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Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

ABSTRACT: Genome-wide association studies (GWAS) require high specificity and large numbers of subjects to identify genotype-phenotype correlations accurately. The aim of this study was to identify type 2 diabetes (T2D) cases and controls for a GWAS, using data captured through routine clinical care across five institutions using different electronic medical record (EMR) systems.An algorithm was developed to identify T2D cases and controls based on a combination of diagnoses, medications, and laboratory results. The performance of the algorithm was validated at three of the five participating institutions compared against clinician review. A GWAS was subsequently performed using cases and controls identified by the algorithm, with samples pooled across all five institutions.The algorithm achieved 98% and 100% positive predictive values for the identification of diabetic cases and controls, respectively, as compared against clinician review. By standardizing and applying the algorithm across institutions, 3353 cases and 3352 controls were identified. Subsequent GWAS using data from five institutions replicated the TCF7L2 gene variant (rs7903146) previously associated with T2D.By applying stringent criteria to EMR data collected through routine clinical care, cases and controls for a GWAS were identified that subsequently replicated a known genetic variant. The use of standard terminologies to define data elements enabled pooling of subjects and data across five different institutions to achieve the robust numbers required for GWAS.An algorithm using commonly available data from five different EMR can accurately identify T2D cases and controls for genetic study across multiple institutions.

SUBMITTER: Kho AN

PROVIDER: S-EPMC3277617 | biostudies-literature | 2012 Mar-Apr

REPOSITORIES: biostudies-literature

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Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

Kho Abel N AN Hayes M Geoffrey MG Rasmussen-Torvik Laura L Pacheco Jennifer A JA Thompson William K WK Armstrong Loren L LL Denny Joshua C JC Peissig Peggy L PL Miller Aaron W AW Wei Wei-Qi WQ Bielinski Suzette J SJ Chute Christopher G CG Leibson Cynthia L CL Jarvik Gail P GP Crosslin David R DR Carlson Christopher S CS Newton Katherine M KM Wolf Wendy A WA Chisholm Rex L RL Lowe William L WL

Journal of the American Medical Informatics Association : JAMIA 20111119 2

<h4>Objective</h4>Genome-wide association studies (GWAS) require high specificity and large numbers of subjects to identify genotype-phenotype correlations accurately. The aim of this study was to identify type 2 diabetes (T2D) cases and controls for a GWAS, using data captured through routine clinical care across five institutions using different electronic medical record (EMR) systems.<h4>Materials and methods</h4>An algorithm was developed to identify T2D cases and controls based on a combina ...[more]

PMID: 22101970

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Project description:AimsUnderstanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.MethodsPatients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information.ResultsOf 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01).ConclusionOur EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

Project description:BackgroundOne of the main objectives of Electronic Health Records (EHRs) is to enhance collaboration among healthcare professionals. However, our knowledge of how EHRs actually affect collaborative practices is limited. This study examines how an EHR facilitates and constrains collaboration in five outpatient clinics.MethodsWe conducted an embedded case study at five outpatient clinics of a Dutch hospital that had implemented an organization-wide EHR. Data were collected through interviews with representatives of medical specialties, administration, nursing, and management. Documents were analyzed to contextualize these data. We examined the following collaborative affordances of EHRs: (1) portability, (2) co-located access, (3) shared overviews, (4) mutual awareness, (5) messaging, and (6) orchestrating.ResultsOur findings demonstrate how an EHR will both facilitate and constrain collaboration among specialties and disciplines. Affordances that were inscribed in the system for collaboration purposes were not fully actualized in the hospital because: (a) The EHR helps health professionals coordinate patient care on an informed basis at any time and in any place but only allows asynchronous patient record use. (b) The comprehensive patient file affords joint clinical decision-making based on shared data, but specialty- and discipline-specific user-interfaces constrain mutual understanding of that data. Moreover, not all relevant information can be easily shared across specialties and outside the hospital. (c) The reduced necessity for face-to-face communication saves time but is experienced as hindering collective responsibility for a smooth workflow. (d) The EHR affords registration at the source and registration of activities through orders, but the heightened administrative burden for physicians and the strict authorization rules on inputting data constrain the flexible, multidisciplinary collaboration. (e) While the EHR affords a complete overview, information overload occurs due to the parallel generation of individually owned notes and the high frequency of asynchronous communication through messages of varying clinical priority.ConclusionsFor the optimal actualization of EHRs' collaborative affordances in hospitals, coordinated use of these affordances by health professionals is a prerequisite. Such coordinated use requires organizational, technical, and behavioral adaptations. Suggestions for hospital-wide policies to enhance trust in both the EHR and in its coordinated use for effective collaboration are offered.

Project description:BACKGROUND AND AIMS:Screening for substance use disorder (SUD) in general medical settings may be particularly important in patients with comorbid health conditions exacerbated by SUD. This study evaluated whether SUD is associated with type 2 diabetes mellitus (T2DM) complications in patients with co-occurring T2DM and hypertension. DESIGN:Analysis of a limited data set obtained through IBM Watson Health Explorys, a platform integrating data from electronic health records. Matched controls were defined for each of five SUDs: tobacco use disorder (TUD), opioid use disorder (OUD), cocaine use disorder, cannabis use disorder (CUD) and alcohol use disorder (AUD) using Mahalanobis distance within propensity score calipers. SETTING:All patients were seen in the MetroHealth System (Cleveland, OH, USA) and had diagnosis codes for T2DM and hypertension. PARTICIPANTS:SUD group participants had a diagnosis of abuse/dependence for the substance of interest. Controls for each SUD group had no diagnosis code related to the SUD of interest and were selected to match the SUD patients on demographics, residential zip code median income and body mass index. Total sample sizes for each SUD-control comparison ranged from 1160 for CUD to 22?128 for TUD. MEASUREMENTS:Outcome was diagnosis (yes/no) of four T2DM complications (cerebrovascular accident, diabetic neuropathy, diabetic renal disease, myocardial infarction) and all-cause mortality. FINDINGS:Logistic regressions revealed that SUD was significantly associated with greater risk of cerebrovascular accident [TUD odds ratio (OR) = 1.79, OUD-OR = 1.94, cocaine use disorder OR = 2.67], diabetic neuropathy [TUD-adjusted OR (aOR) = 1.47, cocaine use disorder-aOR = 1.35, AUD-aOR = 1.27], diabetic renal disease (TUD-aOR = 1.25, OUD-OR = 1.34), myocardial infarction (TUD-OR = 1.96, OUD-OR = 2.01, cocaine use disorder-OR = 2.68, CUD-OR = 2.48, AUD-OR = 1.42) and mortality (TUD-OR = 1.15, cocaine use disorder-OR = 1.61, CUD-OR = 1.49, AUD-OR = 1.35). CONCLUSIONS:Among patients in Ohio USA with both type 2 diabetes mellitus (T2DM) and hypertension, those with substance use disorders appear to have greater risk for T2DM complications and all-cause mortality.

Project description:PurposeHigh doses of gabapentin were associated with pancreatic acinar cell tumors in male Wistar rats, but there is little published epidemiological data regarding gabapentin and carcinogenicity. We explored the association between gabapentin and cancer in a US medical care program and followed up nominally significant associations in a UK primary care database.MethodsIn the US Kaiser Permanente Northern California (KPNC) health system, we performed nested case-control analyses of gabapentin and 55 cancer sites and all cancers combined using conditional logistic regression. Up to 10 controls were matched to each case on year of birth, sex, and year of cohort entry. No other covariates were included in models. Only dispensings for gabapentin 2 years or more before index date were considered. Nominally significant associations with an OR > 1.00 and p < 0.05 for three or more dispensings versus no dispensings were followed up by similar nested case-control analyses in the UK General Practice Research Database (GPRD), adjusting for potential indications for gabapentin and risk factors for the specific cancers.ResultsThe following analyses had OR > 1.00 and p < 0.05 for three or more dispensings of gabapentin versus no dispensing (2-year lag) in KPNC and were also examined in the GPRD: all cancers, breast, lung and bronchus, urinary bladder, kidney/renal pelvis, stomach, anus/anal canal/anorectum, penis, and other nervous system. These cancers were not statistically significantly associated with gabapentin in the GPRD case-control studies (2-year lag). The GPRD and KPNC studies did not identify a statistically significant increased risk of pancreatic cancer with more than two prescriptions of gabapentin in the 2-year lagged analyses.ConclusionsThe epidemiological data in a US cohort with up to 12 years of follow-up and a UK cohort with up to 15 years of follow-up do not support a carcinogenic effect of gabapentin use. However, the confidence intervals for some analyses were wide, and an important effect cannot be confidently excluded.

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Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

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Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

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