Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm.

OBJECTIVE: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. MATERIALS AND METHODS: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. RESULTS: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. DISCUSSION: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. CONCLUSION: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

The impact of stance during heel raises on the hybrid ultimate lifting kit (HULK) device: A future microgravity exercise machine.

Extended missions in microgravity, such as those on the International Space Station (ISS) or future missions to Mars, can result in the physiological deconditioning of astronauts. Current mitigation strategies include a regimented diet in addition to resistance training paired with aerobic exercise. With the increased effort toward long duration space missions, there is room to optimize the cost, required time of use, and mass of exercise equipment. This research effort focuses on understanding the biomechanics of Heel Raise (HR) exercises while using the Hybrid Ultimate Lifting Kit (HULK) device, an exercise device designed to optimize volume and functionality. Using the biomechanics tool OpenSim, the effect of HR foot stance (15° inward, 15° outward, and straight) was assessed by analyzing kinematic and kinetic data. In particular, we analyzed peak joint angles, range of motion, joint moments, and angular impulses of a single subject. Preliminary results indicated no significant differences in terms of ankle/metatarsophalangeal/subtalar joint angles, range of motion, joint moments, and angular impulses between foot stances. In addition, loaded HR exercises were compared to body weight HR exercises without the HULK device. Finally, recommendations are made towards an optimal HR routine for long-duration space missions. The impact to health and rehabilitation on Earth is also discussed.

A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments.

Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.

Screening Colonoscopy Withdrawal Time Threshold for Adequate Proximal Serrated Polyp Detection Rate.

INTRODUCTION: For adequate adenoma detection rate (ADR), guidelines recommend a mean withdrawal time (MWT) of ≥ 6 min. ADR has been shown to correlate strongly with proximal serrated polyp detection rate (PSP-DR), which is another suggested quality measure for screening colonoscopy. However, the impact of directly measured withdrawal time on PSP-DR has not been rigorously studied. We examined the relationship between MWT to ADR and PSP-DR, with the aim of identifying a functional threshold withdrawal time associated with both increased ADR and PSP-DR. METHODS: This was a retrospective study of endoscopy and pathology data from average-risk screening colonoscopy examinations performed at a large system with six endoscopy laboratories. A natural language processing tool was used to determine polyp location and histology. ADR and PSP-DR were calculated for each endoscopist. MWT was calculated from colonoscopy examinations in which no polyps were resected. RESULTS: In total, 31,558 colonoscopy examinations were performed, of which 10,196 were average-risk screening colonoscopy examinations with cecal intubation and adequate prep by 24 gastroenterologists. When assessing the statistical significance of increasing MWT by minute, the first significant time mark for PSP-DR was at 11 min at a rate of 14.2% (p = 0.01). There was a significant difference comparing aggregated MWT < 11 min compared to ≥ 11 min looking at the rates of adenomas [OR 1.65 (1.09-2.51)] and proximal serrated polyps [OR 1.81 (1.06-3.08)]. While ADR linearly correlated well with MWT (R = 0.76, p < 0.001), the linear relationship with PSP-DR was less robust (R = 0.42, p = 0.043). CONCLUSION: In this large cohort of average-risk screening colonoscopy, a MWT of 11 min resulted in a statistically significant increase in both ADR and PSP-DR. Our data suggest that a longer withdrawal time may be required to meet both quality metrics.

A Framework for Leveraging "Big Data" to Advance Epidemiology and Improve Quality: Design of the VA Colonoscopy Collaborative.

OBJECTIVE: To describe a framework for leveraging big data for research and quality improvement purposes and demonstrate implementation of the framework for design of the Department of Veterans Affairs (VA) Colonoscopy Collaborative. METHODS: We propose that research utilizing large-scale electronic health records (EHRs) can be approached in a 4 step framework: 1) Identify data sources required to answer research question; 2) Determine whether variables are available as structured or free-text data; 3) Utilize a rigorous approach to refine variables and assess data quality; 4) Create the analytic dataset and perform analyses. We describe implementation of the framework as part of the VA Colonoscopy Collaborative, which aims to leverage big data to 1) prospectively measure and report colonoscopy quality and 2) develop and validate a risk prediction model for colorectal cancer (CRC) and high-risk polyps. RESULTS: Examples of implementation of the 4 step framework are provided. To date, we have identified 2,337,171 Veterans who have undergone colonoscopy between 1999 and 2014. Median age was 62 years, and 4.6 percent (n = 106,860) were female. We estimated that 2.6 percent (n = 60,517) had CRC diagnosed at baseline. An additional 1 percent (n = 24,483) had a new ICD-9 code-based diagnosis of CRC on follow up. CONCLUSION: We hope our framework may contribute to the dialogue on best practices to ensure high quality epidemiologic and quality improvement work. As a result of implementation of the framework, the VA Colonoscopy Collaborative holds great promise for 1) quantifying and providing novel understandings of colonoscopy outcomes, and 2) building a robust approach for nationwide VA colonoscopy quality reporting.

Regional Variability of Repeat Esophagogastroduodenoscopy Use in the National Veteran Population.

BACKGROUND: Esophagogastroduodenoscopy (EGD) procedures are performed frequently to evaluate gastrointestinal disease and symptoms. AIM: To determine regional practice variability of repeat EGDs in a national population. METHODS: The study sample included US Veterans with an outpatient index EGD from 1/1/2008 to 12/2010. We determined risk of repeat endoscopy from 1/2008 to 10/1/2014. A logistic regression model was used to assess the association between the odds of repeated EGD and patient demographics, ICD diagnostic codes, and geographic region. Multivariable logistic regression was performed to obtain the adjusted odds ratio and predicted probabilities of repeat EGDs by region. RESULTS: A total of 202,086 patients had an index endoscopy from 1/2008 to 12/2010. Unique patients with an index endoscopy were predominantly male (93.2%), white (72.8%), and on average 61 years. A total of 58,469 patients (28.9%) had one or more repeat EGDs, accounting for 103,253 repeat procedures through 10/2014. ICD-9-CM codes associated with increased risk of repeat procedures were Barrett's esophagus (OR 3.6, 95% CI 3.5-3.7), dysphagia (OR 1.3, 95% CI 1.2-1.3), ulcer (OR 1.3, 95% CI 2.2-2.4), stricture (OR 1.8, 95% CI 1.7-1.9), and esophageal varices (OR 2.8, 95% CI 2.7-3.0). There was a significant difference in the probability of repeat EGD by VA region, with the Midwest region having the highest probability (31.2%) and Southeast the lowest probability (27.3%). This difference would account for 400 more EGD procedures per 10,000 Veterans, after adjusting for patient demographics and diagnosis codes. CONCLUSIONS: Regional practice variability accounts for a substantial volume of repeat EGD procedures, regardless of patient characteristics and associated diagnoses.

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

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.

A Decompositional Approach to Executing Quality Data Model Algorithms on the i2b2 Platform.

The Quality Data Model (QDM) is an established standard for representing electronic clinical quality measures on electronic health record (EHR) repositories. The Informatics for Integrated Biology and the Bedside (i2b2) is a widely used platform for implementing clinical data repositories. However, translation from QDM to i2b2 is challenging, since QDM allows for complex queries beyond the capability of single i2b2 messages. We have developed an approach to decompose complex QDM algorithms into workflows of single i2b2 messages, and execute them on the KNIME data analytics platform. Each workflow operation module is composed of parameter lists, a template for the i2b2 message, an mechanism to create parameter updates, and a web service call to i2b2. The communication between workflow modules relies on passing keys ofi2b2 result sets. As a demonstration of validity, we describe the implementation and execution of a type 2 diabetes mellitus phenotype algorithm against an i2b2 data repository.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

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

BACKGROUND AND AIMS: Endoscopic resection (ER) is a safe and effective treatment for nonmalignant complex colorectal polyps (complex polyps). Surgical resection (SR) remains prevalent despite limited outcomes data. We aimed to evaluate SR outcomes for complex polyps and compare SR outcomes to those of ER. METHODS: We 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. RESULTS: Over 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 $14,528. When an AE occurred, costs ($25,557 vs $14,029; P < .0001) and LOS (11 vs 5 days; P < .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%; P = .09). ER costs (including rescue SR, when required) were lower than those of primary SR ($2152 vs $15,264; P < .0001). CONCLUSIONS: AEs occur in approximately one-sixth of patients after SR for complex polyps. ER-accounting for rescue SR caused by malignancy, AEs, or incomplete resection-is associated with markedly lower costs than SR. These data should be used when counseling patients about treatment options for complex polyps.

Stereoscopic Integrated Imaging Goggles for Multimodal Intraoperative Image Guidance.

We have developed novel stereoscopic wearable multimodal intraoperative imaging and display systems entitled Integrated Imaging Goggles for guiding surgeries. The prototype systems offer real time stereoscopic fluorescence imaging and color reflectance imaging capacity, along with in vivo handheld microscopy and ultrasound imaging. With the Integrated Imaging Goggle, both wide-field fluorescence imaging and in vivo microscopy are provided. The real time ultrasound images can also be presented in the goggle display. Furthermore, real time goggle-to-goggle stereoscopic video sharing is demonstrated, which can greatly facilitate telemedicine. In this paper, the prototype systems are described, characterized and tested in surgeries in biological tissues ex vivo. We have found that the system can detect fluorescent targets with as low as 60 nM indocyanine green and can resolve structures down to 0.25 mm with large FOV stereoscopic imaging. The system has successfully guided simulated cancer surgeries in chicken. The Integrated Imaging Goggle is novel in 4 aspects: it is (a) the first wearable stereoscopic wide-field intraoperative fluorescence imaging and display system, (b) the first wearable system offering both large FOV and microscopic imaging simultaneously,

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.

A Standards-based Semantic Metadata Repository to Support EHR-driven Phenotype Authoring and Execution.

This study describes our efforts in developing a standards-based semantic metadata repository for supporting electronic health record (EHR)-driven phenotype authoring and execution. Our system comprises three layers: 1) a semantic data element repository layer; 2) a semantic services layer; and 3) a phenotype application layer. In a prototype implementation, we developed the repository and services through integrating the data elements from both Quality Data Model (QDM) and HL7 Fast Healthcare Inteoroperability Resources (FHIR) models. We discuss the modeling challenges and the potential of our system to support EHR phenotype authoring and execution applications.

A Prototype for Executable and Portable Electronic Clinical Quality Measures Using the KNIME Analytics Platform.

Electronic clinical quality measures (eCQMs) based on the Quality Data Model (QDM) cannot currently be executed against non-standardized electronic health record (EHR) data. To address this gap, we prototyped an implementation of a QDM-based eCQM using KNIME, an open-source platform comprising a wide array of computational workflow tools that are collectively capable of executing QDM-based logic, while also giving users the flexibility to customize mappings from site-specific EHR data. To prototype this capability, we implemented eCQM CMS30 (titled: Statin Prescribed at Discharge) using KNIME. The implementation contains value set modules with connections to the National Library of Medicine's Value Set Authority Center, QDM Data Elements that can query a local EHR database, and logical and temporal operators. We successfully executed the KNIME implementation of CMS30 using data from the Vanderbilt University and Northwestern University EHR systems.

A Modular Architecture for Electronic Health Record-Driven Phenotyping.

Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.

Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research.

OBJECTIVE: To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development. MATERIALS AND METHODS: Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. RESULTS: Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. DISCUSSION: Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. CONCLUSIONS: Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.

ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.

BACKGROUND AND OBJECTIVE: We designed an algorithm to identify abdominal aortic aneurysm cases and controls from electronic health records to be shared and executed within the "electronic Medical Records and Genomics" (eMERGE) Network. MATERIALS AND METHODS: Structured Query Language, was used to script the algorithm utilizing "Current Procedural Terminology" and "International Classification of Diseases" codes, with demographic and encounter data to classify individuals as case, control, or excluded. The algorithm was validated using blinded manual chart review at three eMERGE Network sites and one non-eMERGE Network site. Validation comprised evaluation of an equal number of predicted cases and controls selected at random from the algorithm predictions. After validation at the three eMERGE Network sites, the remaining eMERGE Network sites performed verification only. Finally, the algorithm was implemented as a workflow in the Konstanz Information Miner, which represented the logic graphically while retaining intermediate data for inspection at each node. The algorithm was configured to be independent of specific access to data and was exportable (without data) to other sites. RESULTS: The algorithm demonstrated positive predictive values (PPV) of 92.8% (CI: 86.8-96.7) and 100% (CI: 97.0-100) for cases and controls, respectively. It performed well also outside the eMERGE Network. Implementation of the transportable executable algorithm as a Konstanz Information Miner workflow required much less effort than implementation from pseudo code, and ensured that the logic was as intended. DISCUSSION AND CONCLUSION: This ePhenotyping algorithm identifies abdominal aortic aneurysm cases and controls from the electronic health record with high case and control PPV necessary for research purposes, can be disseminated easily, and applied to high-throughput genetic and other studies.

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

OBJECTIVES: The objectives of this study were to use an open-source natural language-processing tool (NLP) to accurately assess total, anatomic (left and right colon), and advanced adenoma detection rates (ADRs) and to determine how these metrics differ between high- and low-performing endoscopists. METHODS: An NLP tool was developed using the Apache Unstructured Information Management Architecture and queried all procedure records for screening colonoscopies performed in patients aged 50-75 years at a single institution from April 1998 to December 2013. Validation was performed on 200 procedures and associated pathology reports. The total, left colon, right colon, and advanced ADRs were calculated and physicians were stratified by total ADR (<20% and ≥20%). Comparisons of colonoscopy characteristics and ADR comparisons (advanced, left, right, and right/left ratio) were determined by t-tests and Wilcoxon rank-sum tests. RESULTS: The total ADR for 34,998 screening colonoscopies from 1998 to 2013 was 20.3%, as determined via NLP. The institutional left and right colon ADRs were 10.1% and 12.5%, respectively. The overall advanced ADR was 4.4%. Endoscopists with total ADRs ≥20% had higher left (12.4%) and right colon (16.4%) ADRs than endoscopists with ADRs <20% (left ADR=5.6%, right ADR=5.8%). Endoscopists with ADRs ≥20% had higher individual right/left ADR ratios than those with low ADRs (1.4 (interquartile range (IQR) 0.4) vs. 1.0 (IQR 0.4), P=0.02). There was a moderate positive correlation between advanced ADR detection and both right (Spearman's rho=0.5, P=0.05) and left colon (Spearman's rho=0.4, P=0.03) ADRs. CONCLUSIONS: Institutions should consider the use of anatomic and advanced ADRs determined via natural language processing as a refined measure of colonoscopy quality. The ability to continuously monitor and provide feedback on colonoscopy quality metrics may encourage endoscopists to refine technique, resulting in overall improvements in adenoma detection.

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

BACKGROUND: Research supports medical record review using screening triggers as the optimal method to detect hospital adverse events (AE), yet the method is labour-intensive. METHOD: This study compared a traditional trigger tool with an enterprise data warehouse (EDW) based screening method to detect AEs. We created 51 automated queries based on 33 traditional triggers from prior research, and then applied them to 250 randomly selected medical patients hospitalised between 1 September 2009 and 31 August 2010. Two physicians each abstracted records from half the patients using a traditional trigger tool and then performed targeted abstractions for patients with positive EDW queries in the complementary half of the sample. A third physician confirmed presence of AEs and assessed preventability and severity. RESULTS: Traditional trigger tool and EDW based screening identified 54 (22%) and 53 (21%) patients with one or more AE. Overall, 140 (56%) patients had one or more positive EDW screens (total 366 positive screens). Of the 137 AEs detected by at least one method, 86 (63%) were detected by a traditional trigger tool, 97 (71%) by EDW based screening and 46 (34%) by both methods. Of the 11 total preventable AEs, 6 (55%) were detected by traditional trigger tool, 7 (64%) by EDW based screening and 2 (18%) by both methods. Of the 43 total serious AEs, 28 (65%) were detected by traditional trigger tool, 29 (67%) by EDW based screening and 14 (33%) by both. CONCLUSIONS: We found relatively poor agreement between traditional trigger tool and EDW based screening with only approximately a third of all AEs detected by both methods. A combination of complementary methods is the optimal approach to detecting AEs among hospitalised patients.

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.