William K. Thompson

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.

Portability of an algorithm to identify rheumatoid arthritis in electronic health records.

OBJECTIVESnElectronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems.nnnMATERIALS AND METHODSnPhysicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models.nnnRESULTSnApplying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds.nnnDISCUSSIONnThese results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site.nnnCONCLUSIONnElectronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability

OBJECTIVEnHealth care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites.nnnRESULTSnAs of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%).nnnDISCUSSIONnThese results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others.nnnCONCLUSIONnBy providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.

High Density GWAS for LDL Cholesterol in African Americans Using Electronic Medical Records Reveals a Strong Protective Variant in APOE

Only one low‐density lipoprotein cholesterol (LDL‐C) genome‐wide association study (GWAS) has been previously reported in ‐African Americans. We performed a GWAS of LDL‐C in African Americans using data extracted from electronic medical records (EMR) in the eMERGE network. African Americans were genotyped on the Illumina 1M chip. All LDL‐C measurements, prescriptions, and diagnoses of concomitant disease were extracted from EMR. We created two analytic datasets; one dataset having median LDL‐C calculated after the exclusion of some lab values based on comorbidities and medication (n= 618) and another dataset having median LDL‐C calculated without any exclusions (n= 1,249). SNP rs7412 in APOE was strongly associated with LDL‐C in both datasets (p < 5 × 10−8). In the dataset with exclusions, a decrease of 20.0 mg/dL per minor allele was observed. The effect size was attenuated (12.3 mg/dL) in the dataset without any lab values excluded. Although other signals in APOE have been detected in previous GWAS, this large and important SNP association has not been well detected in large GWAS because rs7412 was not included on many genotyping arrays. Use of median LDL‐C extracted from EMR after exclusions for medications and comorbidities increased the percentage of trait variance explained by genetic variation. Clin Trans Sci 2012; Volume 5: 394–399

Desiderata for computable representations of electronic health records-driven phenotype algorithms.

Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Adverse events after surgery for nonmalignant colon polyps are common and associated with increased length of stay and costs.

BACKGROUND AND AIMSnEndoscopic resection (ER) is a safe and effective treatment for nonmalignant complex colorectal polyps (complex polyps). Surgical resection (SR) remains prevalent despite limited outcomes data. Wexa0aimed to evaluate SR outcomes for complex polyps and compare SR outcomes to those of ER.nnnMETHODSnWe performed a single-center, retrospective, cohort study of all patients undergoing SR (2003-2013) and ER (2011-2013) for complex polyps. We excluded patients with invasive carcinoma from the SR cohort. Primary outcomes were 12-month adverse event (AE) rate, length of stay (LOS), and costs. SR outcomes over a 3-year period (2011-2013) were compared with the overlapping ER cohort.nnnRESULTSnOver the 11-year period, 359 patients (mean [± SD] age 64 ± 11 years) underwent SR (58% laparoscopic) for complex polyps. In total, 17% experienced an AE, and 3% required additional surgery; 12-month mortality was 1%. Including readmissions, median LOS was 5 days (IQR 4-7 days), and costs were

Anatomic and advanced adenoma detection rates as quality metrics determined via natural language processing.

14,528. When an AE occurred, costs (

Design patterns for the development of electronic health record-driven phenotype extraction algorithms

25,557 vs

Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events

14,029; Pxa0< .0001) and LOS (11 vs 5 days; Pxa0< .0001) significantly increased. From 2011 to 2013, 198 patients were referred for ER, and 73 underwent primary SR (70% laparoscopic). There was a lower AE rate for ER versus primary SR (10% vs 18%; Pxa0= .09). ER costs (including rescue SR, when required) were lower than those of primary SR (

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review

2152 vs