High-throughput genetic analyses of drug intolerances using electronic health records
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PROVIDER: PRJNA683675 | ENA |
REPOSITORIES: ENA
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Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:RATIONALE: Use of a web-based risk assessment tool may help improve screening rates for colorectal cancer.
PURPOSE: This randomized clinical trial is studying personal electronic health records to see how well they work in improving screening rates for colorectal cancer.
| 2082240 | ecrin-mdr-crc
Project description:Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions (ADRs) using "drug allergy" labels from electronic health records (EHRs). As a proof-of-concept, we conducted GWAS for ADRs to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Center's BioVU DNA Biobank. We identified 7 genetic loci associated with ADRs at P < 5 × 10-8, including known genetic associations such as CYP2D6 and OPRM1 for CYP2D6-metabolized opioid ADR. Additional expression quantitative trait loci and phenome-wide association analyses added evidence to the observed associations. Our high-throughput framework is both scalable and portable, enabling impactful pharmacogenomic research to improve precision medicine.
| S-EPMC8195357 | biostudies-literature
Project description:<p>An important potential enabling resource for Personalized Medicine is the combination of a DNA repository with Electronic Medical Record (EMR) systems sufficiently robust to provide excellence in clinical care and to serve as resources for analysis of disease susceptibility and therapeutic outcomes across patient populations. The Vanderbilt EMR is a state of the art clinical and research tool (that includes >1.7 million records), and is associated with a DNA repository which has been in development for over 3 years; these are the key components of VGER, the Vanderbilt Genome-Electronic Records project, a part of NHGRI's eMERGE network. The VGER model acquires DNA from discarded blood samples collected from routine patient care, and can link these to de-identified data extracted and readily updated from the EMR. The phenotype we analyze here is the QRS duration on the electrocardiogram, since slow conduction (indicated by longer QRS duration) is a marker of arrhythmia susceptibility.</p>
| phs000188.v1.p1 | EGA
Project description:BACKGROUND:Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes. METHODS:We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative clinical notes. Estimates of Research Domain Criteria loading were derived from a cohort of 3619 individuals with 4623 hospital admissions. We applied this tool to a large corpus of psychiatric inpatient admission and discharge notes (2010-2015), and using the same cohort we examined face validity, predictive validity, and convergent validity with gold standard annotations. RESULTS:In mixed-effect models adjusted for sociodemographic and clinical features, greater negative and positive symptom domains were associated with a shorter length of stay (? = -.88, p = .001 and ? = -1.22, p < .001, respectively), while greater social and arousal domain scores were associated with a longer length of stay (? = .93, p < .001 and ? = .81, p = .007, respectively). In fully adjusted Cox regression models, a greater positive domain score at discharge was also associated with a significant increase in readmission risk (hazard ratio = 1.22, p < .001). Positive and negative valence domains were correlated with expert annotation (by analysis of variance [df = 3], R2 = .13 and .19, respectively). Likewise, in a subset of patients, neurocognitive testing was correlated with cognitive performance scores (p < .008 for three of six measures). CONCLUSIONS:This shows that natural language processing can be used to efficiently and transparently score clinical notes in terms of cognitive and psychopathologic domains.
| S-EPMC5972065 | biostudies-literature
Project description:Electronic cigarettes (e-cigarettes) have gained their popularity as a substitute for cigarettes or cigars. Despite the widespread use of flavoring chemicals in e-cigarettes, the health impacts of the flavoring compounds, in particular their effects on critical cellular function in the lung, remain largely unknown. The goal of this study was to identify transcriptomic changes and impacted biological pathways in primary human bronchial epithelial cells (HBECs) exposed to flavoring chemicals (diacetyl or 2,3-pentanedione) and to flavored e-cigarette smoke. An airway-liquid interface culturing method was used to differentiate primary human bronchial epithelial cells (HBECs) into mature epithelial cells, which were then treated with 25 ppm diacetyl, 100 ppm 2,3 pentanedione, or e-cigarette smoke solution containing 2 ppm diacetyl. Poly(A)-selected RNA-Seq libraries were prepared with the PrepX RNA-Seq for Illumina Library kit. An Illumina HiSeq 2500 instrument was used to generate 50 base pair single-end reads. STAR was used to align sequencing reads to the hg38 reference genome, and HTSeq was used to quantify transcript levels. DESeq2 was used to perform differential expression analysis.
2019-08-21 | GSE118431 | GEO
Project description:OBJECTIVE:The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records. MATERIALS AND METHODS:We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices. RESULTS:Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: "will monitor or take precautions," "not clinically significant," and "benefit outweighs risk." DISCUSSION:We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved. CONCLUSIONS:Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.
| S-EPMC6748816 | biostudies-literature
Project description:Background and objectiveSeveral studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs).MethodsWe prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.ResultsWe collected information for 5983 putative EHR-derived drug-drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug-drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.ConclusionsOur proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.
| S-EPMC4712252 | biostudies-literature
Project description:Analysis of primary human bronchial epithelial cells grown in air liquid interface, exposed in vitro to whole tobacco cigarette smoke (48 puffs, 48 minutes) and electronic cigarette aerosol (400 puffs, 200 minutes). Electronic cigarette exposures included two flavors (menthol, tobacco) both with, and without nicotine.
2016-10-12 | GSE82137 | GEO