Qi Wei

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

OBJECTIVEnGenome-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.nnnMATERIALS AND METHODSnAn 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.nnnRESULTSnThe 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.nnnDISCUSSIONnBy 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.nnnCONCLUSIONSnAn algorithm using commonly available data from five different EMR can accurately identify T2D cases and controls for genetic study across multiple institutions.

Development and evaluation of an ensemble resource linking medications to their indications

Objective To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs). Materials and methods We processed four public medication resources, RxNorm, Side Effect Resource (SIDER) 2, MedlinePlus, and Wikipedia, to create MEDI. We applied natural language processing and ontology relationships to extract indications for prescribable, single-ingredient medication concepts and all ingredient concepts as defined by RxNorm. Indications were coded as Unified Medical Language System (UMLS) concepts and International Classification of Diseases, 9th edition (ICD9) codes. A total of 689 extracted indications were randomly selected for manual review for accuracy using dual-physician review. We identified a subset of medication–indication pairs that optimizes recall while maintaining high precision. Results MEDI contains 3112 medications and 63u2005343 medication–indication pairs. Wikipedia was the largest resource, with 2608 medications and 34u2005911 pairs. For each resource, estimated precision and recall, respectively, were 94% and 20% for RxNorm, 75% and 33% for MedlinePlus, 67% and 31% for SIDER 2, and 56% and 51% for Wikipedia. The MEDI high-precision subset (MEDI-HPS) includes indications found within either RxNorm or at least two of the three other resources. MEDI-HPS contains 13u2005304 unique indication pairs regarding 2136 medications. The mean±SD number of indications for each medication in MEDI-HPS is 6.22±6.09. The estimated precision of MEDI-HPS is 92%. Conclusions MEDI is a publicly available, computable resource that links medications with their indications as represented by concepts and billing codes. MEDI may benefit clinical EMR applications and reuse of EMR data for research.

Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus

OBJECTIVEnTo evaluate data fragmentation across healthcare centers with regard to the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm developed to differentiate (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes.nnnMATERIALS AND METHODSnThis population-based study identified all Olmsted County, Minnesota residents in 2007. We used provider-linked electronic medical record data from the two healthcare centers that provide >95% of all care to County residents (ie, Olmsted Medical Center and Mayo Clinic in Rochester, Minnesota, USA). Subjects were limited to residents with one or more encounter January 1, 2006 through December 31, 2007 at both healthcare centers. DM-relevant data on diagnoses, laboratory results, and medication from both centers were obtained during this period. The algorithm was first executed using data from both centers (ie, the gold standard) and then from Mayo Clinic alone. Positive predictive values and false-negative rates were calculated, and the McNemar test was used to compare categorization when data from the Mayo Clinic alone were used with the gold standard. Age and sex were compared between true-positive and false-negative subjects with T2DM. Statistical significance was accepted as p<0.05.nnnRESULTSnWith data from both medical centers, 765 subjects with T2DM (4256 non-DM subjects) were identified. When single-center data were used, 252 T2DM subjects (1573 non-DM subjects) were missed; an additional false-positive 27 T2DM subjects (215 non-DM subjects) were identified. The positive predictive values and false-negative rates were 95.0% (513/540) and 32.9% (252/765), respectively, for T2DM subjects and 92.6% (2683/2898) and 37.0% (1573/4256), respectively, for non-DM subjects. Age and sex distribution differed between true-positive (mean age 62.1; 45% female) and false-negative (mean age 65.0; 56.0% female) T2DM subjects.nnnCONCLUSIONnThe findings show that application of an HTCP algorithm using data from a single medical center contributes to misclassification. These findings should be considered carefully by researchers when developing and executing HTCP algorithms.

Time-oriented question answering from clinical narratives sing semantic-web techniques

The ability to answer temporal-oriented questions based on clinical narratives is essential to clinical research. The temporal dimension in medical data analysis enables clinical researches on many areas, such as, disease progress, individualized treatment, and decision support. The Semantic Web provides a suitable environment to represent the temporal dimension of the clinical data and reason about them. In this paper, we introduce a Semantic-Web based framework, which provides an API for querying temporal information from clinical narratives. The framework is centered by an OWL ontology called CNTRO (Clinical Narrative Temporal Relation Ontology), and contains three major components: time normalizer, SWRL based reasoner, and OWL-DL based reasoner. We also discuss how we adopted these three components in the clinical domain, their limitations, as well as extensions that we found necessary or desirable to archive the purposes of querying time-oriented data from real-world clinical narratives.

The Absence of Longitudinal Data Limits the Accuracy of High-Throughput Clinical Phenotyping for Identifying Type 2 Diabetes Mellitus Subjects

PURPOSEnTo evaluate the impact of insufficient longitudinal data on the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm for identifying (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes.nnnMETHODSnRetrospective study conducted at Mayo Clinic in Rochester, Minnesota. Eligible subjects were Olmsted County residents with ≥1 Mayo Clinic encounter in each of three time periods: (1) 2007, (2) from 1997 through 2006, and (3) before 1997 (N = 54,283). Diabetes relevant electronic medical record (EMR) data about diagnoses, laboratories, and medications were used. We employed the HTCP algorithm to categorize individuals as T2DM cases and non-diabetes controls. Considering the full 11 years (1997-2007) as the gold standard, we compared gold-standard categorizations with those using data for 10 subsequent intervals, ranging from 1998-2007 (10-year data) to 2007 (1-year data). Positive predictive values (PPVs) and false-negative rates (FNRs) were calculated. McNemar tests were used to determine whether categorizations using shorter time periods differed from the gold standard. Statistical significance was defined as P < 0.05.nnnRESULTSnWe identified 2770 T2DM cases and 21,005 controls when the algorithm was applied using 11-year data. Using 2007 data alone, PPVs and FNRs, respectively, were 70% and 25% for case identification and 59% and 67% for control identification. All time frames differed significantly from the gold standard, except for the 10-year period.nnnCONCLUSIONSnThe accuracy of the algorithm reduced remarkably as data were limited to shorter observation periods. This impact should be considered carefully when designing/executing HTCP algorithms.

Platelet Inhibitors Reduce Rupture in a Mouse Model of Established Abdominal Aortic Aneurysm

Objective—Rupture of abdominal aortic aneurysms causes a high morbidity and mortality in the elderly population. Platelet-rich thrombi form on the surface of aneurysms and may contribute to disease progression. In this study, we used a pharmacological approach to examine a role of platelets in established aneurysms induced by angiotensin II infusion into hypercholesterolemic mice. Approach and Results—Administration of the platelet inhibitors aspirin or clopidogrel bisulfate to established abdominal aortic aneurysms dramatically reduced rupture. These platelet inhibitors reduced abdominal aortic platelet and macrophage recruitment resulting in decreased active matrix metalloproteinase-2 and matrix metalloproteinase-9. Platelet inhibitors also resulted in reduced plasma concentrations of platelet factor 4, cytokines, and components of the plasminogen activation system in mice. To determine the validity of these findings in human subjects, a cohort of aneurysm patients were retrospectively analyzed using developed and validated algorithms in the electronic medical record database at Vanderbilt University. Similar to mice, administration of aspirin or P2Y12 inhibitors was associated with reduced death among patients with abdominal aortic aneurysm. Conclusions—These results suggest that platelets contribute to abdominal aortic aneurysm progression and rupture.

Terminology representation guidelines for biomedical ontologies in the semantic web notations

Terminologies and ontologies are increasingly prevalent in healthcare and biomedicine. However they suffer from inconsistent renderings, distribution formats, and syntax that make applications through common terminologies services challenging. To address the problem, one could posit a shared representation syntax, associated schema, and tags. We identified a set of commonly-used elements in biomedical ontologies and terminologies based on our experience with the Common Terminology Services 2 (CTS2) Specification as well as the Lexical Grid (LexGrid) project. We propose guidelines for precisely such a shared terminology model, and recommend tags assembled from SKOS, OWL, Dublin Core, RDF Schema, and DCMI meta-terms. We divide these guidelines into lexical information (e.g. synonyms, and definitions) and semantic information (e.g. hierarchies). The latter we distinguish for use by informal terminologies vs. formal ontologies. We then evaluate the guidelines with a spectrum of widely used terminologies and ontologies to examine how the lexical guidelines are implemented, and whether our proposed guidelines would enhance interoperability.