Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin

Xu, Hua; Jiang, Min; Oetjens, Matthew T.; Bowton, Erica; Ramirez, Andrea H.; Jeff, Janina M.; Basford, Melissa A.; Pulley, Jill M.; Cowan, James D.; Wang, Xiaoming; Ritchie, Marylyn D.; Masys, Daniel R.; Roden, Dan M.; Crawford, Dana C.; Denny, Joshua C.

doi:10.1136/amiajnl-2011-000208

Cited by 67 publications

(54 citation statements)

References 27 publications

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“…Medications were available from structured electronic prescribing records and also extracted from narrative documents using MedEx. 28,29 Hypertension medications were determined using medication strings with indications determined as part of MedicationIndication resource-High Performance Subset (MEDI-HPS), which lists on-and off-label indications of medications (Supplemental Table 3). [30][31][32][33] We used the hypertensive blood pressure guideline thresholds of 140 mmHg systolic and 90 mmHg diastolic.…”

Section: Input Feature Developmentmentioning

confidence: 99%

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Teixeira

Wei

Cronin

et al. 2016

Journal of the American Medical Informatics Association

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Objective: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time-and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites. Materials and Methods: We reviewed the EHRs of 631 individuals followed at Vanderbilt for hypertension status. We developed features and phenotyping algorithms of increasing complexity. Input categories included International Classification of Diseases, Ninth Revision (ICD9) codes, medications, vital signs, narrative-text search results, and Unified Medical Language System (UMLS) concepts extracted using natural language processing (NLP). We developed a module and tested portability by replicating 10 of the best-performing algorithms at the Marshfield Clinic. Results: Random forests using billing codes, medications, vitals, and concepts had the best performance with a median area under the receiver operator characteristic curve (AUC) of 0.976. Normalized sums of all 4 categories also performed well (0.959 AUC). The best non-NLP algorithm combined normalized ICD9 codes, medications, and blood pressure readings with a median AUC of 0.948. Blood pressure cutoffs or ICD9 code counts alone had AUCs of 0.854 and 0.908, respectively. Marshfield Clinic results were similar. Conclusion: This work shows that billing codes or blood pressure readings alone yield good hypertension classification performance. However, even simple combinations of input categories improve performance. The most complex algorithms classified hypertension with excellent recall and precision.

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Section: Input Feature Developmentmentioning

confidence: 99%

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Teixeira

Wei

Cronin

et al. 2016

Journal of the American Medical Informatics Association

Self Cite

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“…Records were also queried for calculated LDL-C. Prescription medication is available in the SD through MedEx [43], an algorithm that extracts medications and their signature mentions from free-text entries available in the EMR.…”

Section: Methodsmentioning

confidence: 99%

Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits

Crawford¹,

Goodloe²,

Farber‐Eger³

et al. 2015

Hum Hered

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Background/Aims: Present-day limited resources demand DNA and phenotyping alternatives to the traditional prospective population-based epidemiologic collections. Methods: To accelerate genomic discovery with an emphasis on diverse populations, we - as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study - accessed all non-European American samples (n = 15,863) available in BioVU, the Vanderbilt University biorepository linked to de-identified electronic medical records, for genomic studies as part of the larger Population Architecture using Genomics and Epidemiology (PAGE) I study. Given previous studies have cautioned against the secondary use of clinically collected data compared with epidemiologically collected data, we present here a characterization of EAGLE BioVU, including the billing and diagnostic (ICD-9) code distributions for adult and pediatric patients as well as comparisons made for select health metrics (body mass index, glucose, HbA1c, HDL-C, LDL-C, and triglycerides) with the population-based National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples (NHANES III, n = 7,159; NHANES 1999-2002, n = 7,839). Results: Overall, the distributions of billing and diagnostic codes suggest this clinical sample is a mixture of healthy and sick patients like that expected for a contemporary American population. Conclusion: Little bias is observed among health metrics, suggesting this clinical collection is suitable for genomic studies along with traditional epidemiologic cohorts.

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“…We used only medications with at least one mention of a dose, route, frequency or strength to limit the medications to those the patient was actually prescribed. A more detailed description of the software has been published elsewhere [12]. …”

Section: Methodsmentioning

confidence: 99%

Utilization of an Emr-Biorepository to Identify the Genetic Predictors of Calcineurin-Inhibitor Toxicity in Heart Transplant Recipients

et al. 2013

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Calcineurin-inhibitors CI are immunosuppressive agents prescribed to patients after solid organ transplant to prevent rejection. Although these drugs have been transformative for allograft survival, long-term use is complicated by side effects including nephrotoxicity. Given the narrow therapeutic index of CI, therapeutic drug monitoring is used to prevent acute rejection from underdosing and acute toxicity from overdosing, but drug monitoring does not alleviate long-term side effects. Patients on calcineurin-inhibitors for long periods almost universally experience declines in renal function, and a subpopulation of transplant recipients ultimately develop chronic kidney disease that may progress to end stage renal disease attributable to calcineurin inhibitor toxicity (CNIT). Pharmacogenomics has the potential to identify patients who are at high risk for developing advanced chronic kidney disease caused by CNIT and providing them with existing alternate immunosuppressive therapy. In this study we utilized BioVU, Vanderbilt University Medical Center’s DNA biorepository linked to de-identified electronic medical records to identify a cohort of 115 heart transplant recipients prescribed calcineurin-inhibitors to identify genetic risk factors for CNIT We identified 37 cases of nephrotoxicity in our cohort, defining nephrotoxicity as a monthly median estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73m2 at least six months post-transplant for at least three consecutive months. All heart transplant patients were genotyped on the Illumina ADME Core Panel, a pharmacogenomic genotyping platform that assays 184 variants across 34 genes. In Cox regression analysis adjusting for age at transplant, pre-transplant chronic kidney disease, pre-transplant diabetes, and the three most significant principal components (PCAs), we did not identify any markers that met our multiple-testing threshold. As a secondary analysis we also modeled post-transplant eGFR directly with linear mixed models adjusted for age at transplant, cyclosporine use, median BMI, and the three most significant principal components. While no SNPs met our threshold for significance, a SNP previously identified in genetic studies of the dosing of tacrolimus CYP3A5 rs776746, replicated in an adjusted analysis at an uncorrected p-value of 0.02 (coeff(S.E.) = 14.60(6.41)). While larger independent studies will be required to further validate this finding, this study underscores the EMRs usefulness as a resource for longitudinal pharmacogenetic study designs.

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Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin

Cited by 67 publications

References 27 publications

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits

Utilization of an Emr-Biorepository to Identify the Genetic Predictors of Calcineurin-Inhibitor Toxicity in Heart Transplant Recipients

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