Citations

AbstractIntroductionElectronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping.MethodsWe selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.ResultsCQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms.ConclusionsCQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.

Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a “virtual proteomic” approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08–1.58; P =0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66–0.94; P =0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Abstract 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 &gt;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.

Background: Coronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown. Methods: We performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus. Results: The meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35–1.86; P =2.6×10 − 10 ). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14–2.57; P =0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17–2.24; P =0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18–4.75; P =0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes. Conclusions: Genetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.

Abstract Rationale Despite significant advances in knowledge of the genetic architecture of asthma, specific contributors to the variability in the burden between populations remain uncovered. Objectives To identify additional genetic susceptibility factors of asthma in European American and African American populations. Methods A phenotyping algorithm mining electronic medical records was developed and validated to recruit cases with asthma and control subjects from the Electronic Medical Records and Genomics network. Genome-wide association analyses were performed in pediatric and adult asthma cases and control subjects with European American and African American ancestry followed by metaanalysis. Nominally significant results were reanalyzed conditioning on allergy status. Measurements and Main Results The validation of the algorithm yielded an average of 95.8% positive predictive values for both cases and control subjects. The algorithm accrued 21,644 subjects (65.83% European American and 34.17% African American). We identified four novel population-specific associations with asthma after metaanalyses: loci 6p21.31, 9p21.2, and 10q21.3 in the European American population, and the PTGES gene in African Americans. TEK at 9p21.2, which encodes TIE2, has been shown to be involved in remodeling the airway wall in asthma, and the association remained significant after conditioning by allergy. PTGES, which encodes the prostaglandin E synthase, has also been linked to asthma, where deficient prostaglandin E2 synthesis has been associated with airway remodeling. Conclusions This study adds to understanding of the genetic architecture of asthma in European Americans and African Americans and reinforces the need to study populations of diverse ethnic backgrounds to identify shared and unique genetic predictors of asthma.

Background Intracranial aneurysms ( IAs ), abdominal aortic aneurysms ( AAAs ), and thoracic aortic aneurysms ( TAAs ) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs , AAAs, and TAAs . Methods and Results We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study ( GWAS ) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA , AAA , and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA ‐, AAA ‐, and TAA ‐associated SNP s and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls ( IA ) and 4391 cases and 37 904 controls ( AAA ), and found nominally significant associations for IA risk locus 18q11 near RBBP 8 to AAA (odds ratio [ OR ]=1.11; P =4.1×10 −5 ) and for TAA risk locus 15q21 near FBN 1 to AAA ( OR =1.07; P =1.1×10 −3 ). Conclusions Although there was no evidence for polygenic overlap between IAs , AAAs , and TAAs , we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs . These two loci will require further replication.

Abstract Objective Health 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. Results As 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%). Discussion These 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. Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.

AbstractBackground 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.

Abstract 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.

Clinical data captured in electronic medical records accurately identify cases and controls for genome-wide association studies.