequency greater than 0.01. The p values for HLA and KIR association were corrected by using a false discovery rate q<0.05 and HLA*KIR interactions were considered significant at p<0.05. Sixty-five HIT cases and 350 matched controls were identified. No statistical differences in baseline characteristics were observed between cases and controls. The HLA-DRB3*01:01 allele was significantly associated with HIT in the overall population (odds ratio 2.81 [1.57-5.02], p=2.1×10-4 , q=0.02) and in individuals with European ancestry, independent of other alleles. No KIR types were associated with HIT, although a significant interaction was observed between KIR2DS5 and the HLA-C1 KIR binding group (p=0.03). The HLA-DRB3*01:01 allele was identified as a potential risk factor for HIT. This class II HLA gene and allele represent biologically plausible candidates for influencing HIT pathogenesis. We found limited evidence of the role of KIR types in HIT pathogenesis. Replication and further study of the HLA-DRB3*01:01 association is necessary.

Rare variants in genes encoding the cardiac sodium channel and associated compounds and their impact on outcome of catheter ablation of atrial fibrillation

Aim

Rare variants of genes encoding the cardiac sodium channel and associated compounds have been linked with atrial fibrillation (AF). Nevertheless, current expert consensus does not support genetic testing in AF, which is in part based on the fact that “there is no therapeutic impact derived from AF genetic test results”. However, there are no studies available supporting this recommendation. Consequently, this study analyzed the impact of rare variants affecting the cardiac sodium channel on rhythm outcome of AF catheter ablation.

Methods and results

In 137 consecutive patients with lone AF enrolled in the Leipzig Heart Center AF ablation registry, screening for mutations in SCN5A, SCN1B – 4B, CAV3, GPD1L, SNTA1 and MOG1 was performed. We identified 3 rare non-synonymous variants in SCN5A, 5 in SCN1B, 1 in SCN4B, 1 in CAV3, 6 in GPD1L, 3 in SNTA1 and 3 in MOG1 (16%). Variant carriers were otherwise comparable with non-variant carriers. Analysis of AF recurrence rates after radiofrequency AF catheter ablation by serial 7-day Holter ECG monitoring between 3 and 12 months revealed no difference between groups, i.e. 45% in variant carriers vs. 49% in non-variant carriers.

Conclusions

Rare variants in genes encoding the cardiac sodium channel and associated compounds are frequently found in lone AF but were not found to impact the outcome of AF catheter ablation. This finding supports current recommendations not to screen for rare variants for the ablation outcome in AF. Nevertheless, it may at least be helpful for understanding AF mechanisms and larger studies are needed to further explore the possible association between genotype and response to AF therapies.

New Pharmacogenomics Research Network: An Open Community Catalyzing Research and Translation in Precision Medicine

The goal of pharmacogenomics research is to discover genetic polymorphisms that underlie variation in drug response. Increasingly, pharmacogenomics research involves large numbers of patients and the application of new technologies and methodologies to enable discovery. The Pharmacogenomics Research Network (PGRN) has become a community-driven network of investigators spanning scientific and clinical disciplines. Here, we highlight the activities and types of resources that enable PGRN members to enhance and drive basic and translational research in pharmacogenomics.

A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records

Electronic health records (EHRs) linked with biobanks have been recognized as valuable data sources for pharmacogenomic studies, which require identification of patients with certain adverse drug reactions (ADRs) from a large population. Since manual chart review is costly and time-consuming, automatic methods to accurately identify patients with ADRs have been called for. In this study, we developed and compared different informatics approaches to identify ADRs from EHRs, using clopidogrel-induced bleeding as our case study. Three different types of methods were investigated: 1) rule-based methods; 2) machine learning-based methods; and 3) scoring function-based methods. Our results show that both machine learning and scoring methods are effective and the scoring method can achieve a high precision with a reasonable recall. We also analyzed the contributions of different types of features and found that the temporality information between clopidogrel and bleeding events, as well as textual evidence from physicians' assertion of the adverse events are helpful. We believe that our findings are valuable in advancing EHR-based pharmacogenomic studies.

Apolipoprotein L1 Variants and Blood Pressure Traits in African Americans

BACKGROUND

African Americans (AA) are disproportionately affected by hypertension-related health disparities. Apolipoprotein L1 (APOL1) risk variants are associated with kidney disease in hypertensive AAs.

OBJECTIVES

This study assessed the APOL1 risk alleles' association with blood pressure traits in AAs.

METHODS

The discovery cohort included 5,204 AA participants from Mount Sinai's BioMe biobank. Replication cohorts included additional BioMe (n = 1,623), Vanderbilt BioVU (n = 1,809), and Northwestern NUgene (n = 567) AA biobank participants. Single nucleotide polymorphisms determining APOL1 G1 and G2 risk alleles were genotyped in BioMe and imputed in BioVU/NUgene participants. APOL1 risk alleles' association with blood pressure-related traits was tested in the discovery cohort, a meta-analysis of replication cohorts, and a combined meta-analysis under recessive and additive models after adjusting for age, sex, body mass index, and estimated glomerular filtration rate.

RESULTS

There were 14% to 16% of APOL1 variant allele homozygotes (2 copies of G1/G2) across cohorts. APOL1 risk alleles were associated under an additive model with systolic blood pressure (SBP) and age at diagnosis of hypertension, which was 2 to 5 years younger in the APOL1 variant allele homozygotes (Cox proportional hazards analysis, p value for combined meta-analysis [p_com] = 1.9 × 10^-5). APOL1 risk alleles were associated with overall SBP (p_com = 7.0 × 10^-8) and diastolic blood pressure (p_com = 2.8 × 10^-4). After adjustment for all covariates, those in the 20- to 29-year age range showed an increase in SBP of 0.94 ± 0.44 mm Hg (p_com = 0.01) per risk variant copy. APOL1-associated estimated glomerular filtration rate decline was observed starting a decade later in life in the 30- to 39-year age range.

CONCLUSIONS

APOL1 risk alleles are associated with higher SBP and earlier hypertension diagnoses in young AAs; this relationship appears to follow an additive model.

Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record

OBJECTIVE

To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated "phecodes" designed to facilitate phenome-wide association studies (PheWAS) in EHRs.

METHODS AND MATERIALS

We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs.

RESULTS

Out of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage.

CONCLUSION

Our results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results.

Kcnj11 Ablation Is Associated With Increased Nitro-Oxidative Stress During Ischemia-Reperfusion Injury: Implications for Human Ischemic Cardiomyopathy

BACKGROUND

Despite increased secondary cardiovascular events in patients with ischemic cardiomyopathy (ICM), the expression of innate cardiac protective molecules in the hearts of patients with ICM is incompletely characterized. Therefore, we used a nonbiased RNAseq approach to determine whether differences in cardiac protective molecules occur with ICM.

METHODS AND RESULTS

RNAseq analysis of human control and ICM left ventricular samples demonstrated a significant decrease in KCNJ11 expression with ICM. KCNJ11 encodes the Kir6.2 subunit of the cardioprotective K_ATP channel. Using wild-type mice and kcnj11-deficient (kcnj11-null) mice, we examined the effect of kcnj11 expression on cardiac function during ischemia-reperfusion injury. Reactive oxygen species generation increased in kcnj11-null hearts above that found in wild-type mice hearts after ischemia-reperfusion injury. Continuous left ventricular pressure measurement during ischemia and reperfusion demonstrated a more compromised diastolic function in kcnj11-null compared with wild-type mice during reperfusion. Analysis of key calcium-regulating proteins revealed significant differences in kcnj11-null mice. Despite impaired relaxation, kcnj11-null hearts increased phospholamban Ser16 phosphorylation, a modification that results in the dissociation of phospholamban from sarcoendoplasmic reticulum Ca²⁺, thereby increasing sarcoendoplasmic reticulum Ca²⁺-mediated calcium reuptake. However, kcnj11-null mice also had increased 3-nitrotyrosine modification of the sarcoendoplasmic reticulum Ca²⁺-ATPase, a modification that irreversibly impairs sarcoendoplasmic reticulum Ca²⁺ function, thereby contributing to diastolic dysfunction.

CONCLUSIONS

KCNJ11 expression is decreased in human ICM. Lack of kcnj11 expression increases peroxynitrite-mediated modification of the key calcium-handling protein sarcoendoplasmic reticulum Ca²⁺-ATPase after myocardial ischemia-reperfusion injury, contributing to impaired diastolic function. These data suggest a mechanism for ischemia-induced diastolic dysfunction in patients with ICM.

Fundamental Cardiovascular Research: Returns on Societal Investment: A Scientific Statement From the American Heart Association

Recent decades have witnessed robust successes in conquering the acutely lethal manifestations of heart and vascular diseases. Many patients who previously would have died now survive. Lifesaving successes like these provide a tremendous and easily recognized benefit to individuals and society. Although cardiovascular mortality has declined, the devastating impact of chronic heart disease and comorbidities on quality of life and healthcare resources continues unabated. Future strides, extending those made in recent decades, will require continued research into mechanisms underlying disease prevention, pathogenesis, progression, and therapeutic intervention. However, severe financial constraints currently jeopardize these efforts. To chart a path for the future, this report analyzes the challenges and opportunities we face in continuing the battle against cardiovascular disease and highlights the return on societal investment afforded by fundamental cardiovascular research.

Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes

BACKGROUND

One potential use for the PR interval is as a biomarker of disease risk. We hypothesized that quantifying the shared genetic architectures of the PR interval and a set of clinical phenotypes would identify genetic mechanisms contributing to PR variability and identify diseases associated with a genetic predictor of PR variability.

METHODS AND RESULTS

We used ECG measurements from the ARIC study (Atherosclerosis Risk in Communities; n=6731 subjects) and 63 genetically modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978). We measured pairwise genetic correlations (rG) between PR phenotypes (PR interval, PR segment, P-wave duration) and each of the 63 phenotypes. The PR segment was genetically correlated with atrial fibrillation (rG=-0.88; P=0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; P=0.02). A genetically predicted PR interval phenotype based on 645 714 single-nucleotide polymorphisms was associated with atrial fibrillation (odds ratio=0.89 per SD change; 95% confidence interval, 0.83-0.95; P=0.0006). The differing pattern of associations among the PR phenotypes is consistent with analyses that show that the genetic correlation between the P wave and PR segment was not significantly different from 0 (rG=-0.03 [0.16]).

CONCLUSIONS

The genetic architecture of the PR interval comprises modulators of atrial fibrillation risk and obesity.

Azithromycin Causes a Novel Proarrhythmic Syndrome

BACKGROUND

The widely used macrolide antibiotic azithromycin increases risk of cardiovascular and sudden cardiac death, although the underlying mechanisms are unclear. Case reports, including the one we document here, demonstrate that azithromycin can cause rapid, polymorphic ventricular tachycardia in the absence of QT prolongation, indicating a novel proarrhythmic syndrome. We investigated the electrophysiological effects of azithromycin in vivo and in vitro using mice, cardiomyocytes, and human ion channels heterologously expressed in human embryonic kidney (HEK 293) and Chinese hamster ovary (CHO) cells.

METHODS AND RESULTS

In conscious telemetered mice, acute intraperitoneal and oral administration of azithromycin caused effects consistent with multi-ion channel block, with significant sinus slowing and increased PR, QRS, QT, and QTc intervals, as seen with azithromycin overdose. Similarly, in HL-1 cardiomyocytes, the drug slowed sinus automaticity, reduced phase 0 upstroke slope, and prolonged action potential duration. Acute exposure to azithromycin reduced peak SCN5A currents in HEK cells (IC₅₀=110±3 μmol/L) and Na⁺ current in mouse ventricular myocytes. However, with chronic (24 hour) exposure, azithromycin caused a ≈2-fold increase in both peak and late SCN5A currents, with findings confirmed for I_Na in cardiomyocytes. Mild block occurred for K⁺ currents representing I_Kr (CHO cells expressing hERG; IC₅₀=219±21 μmol/L) and I_Ks (CHO cells expressing KCNQ1+KCNE1; IC₅₀=184±12 μmol/L), whereas azithromycin suppressed L-type Ca⁺⁺ currents (rabbit ventricular myocytes, IC₅₀=66.5±4 μmol/L) and I_K1 (HEK cells expressing Kir2.1, IC₅₀=44±3 μmol/L).

CONCLUSIONS

Chronic exposure to azithromycin increases cardiac Na⁺ current to promote intracellular Na⁺ loading, providing a potential mechanistic basis for the novel form of proarrhythmia seen with this macrolide antibiotic.

Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics

The potential impact of using human genetic data linked to longitudinal electronic medical records on drug development is extraordinary; however, the practical application of these data necessitates some organizational innovations. Vanderbilt has created resources such as an easily queried database of >2.6 million de-identified electronic health records linked to BioVU, which is a DNA biobank with more than 230,000 unique samples. To ensure these data are used to maximally benefit and accelerate both de novo drug discovery and drug repurposing efforts, we created the Accelerating Drug Development and Repurposing Incubator, a multidisciplinary think tank of experts in various therapeutic areas within both basic and clinical science as well as experts in legal, business, and other operational domains. The Incubator supports a diverse pipeline of drug indication finding projects, leveraging the natural experiment of human genetics.

Phenome-wide association studies: a new method for functional genomics in humans

In experimental physiological research, a common study design for examining the functional role of a gene or a genetic variant is to introduce that genetic variant into a model organism (such as yeast or mouse) and then to search for phenotypic consequences. The development of DNA biobanks linked to dense phenotypic information enables such an experiment to be applied to human subjects in the form of a phenome-wide association study (PheWAS). The PheWAS paradigm takes advantage of a curated medical phenome, often derived from electronic health records, to search for associations between 'input functions' and phenotypes in an unbiased fashion. The most commonly studied input function to date has been single nucleotide polymorphisms (SNPs), but other inputs, such as sets of SNPs or a disease or drug exposure, are now being explored to probe the genetic and phenotypic architecture of human traits. Potential outcomes of these approaches include defining subsets of complex diseases (that can then be targeted by specific therapies) and drug repurposing.

Genome-wide study of resistant hypertension identified from electronic health records

Resistant hypertension is defined as high blood pressure that remains above treatment goals in spite of the concurrent use of three antihypertensive agents from different classes. Despite the important health consequences of resistant hypertension, few studies of resistant hypertension have been conducted. To perform a genome-wide association study for resistant hypertension, we defined and identified cases of resistant hypertension and hypertensives with treated, controlled hypertension among >47,500 adults residing in the US linked to electronic health records (EHRs) and genotyped as part of the electronic MEdical Records & GEnomics (eMERGE) Network. Electronic selection logic using billing codes, laboratory values, text queries, and medication records was used to identify resistant hypertension cases and controls at each site, and a total of 3,006 cases of resistant hypertension and 876 controlled hypertensives were identified among eMERGE Phase I and II sites. After imputation and quality control, a total of 2,530,150 SNPs were tested for an association among 2,830 multi-ethnic cases of resistant hypertension and 876 controlled hypertensives. No test of association was genome-wide significant in the full dataset or in the dataset limited to European American cases (n = 1,719) and controls (n = 708). The most significant finding was CLNK rs13144136 at p = 1.00x10^-6 (odds ratio = 0.68; 95% CI = 0.58–0.80) in the full dataset with similar results in the European American only dataset. We also examined whether SNPs known to influence blood pressure or hypertension also influenced resistant hypertension. None was significant after correction for multiple testing. These data highlight both the difficulties and the potential utility of EHR-linked genomic data to study clinically-relevant traits such as resistant hypertension.

Pharmacogenetics of Potassium Channel Blockers

The QT interval on surface electrocardiograms provides a model of a multicomponent integrated readout of many biological systems, including ion channels, modulatory subunits, signaling systems that modulate their activity, and mechanisms that regulate the expression of their responsible genes. The problem of drug exposure causing exaggerated QT interval prolongation and torsades de pointes highlights the multicomponent nature of cardiac repolarization and the way in which simple perturbations can yield exaggerated responses. Future directions will involve cellular approaches coupled to evolving technologies that can interrogate multicellular systems and provide a sophisticated view of mechanisms in this previously idiosyncratic drug reaction.

Common Genetic Variant Risk Score Is Associated With Drug-Induced QT Prolongation and Torsade de Pointes Risk: A Pilot Study

BACKGROUND

Drug-induced QT interval prolongation, a risk factor for life-threatening ventricular arrhythmias, is a potential side effect of many marketed and withdrawn medications. The contribution of common genetic variants previously associated with baseline QT interval to drug-induced QT prolongation and arrhythmias is not known.

METHODS

We tested the hypothesis that a weighted combination of common genetic variants contributing to QT interval at baseline, identified through genome-wide association studies, can predict individual response to multiple QT-prolonging drugs. Genetic analysis of 22 subjects was performed in a secondary analysis of a randomized, double-blind, placebo-controlled, crossover trial of 3 QT-prolonging drugs with 15 time-matched QT and plasma drug concentration measurements. Subjects received single doses of dofetilide, quinidine, ranolazine, and placebo. The outcome was the correlation between a genetic QT score comprising 61 common genetic variants and the slope of an individual subject's drug-induced increase in heart rate-corrected QT (QTc) versus drug concentration.

RESULTS

The genetic QT score was correlated with drug-induced QTc prolongation. Among white subjects, genetic QT score explained 30% of the variability in response to dofetilide (r=0.55; 95% confidence interval, 0.09-0.81; P=0.02), 23% in response to quinidine (r=0.48; 95% confidence interval, -0.03 to 0.79; P=0.06), and 27% in response to ranolazine (r=0.52; 95% confidence interval, 0.05-0.80; P=0.03). Furthermore, the genetic QT score was a significant predictor of drug-induced torsade de pointes in an independent sample of 216 cases compared with 771 controls (r²=12%, P=1×10^-7).

CONCLUSIONS

We demonstrate that a genetic QT score comprising 61 common genetic variants explains a significant proportion of the variability in drug-induced QT prolongation and is a significant predictor of drug-induced torsade de pointes. These findings highlight an opportunity for recent genetic discoveries to improve individualized risk-benefit assessment for pharmacological therapies. Replication of these findings in larger samples is needed to more precisely estimate variance explained and to establish the individual variants that drive these effects.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT01873950.

Identification of unique venous thromboembolism-susceptibility variants in African-Americans

To identify novel single nucleotide polymorphisms (SNPs) associated with venous thromboembolism (VTE) in African-Americans (AAs), we performed a genome-wide association study (GWAS) of VTE in AAs using the Electronic Medical Records and Genomics (eMERGE) Network, comprised of seven sites each with DNA biobanks (total ~39,200 unique DNA samples) with genome-wide SNP data (imputed to 1000 Genomes Project cosmopolitan reference panel) and linked to electronic health records (EHRs). Using a validated EHR-driven phenotype extraction algorithm, we identified VTE cases and controls and tested for an association between each SNP and VTE using unconditional logistic regression, adjusted for age, sex, stroke, site-platform combination and sickle cell risk genotype. Among 393 AA VTE cases and 4,941 AA controls, three intragenic SNPs reached genome-wide significance: LEMD3 rs138916004 (OR=3.2; p=1.3E-08), LY86 rs3804476 (OR=1.8; p=2E-08) and LOC100130298 rs142143628 (OR=4.5; p=4.4E-08); all three SNPs validated using internal cross-validation, parametric bootstrap and meta-analysis methods. LEMD3 rs138916004 and LOC100130298 rs142143628 are only present in Africans (1000G data). LEMD3 showed a significant differential expression in both NCBI Gene Expression Omnibus (GEO) and the Mayo Clinic gene expression data, LOC100130298 showed a significant differential expression only in the GEO expression data, and LY86 showed a significant differential expression only in the Mayo expression data. LEMD3 encodes for an antagonist of TGF-β-induced cell proliferation arrest. LY86 encodes for MD-1 which down-regulates the pro-inflammatory response to lipopolysaccharide; LY86 variation was previously associated with VTE in white women; LOC100130298 is a non-coding RNA gene with unknown regulatory activity in gene expression and epigenetics.

PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

BACKGROUND

Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk.

METHODS

In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores.

FINDINGS

Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m2, -0·09 to 0·30).

INTERPRETATION

PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibitor treatment, as was previously done for statins.

FUNDING

British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National Institute for Health Research (NIHR) Biomedical Research Centre.

STRATEGIES FOR EQUITABLE PHARMACOGENOMIC-GUIDED WARFARIN DOSING AMONG EUROPEAN AND AFRICAN AMERICAN INDIVIDUALS IN A CLINICAL POPULATION

The blood thinner warfarin has a narrow therapeutic range and high inter- and intra-patient variability in therapeutic doses. Several studies have shown that pharmacogenomic variants help predict stable warfarin dosing. However, retrospective and randomized controlled trials that employ dosing algorithms incorporating pharmacogenomic variants under perform in African Americans. This study sought to determine if: 1) including additional variants associated with warfarin dose in African Americans, 2) predicting within single ancestry groups rather than a combined population, or 3) using percentage African ancestry rather than observed race, would improve warfarin dosing algorithms in African Americans. Using BioVU, the Vanderbilt University Medical Center biobank linked to electronic medical records, we compared 25 modeling strategies to existing algorithms using a cohort of 2,181 warfarin users (1,928 whites, 253 blacks). We found that approaches incorporating additional variants increased model accuracy, but not in clinically significant ways. Race stratification increased model fidelity for African Americans, but the improvement was small and not likely to be clinically significant. Use of percent African ancestry improved model fit in the context of race misclassification.

Pharmacogenetic Implementation Lessons From the "Real World"

The manuscript "Anticoagulation Endpoints With Clinical Implementation of Warfarin Pharmacogenetic Dosing in a Real- World Setting: A Proposal for a New Pharmacogenetic Dosing Approach" describes process outcomes in an institutional program to use pharmacogenetic testing to optimize warfarin dose in a cohort of 257 patients of diverse ancestries. The strengths and weaknesses of the approach and program are discussed, along with the current and potential future status of warfarin as a model for pharmacogenetic testing.

Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Importance

Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and health care expenditures. Accurate prediction of incident AF would enhance AF management and potentially improve patient outcomes.

Objective

To validate the AF risk prediction model originally developed by the Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) investigators using a large repository of electronic medical records (EMRs).

Design, Setting, and Participants

In this prediction model study, deidentified EMRs of 33 494 individuals 40 years or older who were white or African American and had no history of AF were reviewed and analyzed. The participants were followed up in the internal medicine outpatient clinics at Vanderbilt University Medical Center for incident AF from December 31, 2005, until December 31, 2010. Adjusting for differences in baseline hazard, the CHARGE-AF Cox proportional hazards model regression coefficients were applied to the EMR cohort. A simple version of the model with no echocardiographic variables was also evaluated. Data were analyzed from October 31, 2013, to January 31, 2014.

Main Outcomes and Measures

Incident AF. Predictors in the model included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, type 2 diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval.

Results

Among the 33 494 participants, the median age was 57 (interquartile range, 49-67) years; 57% of patients were women, 43% were men, 85.7% were white, and 14.3% were African American. During the mean (SD) follow-up of 4.8 (0.9) years, 2455 individuals (7.3%) developed AF. Both models had poor calibration in the EMR cohort, with underprediction of AF among low-risk individuals and overprediction of AF among high-risk individuals (10th and 90th percentiles for predicted probability of incident AF, 0.005 and 0.179, respectively). The full CHARGE-AF model had a C index of 0.708 (95% CI, 0.699-0.718) in our cohort. The simple model had similar discrimination (C index, 0.709; 95% CI, 0.699-0.718; P = .70 for difference between models).

Conclusions and Relevance

Despite reasonable discrimination, the CHARGE-AF models showed poor calibration in this EMR cohort. This study highlights the difficulties of applying a risk model derived from prospective cohort studies to an EMR cohort and suggests that these AF risk prediction models be used with caution in the EMR setting. Future risk models may need to be developed and validated within EMR cohorts.

Genomic contributors to atrial electroanatomical remodeling and atrial fibrillation progression: Pathway enrichment analysis of GWAS data

In atrial fibrillation (AF), left atrial diameter (LAD) and low voltage area (LVA) are intermediate phenotypes that are associated with AF type and progression. In this study, we tested the hypothesis, that these phenotypes share common, genetically-determined pathways using pathway enrichment analysis of GWAS data. Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) were genotyped for ~1,000,000 SNPs. SNPs found significantly associated with LAD, LVA or AF type were used for gene-based association tests in a systematic biological Knowledge-based mining system for Genome-wide Genetic studies (KGG). Associated genes were tested for pathway enrichment using two enrichment tools (WebGestalt and GATHER) and the databases provided by Kyoto Encyclopedia of Genes and Genomes. The calcium signaling pathway (hsa04020) was the only pathway that reached statistical significance for LAD and LVA in both enrichment tools and was also significantly associated with AF type. Within this pathway, there were 39 genes (i.e. CACNA1C, RyR2) that were associated with LAD, LVA and AF type. In conclusion, there is a genomic contribution to electroanatomical remodeling (LAD, LVA) and AF type via the calcium signaling pathway. Future and larger studies are necessary to replicate and apply these findings.

Fulminant Myocarditis with Combination Immune Checkpoint Blockade

Immune checkpoint inhibitors have improved clinical outcomes associated with numerous cancers, but high-grade, immune-related adverse events can occur, particularly with combination immunotherapy. We report the cases of two patients with melanoma in whom fatal myocarditis developed after treatment with ipilimumab and nivolumab. In both patients, there was development of myositis with rhabdomyolysis, early progressive and refractory cardiac electrical instability, and myocarditis with a robust presence of T-cell and macrophage infiltrates. Selective clonal T-cell populations infiltrating the myocardium were identical to those present in tumors and skeletal muscle. Pharmacovigilance studies show that myocarditis occurred in 0.27% of patients treated with a combination of ipilimumab and nivolumab, which suggests that our patients were having a rare, potentially fatal, T-cell-driven drug reaction. (Funded by Vanderbilt-Ingram Cancer Center Ambassadors and others.).

Contrasting Nav1.8 Activity in Scn10a-/- Ventricular Myocytes and the Intact Heart

BACKGROUND

Genome-wide association studies have implicated variants in SCN10A, which encodes Nav1.8, as modulators of cardiac conduction. Follow-up work has indicated the SCN10A sequence includes an intronic enhancer for SCN5A. Yet the role of the Nav1.8 protein in the myocardium itself is still unclear. To investigate this, we use homozygous knockout mice (Scn10a^-/-) generated by disruption of exons 4 and 5, leaving the Scn5a enhancer intact.

METHODS AND RESULTS

We previously reported that pharmacologic blockade of Nav1.8 in wild-type animals blunts action potential prolongation by ATX-II at slow drive rates (≤1 Hz). Here we present evidence of the same blunting in Scn10a^-/- compared to wild-type ventricular myocytes, supporting the conclusion that Nav1.8 contributes to late sodium current at slow rates. In contrast to earlier studies, we found no differences in electrocardiographic parameters between genotypes. Low-dose ATX-II exposure in lightly anesthetized animals and Langendorff-perfused hearts prolonged QTc and generated arrhythmias to the same extent in wild-type and Scn10a^-/-. RNA sequencing failed to identify full-length Scn10a transcripts in wild-type or knockout isolated ventricular myocytes. However, loss of late current in Scn10a^-/- myocytes was replicated independently in a blinded set of experiments.

CONCLUSIONS

While Scn10a transcripts are not detectible in ventricular cardiomyocytes, gene deletion results in reproducible loss of late sodium current under extreme experimental conditions. However, there are no identifiable consequences of this Scn10a deletion in the intact mouse heart at usual rates. These findings argue that common variants in SCN10A that affect ventricular conduction do so by modulating SCN5A.

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability

OBJECTIVE

Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites.

RESULTS

As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%).

DISCUSSION

These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others.

CONCLUSION

By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.

A Longitudinal HbA1c Model Elucidates Genes Linked to Disease Progression on Metformin

One-third of type-2 diabetic patients respond poorly to metformin. Despite extensive research, the impact of genetic and nongenetic factors on long-term outcome is unknown. In this study we combine nonlinear mixed effect modeling with computational genetic methodologies to identify predictors of long-term response. In all, 1,056 patients contributed their genetic, demographic, and long-term HbA1c data. The top nine variants (of 12,000 variants in 267 candidate genes) accounted for approximately one-third of the variability in the disease progression parameter. Average serum creatinine level, age, and weight were determinants of symptomatic response; however, explaining negligible variability. Two single nucleotide polymorphisms (SNPs) in CSMD1 gene (rs2617102, rs2954625) and one SNP in a pharmacologically relevant SLC22A2 gene (rs316009) influenced disease progression, with minor alleles leading to less and more favorable outcomes, respectively. Overall, our study highlights the influence of genetic factors on long-term HbA1c response and provides a computational model, which when validated, may be used to individualize treatment.