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The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.

ABSTRACT: BACKGROUND:Biorepositories linked to de-identified electronic medical records (EMRs) have the potential to complement traditional epidemiologic studies in genotype-phenotype studies of complex human diseases and traits. A major challenge in meeting this potential is the use of EMR-derived data to extract phenotypes and covariates for genetic association studies. Unlike traditional epidemiologic data, EMR-derived data are collected for clinical care and are therefore highly variable across patients. The variability of clinical data coupled with the challenges associated with searching unstructured clinical notes requires the development of algorithms to extract phenotypes for analysis. Given the number of possible algorithms that could be developed for any one EMR-derived phenotype, we explored here the impact algorithm decision logic has on genetic association study results for a single quantitative trait, high density lipoprotein cholesterol (HDL-C). RESULTS:We used five different algorithms to extract HDL-C from African American subjects genotyped on the Illumina Metabochip (n?=?11,519) as part of Epidemiologic Architecture for Genes Linked to Environment (EAGLE). Tests of association between HDL-C and genetic risk scores for HDL-C associated variants suggest that the genetic effect size does not vary substantially across the five HDL-C definitions. CONCLUSIONS:These data collectively suggest that, at least for this quantitative trait, algorithm decision logic and phenotyping details do not appreciably impact genetic association study test statistics.

SUBMITTER: Dumitrescu L

PROVIDER: S-EPMC4428098 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.

Dumitrescu Logan L Goodloe Robert R Bradford Yukiko Y Farber-Eger Eric E Boston Jonathan J Crawford Dana C DC

BioData mining 20150506

<h4>Background</h4>Biorepositories linked to de-identified electronic medical records (EMRs) have the potential to complement traditional epidemiologic studies in genotype-phenotype studies of complex human diseases and traits. A major challenge in meeting this potential is the use of EMR-derived data to extract phenotypes and covariates for genetic association studies. Unlike traditional epidemiologic data, EMR-derived data are collected for clinical care and are therefore highly variable acros ...[more]

PMID: 25969697

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Project description:BackgroundElectronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.ObjectiveThis review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.MethodsA scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.ResultsA total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule-based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.ConclusionsRecognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Project description:ObjectiveElectronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping.MethodsTwo relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance.ResultsWe developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003).DiscussionILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.ConclusionRelational learning using ILP offers a viable approach to EHR-driven phenotyping.

Project description:ImportanceWhile dosing reminders for glaucoma medications have been shown to increase drug adherence, an outstanding limitation preventing broad adoption is difficulty linking reminders to medication in each patient's electronic health record (EHR).ObjectiveTo examine the feasibility of implementing an EHR-linked, automated reminder system for glaucoma medications and assess patient satisfaction with it.Design, setting, and participantsIn this prospective, cross-sectional study, patients receiving glaucoma medications were recruited from a university-based glaucoma clinic from April 2017 to January 2018. Analysis began in February 2018.ExposuresA web-based application was added to the EHR patient portal, allowing patients to configure telecommunication-based reminders for their glaucoma medications. For all potential participants, basic information was collected to determine risk of nonadherence with glaucoma medications. Those consenting to participate then answered questions on medication adherence, reminders, and their likelihood of using EHR-linked reminders. They then configured reminders using the EHR patient portal. After 3 months, they answered questions about their satisfaction with the system.Main outcomes and measuresProportions of patients and high-risk patients willing to use the reminders, and patient satisfaction with the system.ResultsOf 147 patients approached, 100 (68%) agreed to participate, of whom the majority were men (54 [54%]) and white (51 [51%]), with a mean (SD) age of 65 (12.4) years. There were no differences between those willing and unwilling to participate, except for a slightly lower self-reported medication adherence rate among those who participated (mean [SD], 91% [13.6%] vs 97% [6.0%]). Based on a previously validated risk assessment score, 9% (9 of 100) of participants were categorized as being at high risk for poor adherence (≥50% probability of nonadherence) compared with 11% (5 of 47) of nonparticipants. Of 100 participants, 94 ultimately configured reminders, of whom 89 (95%) completed follow-up. On follow-up, 74% (n = 66; 95% CI, 65-83) of participants found the reminders to be useful, 15% (n = 13) were neutral, and 11% (n = 10) found them not useful. Most participants (72 [81%]; 95% CI, 77-85) had help configuring reminders.Conclusions and relevanceElectronic health records-linked reminders had good uptake and were well received among patients at a tertiary glaucoma clinic. These portals may represent a new and convenient method of allowing patients to link their glaucoma medications to automated reminders, although the generalizability of these results and the effect on glaucoma outcomes remains unknown.

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The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.

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The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.

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