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Yan C, Ong HH, Grabowska ME, Krantz MS, Su WC, Dickson AL, Peterson JF, Feng Q, Roden DM, Stein CM, Kerchberger VE, Malin BA, Wei WQ. Large language models facilitate the generation of electronic health record phenotyping algorithms. J Am Med Inform Assoc 2024; 31:1994-2001. [PMID: 38613820 PMCID: PMC11339509 DOI: 10.1093/jamia/ocae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/21/2024] [Accepted: 03/22/2024] [Indexed: 04/15/2024] Open
Abstract
OBJECTIVES Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Henry H Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Monika E Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Matthew S Krantz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Wu-Chen Su
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Alyson L Dickson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - V Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37203, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37203, United States
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Carrell DS, Floyd JS, Gruber S, Hazlehurst BL, Heagerty PJ, Nelson JC, Williamson BD, Ball R. A general framework for developing computable clinical phenotype algorithms. J Am Med Inform Assoc 2024; 31:1785-1796. [PMID: 38748991 PMCID: PMC11258420 DOI: 10.1093/jamia/ocae121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 07/20/2024] Open
Abstract
OBJECTIVE To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data. MATERIALS AND METHODS Drawing on extensive prior phenotyping experiences and insights derived from 3 algorithm development projects conducted specifically for this purpose, our team with expertise in clinical medicine, statistics, informatics, pharmacoepidemiology, and healthcare data science methods conceptualized stages of development and corresponding sets of principles, strategies, and practical guidelines for improving the algorithm development process. RESULTS We propose 5 stages of algorithm development and corresponding principles, strategies, and guidelines: (1) assessing fitness-for-purpose, (2) creating gold standard data, (3) feature engineering, (4) model development, and (5) model evaluation. DISCUSSION AND CONCLUSION This framework is intended to provide practical guidance and serve as a basis for future elaboration and extension.
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Affiliation(s)
- David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States
| | - James S Floyd
- Department of Medicine, School of Medicine, University of Washington, Seattle, WA 98195, United States
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA 98195, United States
| | - Susan Gruber
- Putnam Data Sciences, LLC, Cambridge, MA 02139, United States
| | - Brian L Hazlehurst
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR 97227, United States
| | - Patrick J Heagerty
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA 98195, United States
| | - Jennifer C Nelson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States
| | - Brian D Williamson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States
| | - Robert Ball
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD 20993, United States
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Chen JS, Copado IA, Vallejos C, Kalaw FGP, Soe P, Cai CX, Toy BC, Borkar D, Sun CQ, Shantha JG, Baxter SL. Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis. OPHTHALMOLOGY SCIENCE 2024; 4:100468. [PMID: 38560278 PMCID: PMC10973665 DOI: 10.1016/j.xops.2024.100468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/04/2024] [Accepted: 01/11/2024] [Indexed: 04/04/2024]
Abstract
Purpose Use of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR. Design Literature review and quantitative analysis. Subjects Published manuscripts. Methods Four graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions. Main Outcome Measures Number of studies included and numeric counts of billing codes used to define codified cohorts. Results In total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts. Conclusions Substantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Jimmy S Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Ivan A Copado
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Cecilia Vallejos
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Fritz Gerald P Kalaw
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Priyanka Soe
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Cindy X Cai
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Brian C Toy
- Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Durga Borkar
- Department of Ophthalmology, Duke Eye Center, Duke University, Durham, North Carolina
| | - Catherine Q Sun
- F.I. Proctor Foundation, University of California San Francisco, San Francisco, California
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Jessica G Shantha
- F.I. Proctor Foundation, University of California San Francisco, San Francisco, California
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
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Jafari E, Blackman MH, Karnes JH, Van Driest SL, Crawford DC, Choi L, McDonough CW. Using electronic health records for clinical pharmacology research: Challenges and considerations. Clin Transl Sci 2024; 17:e13871. [PMID: 38943244 PMCID: PMC11213823 DOI: 10.1111/cts.13871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 07/01/2024] Open
Abstract
Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR-based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug-related phenotypes from the EHR. There is great potential for EHR-based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug-related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug-related data from EHRs. We provide an examination of EHR-based computable phenotypes and discuss cutting-edge approaches to map medication information for clinical pharmacology research, including medication-based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR-based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions.
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Affiliation(s)
- Eissa Jafari
- Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics and Precision Medicine, College of PharmacyUniversity of FloridaGainesvilleFloridaUSA
- Department of Pharmacy Practice, College of PharmacyJazan UniversityJazanSaudi Arabia
| | - Marisa H. Blackman
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and ScienceUniversity of Arizona R. Ken Coit College of PharmacyTucsonArizonaUSA
| | - Sara L. Van Driest
- Department of PediatricsVanderbilt University Medical Center (VUMC)NashvilleTennesseeUSA
- Present address:
All of US Research Program, National Institutes of HealthBethesdaMarylandUSA
| | - Dana C. Crawford
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational BiologyCase Western Reserve UniversityClevelandOhioUSA
- Department of Genetics and Genome Sciences, Cleveland Institute for Computational BiologyCase Western Reserve UniversityClevelandOhioUSA
| | - Leena Choi
- Department of Biostatistics and Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics and Precision Medicine, College of PharmacyUniversity of FloridaGainesvilleFloridaUSA
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Miller M, Jorm L, Partyka C, Burns B, Habig K, Oh C, Immens S, Ballard N, Gallego B. Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia. Injury 2024; 55:111570. [PMID: 38664086 DOI: 10.1016/j.injury.2024.111570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/11/2024] [Accepted: 04/14/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.
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Affiliation(s)
- Matthew Miller
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Anesthesia, St George Hospital, Kogarah, NSW 2217 Australia; Centre for Big Data Research in Health at UNSW Sydney, Kensington, NSW 2052, Australia.
| | - Louisa Jorm
- Foundation Director of the Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia
| | - Chris Partyka
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, Royal North Shore Hospital, St Leonards, NSW 2065, Australia
| | - Brian Burns
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Royal North Shore Hospital, St Leonards, NSW 2065, Australia; Faculty of Medicine & Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Karel Habig
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia
| | - Carissa Oh
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, St George Hospital, Kogarah, NSW 2217 Australia
| | - Sam Immens
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia
| | - Neil Ballard
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Paediatric Emergency Medicine, Sydney Children's Hospital, Randwick, NSW 2031, Australia; Department of Emergency Medicine, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Blanca Gallego
- Clinical analytics and machine learning unit, Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia
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Rivera AS, Pierce JB, Sinha A, Pawlowski AE, Lloyd-Jones DM, Lee YC, Feinstein MJ, Petito LC. Designing target trials using electronic health records: A case study of second-line disease-modifying anti-rheumatic drugs and cardiovascular disease outcomes in patients with rheumatoid arthritis. PLoS One 2024; 19:e0305467. [PMID: 38875273 PMCID: PMC11178161 DOI: 10.1371/journal.pone.0305467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 05/30/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Emulation of the "target trial" (TT), a hypothetical pragmatic randomized controlled trial (RCT), using observational data can be used to mitigate issues commonly encountered in comparative effectiveness research (CER) when randomized trials are not logistically, ethically, or financially feasible. However, cardiovascular (CV) health research has been slow to adopt TT emulation. Here, we demonstrate the design and analysis of a TT emulation using electronic health records to study the comparative effectiveness of the addition of a disease-modifying anti-rheumatic drug (DMARD) to a regimen of methotrexate on CV events among rheumatoid arthritis (RA) patients. METHODS We used data from an electronic medical records-based cohort of RA patients from Northwestern Medicine to emulate the TT. Follow-up began 3 months after initial prescription of MTX (2000-2020) and included all available follow-up through June 30, 2020. Weighted pooled logistic regression was used to estimate differences in CVD risk and survival. Cloning was used to handle immortal time bias and weights to improve baseline and time-varying covariate imbalance. RESULTS We identified 659 eligible people with RA with average follow-up of 46 months and 31 MACE events. The month 24 adjusted risk difference for MACE comparing initiation vs non-initiation of a DMARD was -1.47% (95% confidence interval [CI]: -4.74, 1.95%), and the marginal hazard ratio (HR) was 0.72 (95% CI: 0.71, 1.23). In analyses subject to immortal time bias, the HR was 0.62 (95% CI: 0.29-1.44). CONCLUSION In this sample, we did not observe evidence of differences in risk of MACE, a finding that is compatible with previously published meta-analyses of RCTs. Thoughtful application of the TT framework provides opportunities to conduct CER in observational data. Benchmarking results of observational analyses to previously published RCTs can lend credibility to interpretation.
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Affiliation(s)
- Adovich S Rivera
- Institute for Public Health and Management, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
| | - Jacob B Pierce
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Arjun Sinha
- Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Anna E Pawlowski
- Northwestern Medicine Enterprise Data Warehouse, Northwestern University, Chicago, Illinois, United States of America
| | - Donald M Lloyd-Jones
- Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Preventive Medicine, Division of Epidemiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Yvonne C Lee
- Department of Medicine, Division of Rheumatology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Matthew J Feinstein
- Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Preventive Medicine, Division of Epidemiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Lucia C Petito
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
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Newby D, Taylor N, Joyce DW, Winchester LM. Optimising the use of electronic medical records for large scale research in psychiatry. Transl Psychiatry 2024; 14:232. [PMID: 38824136 PMCID: PMC11144247 DOI: 10.1038/s41398-024-02911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/03/2024] Open
Abstract
The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.
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Affiliation(s)
- Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dan W Joyce
- Department of Primary Care and Mental Health and Civic Health, Innovation Labs, Institute of Population Health, University of Liverpool, Liverpool, UK
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Jiang K, Cao T. Automated HIV Case Identification from the MIMIC-IV Database. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:555-564. [PMID: 38827090 PMCID: PMC11141847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Automatic HIV phenotyping is needed for HIV research based on electronic health records (EHRs). MIMIC-IV, an extension of MIMIC-III, contains more than 520,000 hospital admissions and has become a valuable EHR database for secondary medical research. However, there was no prior phenotyping algorithm to extract HIV cases from MIMIC-IV, which requires a comprehensive knowledge of the database. Moreover, previous HIV phenotyping algorithms did not consider the new HIV-1/HIV-2 antibody differentiation immunoassay tests that MIMIC-IV contains. Our work provided insight into the structure and data elements in MIMIC-IV and proposed a new HIV phenotyping algorithm to fill in these gaps. The results included MIMIC-IV's data tables and elements used, 1,781 and 1,843 HIV cases from MIMIC-IV's versions 0.4 and 2.1, respectively, and summary statistics of these two HIV case cohorts. They could be used for the development of statistical and machine learning models in future studies about the disease.
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Affiliation(s)
- Kai Jiang
- The University of Texas Health Science Center at Houston School of Public Health, Houston, Texas, United States
| | - Tru Cao
- The University of Texas Health Science Center at Houston School of Public Health, Houston, Texas, United States
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Singh A, Schooley B, Mobley J, Mobley P, Lindros S, Brooks JM, Floyd SB. Human-centered Design of a Health Recommender System for Orthopaedic Shoulder Treatment. RESEARCH SQUARE 2024:rs.3.rs-4359437. [PMID: 38826294 PMCID: PMC11142362 DOI: 10.21203/rs.3.rs-4359437/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Rich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be applied to a decision support system application to generate data summaries for similar patients during the clinical encounter to assist physicians and patients in making evidence-based shared treatment decisions. Objective A human-centered design (HCD) process was used to develop a HRS for treatment decision support in orthopaedic medicine, the Informatics Consult for Individualized Treatment (I-C-IT). We also evaluate the usability and utility of the system from the physician's perspective, focusing on elements of utility and shared decision-making in orthopaedic medicine. Methods The HCD process for I-C-IT included 6 steps across three phases of analysis, design, and evaluation. A team of informaticians and comparative effectiveness researchers directly engaged with orthopaedic surgeon subject matter experts in a collaborative I-C-IT prototype design process. Ten orthopaedic surgeons participated in a mixed methods evaluation of the I-C-IT prototype that was produced. Results The HCD process resulted in a prototype system, I-C-IT, with 14 data visualization elements and a set of design principles crucial for HRS for decision support. The overall standard system usability scale (SUS) score for the I-C-IT Webapp prototype was 88.75 indicating high usability. In addition, utility questions addressing shared decision-making found that 90% of orthopaedic surgeon respondents either strongly agreed or agreed that I-C-IT would help them make data informed decisions with their patients. Conclusion The HCD process produced an HRS prototype that is capable of supporting orthopaedic surgeons and patients in their information needs during clinical encounters. Future research should focus on refining I-C-IT by incorporating patient feedback in future iterative cycles of system design and evaluation.
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Affiliation(s)
| | | | - Jack Mobley
- University of South Carolina School of Medicine Greenville
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Ozrazgat-Baslanti T, Ren Y, Adiyeke E, Islam R, Hashemighouchani H, Ruppert M, Miao S, Loftus T, Johnson-Mann C, Madushani RWMA, Shenkman EA, Hogan W, Segal MS, Lipori G, Bihorac A, Hobson C. Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients. PLoS One 2024; 19:e0299332. [PMID: 38652731 PMCID: PMC11037544 DOI: 10.1371/journal.pone.0299332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 02/07/2024] [Indexed: 04/25/2024] Open
Abstract
Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non-race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%-100%) and 99% (95% CI 97%-100%) and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.
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Affiliation(s)
- Tezcan Ozrazgat-Baslanti
- University of Florida Intelligent Clinical Care Center (IC3), Gainesville, Florida, United States of America
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Yuanfang Ren
- University of Florida Intelligent Clinical Care Center (IC3), Gainesville, Florida, United States of America
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Esra Adiyeke
- University of Florida Intelligent Clinical Care Center (IC3), Gainesville, Florida, United States of America
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Rubab Islam
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Haleh Hashemighouchani
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Matthew Ruppert
- University of Florida Intelligent Clinical Care Center (IC3), Gainesville, Florida, United States of America
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Shunshun Miao
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Tyler Loftus
- University of Florida Intelligent Clinical Care Center (IC3), Gainesville, Florida, United States of America
- Department of Surgery, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Crystal Johnson-Mann
- Department of Surgery, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - R. W. M. A. Madushani
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Elizabeth A. Shenkman
- University of Florida Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, United States of America
| | - William Hogan
- University of Florida Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, United States of America
| | - Mark S. Segal
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Gloria Lipori
- University of Florida Health, Gainesville, Florida, United States of America
| | - Azra Bihorac
- University of Florida Intelligent Clinical Care Center (IC3), Gainesville, Florida, United States of America
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Charles Hobson
- Department of Health Services Research, Management and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
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11
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Wei WQ, Rowley R, Wood A, MacArthur J, Embi PJ, Denaxas S. Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions. J Am Med Inform Assoc 2024; 31:1036-1041. [PMID: 38269642 PMCID: PMC10990558 DOI: 10.1093/jamia/ocae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/12/2023] [Accepted: 01/08/2024] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. RECOMMENDATIONS Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.
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Affiliation(s)
- Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, MD 20892, United States
| | - Angela Wood
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 1TN, United Kingdom
| | - Jacqueline MacArthur
- British Heart Foundation Data Science Center, Health Data Research, London, NW1 2BE, United Kingdom
| | - Peter J Embi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Spiros Denaxas
- British Heart Foundation Data Science Center, Health Data Research, London, NW1 2BE, United Kingdom
- Institute of Health Informatics, University College London, London, WC1E 6BT, United Kingdom
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12
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Jeffery AD, Fabbri D, Reeves RM, Matheny ME. Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression. Heliyon 2024; 10:e26434. [PMID: 38444495 PMCID: PMC10912240 DOI: 10.1016/j.heliyon.2024.e26434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024] Open
Abstract
Objective Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence. Materials and methods Using de-identified electronic health records of 52,861 post-operative encounters, we applied a data programming paradigm (implemented in the Snorkel software) for the development of a machine learning classifier for opioid-induced respiratory depression. Our approach included subject matter experts creating 14 labeling functions that served as noisy labels for developing a probabilistic Generative model. We used probabilistic labels from the Generative model as outcome labels for training a Discriminative model on the source data. We evaluated performance of the Discriminative model with a hold-out test set of 599 independently-reviewed patient records. Results The final Discriminative classification model achieved an accuracy of 0.977, an F1 score of 0.417, a sensitivity of 1.0, and an AUC of 0.988 in the hold-out test set with a prevalence of 0.83% (5/599). Discussion All of the confirmed Cases were identified by the classifier. For rare outcomes, this finding is encouraging because it reduces the number of manual reviews needed by excluding visits/patients with low probabilities. Conclusion Application of a data programming paradigm with expert-informed labeling functions might have utility for phenotyping clinical phenomena that are not easily ascertainable from highly-structured data.
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Affiliation(s)
- Alvin D. Jeffery
- Vanderbilt University School of Nursing, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
| | - Daniel Fabbri
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ruth M. Reeves
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
| | - Michael E. Matheny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
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13
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Yan C, Ong HH, Grabowska ME, Krantz MS, Su WC, Dickson AL, Peterson JF, Feng Q, Roden DM, Stein CM, Kerchberger VE, Malin BA, Wei WQ. Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.19.23300230. [PMID: 38196578 PMCID: PMC10775330 DOI: 10.1101/2023.12.19.23300230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Objectives Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Henry H. Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Monika E. Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Matthew S. Krantz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Wu-Chen Su
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Alyson L. Dickson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan M. Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - C. Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - V. Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Bradley A. Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
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14
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Jeffery AD, Fabbri D, Reeves RM, Matheny ME. Use of Noisy Labels as Weak Learners to Identify Incompletely Ascertainable Outcomes: A Feasibility Study with Opioid-Induced Respiratory Depression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.29.24301963. [PMID: 38352435 PMCID: PMC10863026 DOI: 10.1101/2024.01.29.24301963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Objective Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence. Materials and Methods Using de-identified electronic health records of 52,861 post-operative encounters, we applied a data programming paradigm (implemented in the Snorkel software) for the development of a machine learning classifier for opioid-induced respiratory depression. Our approach included subject matter experts creating 14 labeling functions that served as noisy labels for developing a probabilistic Generative model. We used probabilistic labels from the Generative model as outcome labels for training a Discriminative model on the source data. We evaluated performance of the Discriminative model with a hold-out test set of 599 independently-reviewed patient records. Results The final Discriminative classification model achieved an accuracy of 0.977, an F1 score of 0.417, a sensitivity of 1.0, and an AUC of 0.988 in the hold-out test set with a prevalence of 0.83% (5/599). Discussion All of the confirmed Cases were identified by the classifier. For rare outcomes, this finding is encouraging because it reduces the number of manual reviews needed by excluding visits/patients with low probabilities. Conclusion Application of a data programming paradigm with expert-informed labeling functions might have utility for phenotyping clinical phenomena that are not easily ascertainable from highly-structured data.
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Affiliation(s)
- Alvin D Jeffery
- School of Nursing, Vanderbilt University, Department of Biomedical Informatics, Vanderbilt University Medical Center, Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
| | - Daniel Fabbri
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ruth M Reeves
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
| | - Michael E Matheny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
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15
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Poulsen MN, Freda PJ, Troiani V, Mowery DL. Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study. JMIR Ment Health 2024; 11:e53366. [PMID: 38224481 PMCID: PMC10825772 DOI: 10.2196/53366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 01/16/2024] Open
Abstract
BACKGROUND Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns. OBJECTIVE To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts. METHODS We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity. RESULTS The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients. CONCLUSIONS Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.
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Affiliation(s)
- Melissa N Poulsen
- Department of Population Health Sciences, Geisinger, Danville, PA, United States
| | - Philip J Freda
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, United States
| | - Vanessa Troiani
- Department of Autism and Developmental Medicine, Geisinger, Danville, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology and Informatics, Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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16
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Wen J, Hou J, Bonzel CL, Zhao Y, Castro VM, Gainer VS, Weisenfeld D, Cai T, Ho YL, Panickan VA, Costa L, Hong C, Gaziano JM, Liao KP, Lu J, Cho K, Cai T. LATTE: Label-efficient incident phenotyping from longitudinal electronic health records. PATTERNS (NEW YORK, N.Y.) 2024; 5:100906. [PMID: 38264714 PMCID: PMC10801250 DOI: 10.1016/j.patter.2023.100906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/06/2023] [Accepted: 12/01/2023] [Indexed: 01/25/2024]
Abstract
Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embeddings, LATTE selects predictive features and compresses their information into longitudinal visit embeddings through visit attention learning. LATTE models the sequential dependency between the target event and visit embeddings to derive the timings. To improve label efficiency, LATTE constructs longitudinal silver-standard labels from unlabeled patients to perform semi-supervised training. LATTE is evaluated on the onset of type 2 diabetes, heart failure, and relapses of multiple sclerosis. LATTE consistently achieves substantial improvements over benchmark methods while providing high prediction interpretability. The event timings are shown to help discover risk factors of heart failure among patients with rheumatoid arthritis.
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Affiliation(s)
- Jun Wen
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Jue Hou
- University of Minnesota, Minneapolis, MN, USA
| | - Clara-Lea Bonzel
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | | | | | | | | | - Tianrun Cai
- VA Boston Healthcare System, Boston, MA, USA
- Mass General Brigham, Boston, MA, USA
| | - Yuk-Lam Ho
- VA Boston Healthcare System, Boston, MA, USA
| | - Vidul A. Panickan
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | | | | | - J. Michael Gaziano
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Katherine P. Liao
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Junwei Lu
- VA Boston Healthcare System, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kelly Cho
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Tianxi Cai
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
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17
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John London A, Karlawish J, Largent EA, Phillips Hey S, McCarthy EP. Algorithmic identification of persons with dementia for research recruitment: ethical considerations. Inform Health Soc Care 2024; 49:28-41. [PMID: 38196387 PMCID: PMC11001531 DOI: 10.1080/17538157.2023.2299881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research.
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Affiliation(s)
- Alex John London
- Center for Ethics and Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Jason Karlawish
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily A. Largent
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | | | - Ellen P. McCarthy
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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18
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He S, Park S, Fujii Y, Pierce SL, Kraus EM, Wall HK, Therrien NL, Jackson SL. State-Level Hypertension Prevalence and Control Among Adults in the U.S. Am J Prev Med 2024; 66:46-54. [PMID: 37877903 PMCID: PMC10898652 DOI: 10.1016/j.amepre.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 10/26/2023]
Abstract
INTRODUCTION Improving hypertension control is a national priority. Electronic health record data have the potential to augment traditional surveillance systems. This study aimed to assess hypertension prevalence and control at the state level using a previously established electronic health record-based phenotype for hypertension. METHODS Adult patients (N=11,031,368) were included from the IQVIA ambulatory electronic medical record-U.S. 2019 data set. IQVIA ambulatory electronic medical record comprises electronic health records from >100,000 providers and includes patients from every U.S. state and Washington DC. Authors compared hypertension prevalence and control estimates against those from the Behavioral Risk Factor Surveillance System 2019. Results were age-standardized and stratified by state and sociodemographic characteristics. Statistical analyses were conducted in 2022-2023. RESULTS IQVIA ambulatory electronic medical record-U.S. patients had a median age of 55 years, and 56.7% were women. Overall age-standardized hypertension prevalence was higher in IQVIA ambulatory electronic medical record-U.S. (35.0%) than in the Behavioral Risk Factor Surveillance System (29.7%), however, state-level geographic patterns were similar, with the highest burden in the South and Appalachia. Similar patterns were also observed by sociodemographic characteristics in both data sets: hypertension prevalence was higher in older age groups (than younger), men (than women), and Black patients (than other races). Hypertension control varied widely across states: among states with >1% data coverage, control rates were lowest in Nevada (51.1%), Washington DC (52.0%), and Mississippi (55.2%); highest in Kansas (73.4%), New Jersey (72.3%), and Iowa (71.9%). CONCLUSIONS This study provided the first-ever estimates of hypertension control for all states and Washington DC. Electronic health record-based surveillance could support hypertension prevention and control efforts at the state level.
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Affiliation(s)
- Siran He
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
| | - Soyoun Park
- Division of Injury Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta Georgia
| | - Yui Fujii
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia; Bizzell U.S., New Carrollton, Maryland
| | - Samantha L Pierce
- Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Emily M Kraus
- Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia; Public Health Informatics Institute, Taskforce for Global Health, Decatur, Georgia; Kraushold Consulting, Denver, Colorado
| | - Hilary K Wall
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nicole L Therrien
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Sandra L Jackson
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
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19
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Ostropolets A, Hripcsak G, Husain SA, Richter LR, Spotnitz M, Elhussein A, Ryan PB. Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records. J Am Med Inform Assoc 2023; 31:119-129. [PMID: 37847668 PMCID: PMC10746303 DOI: 10.1093/jamia/ocad202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 09/23/2023] [Accepted: 10/02/2023] [Indexed: 10/19/2023] Open
Abstract
OBJECTIVES Chart review as the current gold standard for phenotype evaluation cannot support observational research on electronic health records and claims data sources at scale. We aimed to evaluate the ability of structured data to support efficient and interpretable phenotype evaluation as an alternative to chart review. MATERIALS AND METHODS We developed Knowledge-Enhanced Electronic Profile Review (KEEPER) as a phenotype evaluation tool that extracts patient's structured data elements relevant to a phenotype and presents them in a standardized fashion following clinical reasoning principles. We evaluated its performance (interrater agreement, intermethod agreement, accuracy, and review time) compared to manual chart review for 4 conditions using randomized 2-period, 2-sequence crossover design. RESULTS Case ascertainment with KEEPER was twice as fast compared to manual chart review. 88.1% of the patients were classified concordantly using charts and KEEPER, but agreement varied depending on the condition. Missing data and differences in interpretation accounted for most of the discrepancies. Pairs of clinicians agreed in case ascertainment in 91.2% of the cases when using KEEPER compared to 76.3% when using charts. Patient classification aligned with the gold standard in 88.1% and 86.9% of the cases respectively. CONCLUSION Structured data can be used for efficient and interpretable phenotype evaluation if they are limited to relevant subset and organized according to the clinical reasoning principles. A system that implements these principles can achieve noninferior performance compared to chart review at a fraction of time.
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Affiliation(s)
- Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
- Medical Informatics Services, New York-Presbyterian Hospital, New York, NY 10032, United States
| | - Syed A Husain
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Lauren R Richter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Ahmed Elhussein
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ 08560, United States
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20
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Kashkoush J, Gupta M, Meissner MA, Nielsen ME, Kirchner HL, Garg T. Performance Characteristics of a Rule-Based Electronic Health Record Algorithm to Identify Patients with Gross and Microscopic Hematuria. Methods Inf Med 2023; 62:183-192. [PMID: 37666279 DOI: 10.1055/a-2165-5552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
BACKGROUND Two million patients per year are referred to urologists for hematuria, or blood in the urine. The American Urological Association recently adopted a risk-stratified hematuria evaluation guideline to limit multi-phase computed tomography to individuals at highest risk of occult malignancy. OBJECTIVES To understand population-level hematuria evaluations, we developed an algorithm to accurately identify hematuria cases from electronic health records (EHRs). METHODS We used International Classification of Diseases (ICD)-9/ICD-10 diagnosis codes, urine color, and urine microscopy values to identify hematuria cases and to differentiate between gross and microscopic hematuria. Using an iterative process, we refined the ICD-9 algorithm on a gold standard, chart-reviewed cohort of 3,094 hematuria cases, and the ICD-10 algorithm on a 300 patient cohort. We applied the algorithm to Geisinger patients ≥35 years (n = 539,516) and determined performance by conducting chart review (n = 500). RESULTS After applying the hematuria algorithm, we identified 51,500 hematuria cases and 488,016 clean controls. Of the hematuria cases, 11,435 were categorized as gross, 26,658 as microscopic, 12,562 as indeterminate, and 845 were uncategorized. The positive predictive value (PPV) of identifying hematuria cases using the algorithm was 100% and the negative predictive value (NPV) was 99%. The gross hematuria algorithm had a PPV of 100% and NPV of 99%. The microscopic hematuria algorithm had lower PPV of 78% and NPV of 100%. CONCLUSION We developed an algorithm utilizing diagnosis codes and urine laboratory values to accurately identify hematuria and categorize as gross or microscopic in EHRs. Applying the algorithm will help researchers to understand patterns of care for this common condition.
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Affiliation(s)
- Jasmine Kashkoush
- Department of Urology, Geisinger, Danville, Pennsylvania, United States
| | - Mudit Gupta
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania, United States
| | | | - Matthew E Nielsen
- Department of Urology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States
- Department of Epidemiology, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, North Carolina, United States
- Department of Health Policy & Management, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, North Carolina, United States
| | - H Lester Kirchner
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, United States
| | - Tullika Garg
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, United States
- Department of Urology, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
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21
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Poulsen MN, Nordberg CM, Troiani V, Berrettini W, Asdell PB, Schwartz BS. Identification of opioid use disorder using electronic health records: Beyond diagnostic codes. Drug Alcohol Depend 2023; 251:110950. [PMID: 37716289 PMCID: PMC10620734 DOI: 10.1016/j.drugalcdep.2023.110950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population. METHODS Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms. RESULTS In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis. DISCUSSION Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. Findings show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.
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Affiliation(s)
- Melissa N Poulsen
- Department of Population Health Sciences, Geisinger, Danville, PA, USA.
| | - Cara M Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA, USA.
| | - Vanessa Troiani
- Department of Autism and Developmental Medicine, Geisinger, Lewisburg, PA, USA.
| | - Wade Berrettini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Patrick B Asdell
- Department of Family Medicine, Summa Health, Barberton, OH, USA.
| | - Brian S Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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22
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Chu CD, Xia F, Du Y, Singh R, Tuot DS, Lamprea-Montealegre JA, Gualtieri R, Liao N, Kong SX, Williamson T, Shlipak MG, Estrella MM. Estimated Prevalence and Testing for Albuminuria in US Adults at Risk for Chronic Kidney Disease. JAMA Netw Open 2023; 6:e2326230. [PMID: 37498594 PMCID: PMC10375308 DOI: 10.1001/jamanetworkopen.2023.26230] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/16/2023] [Indexed: 07/28/2023] Open
Abstract
Importance Albuminuria testing is crucial for guiding evidence-based treatments to mitigate chronic kidney disease (CKD) progression and cardiovascular morbidity, but it is widely underutilized among persons with or at risk for CKD. Objective To estimate the extent of albuminuria underdetection from lack of testing and evaluate its association with CKD treatment in a large US cohort of patients with hypertension or diabetes. Design, Setting, and Participants This cohort study examined adults with hypertension or diabetes, using data from the 2007 to 2018 National Health and Nutrition Examination Surveys (NHANES) and the Optum deidentified electronic health record (EHR) data set of diverse US health care organizations. Analyses were conducted from October 31, 2022, to May 19, 2023. Main Outcomes and Measures Using NHANES as a nationally representative sample, a logistic regression model was developed to estimate albuminuria (urine albumin-creatinine ratio ≥30 mg/g). This model was then applied to active outpatients in the EHR from January 1, 2017, to December 31, 2018. The prevalence of albuminuria among those with and without albuminuria testing during this period was estimated. A multivariable logistic regression was used to examine associations between having albuminuria testing and CKD therapies within the subsequent year (prescription for angiotensin-converting enzyme inhibitor [ACEi] or angiotensin II receptor blocker [ARB], prescription for sodium-glucose cotransporter 2 inhibitor [SGLT2i], and blood pressure control to less than 130/80 mm Hg or less than 140/90 mm Hg on the latest outpatient measure). Results The total EHR study population included 192 108 patients (mean [SD] age, 60.3 [15.1] years; 185 589 [96.6%] with hypertension; 50 507 [26.2%] with diabetes; mean [SD] eGFR, 84 [21] mL/min/1.73 m2). There were 33 629 patients (17.5%) who had albuminuria testing; of whom 11 525 (34.3%) had albuminuria. Among 158 479 patients who were untested, the estimated albuminuria prevalence rate was 13.4% (n = 21 231). Thus, only 35.2% (11 525 of 32 756) of the projected population with albuminuria had been tested. Albuminuria testing was associated with higher adjusted odds of receiving ACEi or ARB treatment (OR, 2.39 [95% CI, 2.32-2.46]), SGLT2i treatment (OR, 8.22 [95% CI, 7.56-8.94]), and having blood pressure controlled to less than 140/90 mm Hg (OR, 1.20 [95% CI, 1.16-1.23]). Conclusions and Relevance In this cohort study of patients with hypertension or diabetes, it was estimated that approximately two-thirds of patients with albuminuria were undetected due to lack of testing. These results suggest that improving detection of CKD with albuminuria testing represents a substantial opportunity to optimize care delivery for reducing CKD progression and cardiovascular complications.
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Affiliation(s)
- Chi D. Chu
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
| | - Fang Xia
- Gilead Sciences Inc, Foster City, California
| | | | | | | | - Julio A. Lamprea-Montealegre
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
| | | | | | | | | | - Michael G. Shlipak
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
| | - Michelle M. Estrella
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
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23
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Spotnitz M, Acharya N, Cimino JJ, Murphy S, Namjou B, Crimmins N, Walunas T, Liu C, Crosslin D, Benoit B, Rosenthal E, Pacheco JA, Ostropolets A, Reyes Nieva H, Patterson JS, Richter LR, Callahan TJ, Elhussein A, Pang C, Kiryluk K, Nestor J, Khan A, Mohan S, Minty E, Chung W, Wei WQ, Natarajan K, Weng C. A metadata framework for computational phenotypes. JAMIA Open 2023; 6:ooad032. [PMID: 37181728 PMCID: PMC10168627 DOI: 10.1093/jamiaopen/ooad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/10/2023] [Accepted: 04/21/2023] [Indexed: 05/16/2023] Open
Abstract
With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.
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Affiliation(s)
- Matthew Spotnitz
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Nripendra Acharya
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - James J Cimino
- Informatics Institute, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Shawn Murphy
- Laboratory of Computer Science, Mass General Brigham, Boston, Massachusetts, USA
- Department of Neurology, Mass General Brigham, Boston, Massachusetts, USA
| | - Bahram Namjou
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Nancy Crimmins
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Theresa Walunas
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Cong Liu
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - David Crosslin
- Division of Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Barbara Benoit
- Department of Research Information Science & Computing, Mass General Brigham, Boston, Massachusetts, USA
| | | | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Harry Reyes Nieva
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Jason S Patterson
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Lauren R Richter
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Ahmed Elhussein
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Chao Pang
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Jordan Nestor
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Sumit Mohan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Evan Minty
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Wendy Chung
- Department of Pediatrics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
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Kim DH, Jensen A, Jones K, Raghavan S, Phillips LS, Hung A, Sun YV, Li G, Reaven P, Zhou H, Zhou JJ. A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank. JAMIA Open 2023; 6:ooad006. [PMID: 36789288 PMCID: PMC9912368 DOI: 10.1093/jamiaopen/ooad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/19/2023] [Accepted: 01/31/2023] [Indexed: 02/12/2023] Open
Abstract
Objective Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case-control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them. Materials and Methods We developed an algorithmic approach to phenotyping the incidence of diseases by consolidating data sources from the UK Biobank (UKB), including primary care electronic health records (EHRs). We focused on defining events, event dates, and their censoring time, including relevant terms and existing phenotypes, excluding generic, rare, or semantically distant terms, forward-mapping terminology terms, and expert review. We applied our approach to phenotyping diabetes complications, including a composite cardiovascular disease (CVD) outcome, diabetic kidney disease (DKD), and diabetic retinopathy (DR), in the UKB study. Results We identified 49 049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40 193 had type 2 diabetes (T2D). A total of 23 833 diabetes subjects had linked primary care records. There were 3237, 3113, and 4922 patients with CVD, DKD, and DR events, respectively. The risk prediction performance for each outcome was assessed, and our results are consistent with the prediction area under the ROC (receiver operating characteristic) curve (AUC) of standard risk prediction models using cohort studies. Discussion and Conclusion Our publicly available pipeline and platform enable streamlined curation of incidence events, identification of time-varying risk factors underlying disease progression, and the definition of a relevant cohort for time-to-event analyses. These important steps need to be considered simultaneously to study disease progression.
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Affiliation(s)
- Do Hyun Kim
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Aubrey Jensen
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Kelly Jones
- Department of Computer Science, Columbia University, New York, New York, USA
| | - Sridharan Raghavan
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
- Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| | - Lawrence S Phillips
- Division of Endocrinology, Emory University School of Medicine, Atlanta, Georgia, USA
- Atlanta VA Medical Center, Decatur, Georgia, USA
| | - Adriana Hung
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
- Vanderbilt University, Nashville, Tennessee, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University, Atlanta, Georgia, USA
| | - Gang Li
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Peter Reaven
- Phoenix VA Health Care System, Phoenix, Arizona, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Jin J Zhou
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
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25
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Dikilitas O, Sherafati A, Saadatagah S, Satterfield BA, Kochan DC, Anderson KC, Chung WK, Hebbring SJ, Salvati ZM, Sharp RR, Sturm AC, Gibbs RA, Rowley R, Venner E, Linder JE, Jones LK, Perez EF, Peterson JF, Jarvik GP, Rehm HL, Zouk H, Roden DM, Williams MS, Manolio TA, Kullo IJ. Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003816. [PMID: 37071725 PMCID: PMC10113961 DOI: 10.1161/circgen.122.003816] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 01/03/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. METHODS Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. RESULTS The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00-4.53]) and premature CHD (odds ratio, 3.68 [2.34-5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. CONCLUSIONS In a multisite cohort of electronic health record-linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record-linked biobanks to detect FH.
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Affiliation(s)
- Ozan Dikilitas
- Department of Internal Medicine (O.D.), Mayo Clinic, Rochester, MN
- Department of Cardiovascular Medicine (O.D., A.S., S.S., B.A.S., D.C.K., I.J.K.), Mayo Clinic, Rochester, MN
| | - Alborz Sherafati
- Department of Cardiovascular Medicine (O.D., A.S., S.S., B.A.S., D.C.K., I.J.K.), Mayo Clinic, Rochester, MN
| | - Seyedmohammad Saadatagah
- Department of Cardiovascular Medicine (O.D., A.S., S.S., B.A.S., D.C.K., I.J.K.), Mayo Clinic, Rochester, MN
| | - Benjamin A Satterfield
- Department of Cardiovascular Medicine (O.D., A.S., S.S., B.A.S., D.C.K., I.J.K.), Mayo Clinic, Rochester, MN
| | - David C Kochan
- Department of Cardiovascular Medicine (O.D., A.S., S.S., B.A.S., D.C.K., I.J.K.), Mayo Clinic, Rochester, MN
| | - Katherine C Anderson
- Department of Medicine (K.C.A., J.E.L., J.F.P.), Vanderbilt University Medical Center, Nashville, TN
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University Irving Medical Center, New York (W.K.C.)
| | | | - Zachary M Salvati
- Genomic Medicine Institute, Geisinger, Danville, PA (Z.M.S., A.C.S., L.K.J., M.S.W.)
| | - Richard R Sharp
- Biomedical Ethics Research Program (R.R.S.), Mayo Clinic, Rochester, MN
| | - Amy C Sturm
- Genomic Medicine Institute, Geisinger, Danville, PA (Z.M.S., A.C.S., L.K.J., M.S.W.)
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX (R.A.G., E.V.)
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, MD (R.R., T.A.M.)
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX (R.A.G., E.V.)
| | - Jodell E Linder
- Department of Medicine (K.C.A., J.E.L., J.F.P.), Vanderbilt University Medical Center, Nashville, TN
| | - Laney K Jones
- Genomic Medicine Institute, Geisinger, Danville, PA (Z.M.S., A.C.S., L.K.J., M.S.W.)
| | - Emma F Perez
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA (E.F.P.)
| | - Josh F Peterson
- Department of Medicine (K.C.A., J.E.L., J.F.P.), Vanderbilt University Medical Center, Nashville, TN
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle (G.P.J.)
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge (H.L.R., H.Z.)
| | - Hana Zouk
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge (H.L.R., H.Z.)
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston (H.Z.)
| | - Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics (D.M.R.), Vanderbilt University Medical Center, Nashville, TN
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, PA (Z.M.S., A.C.S., L.K.J., M.S.W.)
| | - Teri A Manolio
- National Human Genome Research Institute, Bethesda, MD (R.R., T.A.M.)
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine (O.D., A.S., S.S., B.A.S., D.C.K., I.J.K.), Mayo Clinic, Rochester, MN
- Gonda Vascular Ctr (I.J.K.), Mayo Clinic, Rochester, MN
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He T, Belouali A, Patricoski J, Lehmann H, Ball R, Anagnostou V, Kreimeyer K, Botsis T. Trends and opportunities in computable clinical phenotyping: A scoping review. J Biomed Inform 2023; 140:104335. [PMID: 36933631 DOI: 10.1016/j.jbi.2023.104335] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023]
Abstract
Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.
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Affiliation(s)
- Ting He
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Anas Belouali
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jessica Patricoski
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harold Lehmann
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Ball
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Valsamo Anagnostou
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kory Kreimeyer
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Taxiarchis Botsis
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Kennedy U, Paterson M, Clark N. Using a gradient boosted model for case ascertainment from free-text veterinary records. Prev Vet Med 2023; 212:105850. [PMID: 36638610 DOI: 10.1016/j.prevetmed.2023.105850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
Case ascertainment for prevalence and incidence studies from veterinary clinical data poses a major challenge because medical notes are not consistently structured or complete. Using natural language processing (NLP) and machine learning, this study aimed to obtain accurate case recognition for feline upper respiratory tract infections (primarily caused by viruses such as feline herpes virus (FHV-1) and feline calici virus (FCV), and bacteria such as Chlamydophila felis, Mycoplasma felis and Bordetella bronchiseptica using retrospective electronic veterinary records from the Royal Society for Prevention of Cruelty to Animals, Queensland (RSPCA Qld). Data cleaning and NLP on eight years of free-text veterinary records from RSPCA Queensland was carried out to derive text-based predictors. The NLP steps included sorting records by length of stay, vectorising, tokenising and spell checking against a bespoke veterinary database. A gradient boosted model (GBM) was trained to predict the probability of each animal having a diagnosis of upper respiratory infection. A manually annotated dataset was used for training the algorithm to learn dominant patterns between predictors (frequencies of n-grams) and responses (manual binary case classification). The GBM's performance was tested against an out of sample validation dataset, and model agnostics were used to interrogate the model's learning process. The GBM used patient-level frequencies of 1250 unique n-grams as predictor variables and was able to predict the probability of cases in the validation dataset with an accuracy of 0.95 (95% CI 0.92, 0.97) and F1 score of 0.96. Predictors that exerted the highest influence on the model included frequencies of "doxycycline", "flu", "sneezing", "doxybrom" and "ocular". The trained GBM was deployed on the full dataset spanning eight years, comprising 60,258 clinical entries. The prevalence in the full dataset was predicted to be 23.59%, which is in line with domain expertise from practicing veterinarians at the shelter. Case ascertainment is a crucial step for further epidemiological study of cat flu. Ultimately, this tool can be extended to other clinical procedures, conditions, and diseases such as intensive care treatment due to snake bites and tick paralysis, physical injuries such as orthopaedic fractures or chest injuries and labour-intensive infectious diseases like parvovirus, canine cough, and ringworm, all of which require prolonged quarantine and care.
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Affiliation(s)
- Uttara Kennedy
- UQ School of Veterinary Science, The University of Queensland, Gatton, Queensland 4343, Australia; RSPCA Queensland, Animal Care Campus, 139 Wacol Station Road, Wacol, Queensland 4076, Australia.
| | - Mandy Paterson
- UQ School of Veterinary Science, The University of Queensland, Gatton, Queensland 4343, Australia; RSPCA Queensland, Animal Care Campus, 139 Wacol Station Road, Wacol, Queensland 4076, Australia
| | - Nicholas Clark
- UQ School of Veterinary Science, The University of Queensland, Gatton, Queensland 4343, Australia
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Wan NC, Yaqoob AA, Ong HH, Zhao J, Wei WQ. Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping. J Am Med Inform Assoc 2023; 30:456-465. [PMID: 36451277 PMCID: PMC9933070 DOI: 10.1093/jamia/ocac234] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 10/28/2022] [Accepted: 11/23/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP. MATERIALS AND METHODS We compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency-inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center's BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement. RESULTS Jaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration. CONCLUSIONS Resources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.
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Affiliation(s)
- Nicholas C Wan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Ali A Yaqoob
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Henry H Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Brandt PS, Kho A, Luo Y, Pacheco JA, Walunas TL, Hakonarson H, Hripcsak G, Liu C, Shang N, Weng C, Walton N, Carrell DS, Crane PK, Larson EB, Chute CG, Kullo IJ, Carroll R, Denny J, Ramirez A, Wei WQ, Pathak J, Wiley LK, Richesson R, Starren JB, Rasmussen LV. Characterizing variability of electronic health record-driven phenotype definitions. J Am Med Inform Assoc 2023; 30:427-437. [PMID: 36474423 PMCID: PMC9933077 DOI: 10.1093/jamia/ocac235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 10/19/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.
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Affiliation(s)
- Pascal S Brandt
- Department of Biomedical and Medical Education, University of Washington, Seattle, Washington, USA
| | - Abel Kho
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jennifer A Pacheco
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Theresa L Walunas
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Nephi Walton
- Intermountain Precision Genomics, Intermountain Healthcare, St George, Utah, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Eric B Larson
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Health Services, University of Washington, Seattle, Washington, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert Carroll
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Josh Denny
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Andrea Ramirez
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jyoti Pathak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Laura K Wiley
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Rachel Richesson
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Justin B Starren
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Pacheco JA, Rasmussen LV, Wiley K, Person TN, Cronkite DJ, Sohn S, Murphy S, Gundelach JH, Gainer V, Castro VM, Liu C, Mentch F, Lingren T, Sundaresan AS, Eickelberg G, Willis V, Furmanchuk A, Patel R, Carrell DS, Deng Y, Walton N, Satterfield BA, Kullo IJ, Dikilitas O, Smith JC, Peterson JF, Shang N, Kiryluk K, Ni Y, Li Y, Nadkarni GN, Rosenthal EA, Walunas TL, Williams MS, Karlson EW, Linder JE, Luo Y, Weng C, Wei W. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network. Sci Rep 2023; 13:1971. [PMID: 36737471 PMCID: PMC9898520 DOI: 10.1038/s41598-023-27481-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/03/2023] [Indexed: 02/05/2023] Open
Abstract
The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.
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Affiliation(s)
| | | | - Ken Wiley
- National Human Genome Research Institute, Bethesda, USA
| | | | - David J Cronkite
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | | | | | | | | | | | - Cong Liu
- Columbia University, New York, USA
| | - Frank Mentch
- Children's Hospital of Philadelphia, Philadelphia, USA
| | - Todd Lingren
- Cincinnati Children's Hospital Medical Center, Cincinnati, USA
| | | | | | | | | | | | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Yu Deng
- Northwestern University, Evanston, USA
| | | | | | | | | | | | | | | | | | - Yizhao Ni
- Cincinnati Children's Hospital Medical Center, Cincinnati, USA
| | - Yikuan Li
- Northwestern University, Evanston, USA
| | | | | | | | | | | | | | - Yuan Luo
- Northwestern University, Evanston, USA
| | | | - WeiQi Wei
- Vanderbilt University Medical Center, Nashville, USA
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Chen Z, Zhang H, Yang X, Wu S, He X, Xu J, Guo J, Prosperi M, Wang F, Xu H, Chen Y, Hu H, DeKosky ST, Farrer M, Guo Y, Wu Y, Bian J. Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for Alzheimer's disease and related dementias. Int J Med Inform 2023; 170:104973. [PMID: 36577203 PMCID: PMC11325083 DOI: 10.1016/j.ijmedinf.2022.104973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/11/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Cognitive tests and biomarkers are the key information to assess the severity and track the progression of Alzheimer's' disease (AD) and AD-related dementias (AD/ADRD), yet, both are often only documented in clinical narratives of patients' electronic health records (EHRs). In this work, we aim to (1) assess the documentation of cognitive tests and biomarkers in EHRs that can be used as real-world endpoints, and (2) identify, extract, and harmonize the different commonly used cognitive tests from clinical narratives using natural language processing (NLP) methods into categorical AD/ADRD severity. METHODS We developed a rule-based NLP pipeline to extract the cognitive tests and biomarkers from clinical narratives in AD/ADRD patients' EHRs. We aggregated the extracted results to the patient level and harmonized the cognitive test scores into severity categories using cutoffs determined based on both relevant literature and domain knowledge of AD/ADRD clinicians. RESULTS We identified an AD/ADRD cohort of 48,912 patients from the University of Florida (UF) Health system and identified 7 measurements (6 cognitive tests and 1 biomarker) that are frequently documented in our data. Our NLP pipeline achieved an overall F1-score of 0.9059 across the 7 measurements. Among the 6 cognitive tests, we were able to harmonize 4 cognitive test scores into severity categories, and the population characteristics of patients with different severity were described. We also identified several factors related to the availability of their documentation in EHRs. CONCLUSION This study demonstrates that our NLP pipelines can extract cognitive tests and biomarkers of AD/ADRD accurately for downstream studies. Although, the documentation of cognitive tests and biomarkers in EHRs appears to be low, RWD is still an important resource for AD/ADRD research. Nevertheless, providing standardized approach to document cognitive tests and biomarkers in EHRS are also warranted.
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Affiliation(s)
- Zhaoyi Chen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Xi Yang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Songzi Wu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Xing He
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Hua Xu
- Center for Translational AI in Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Hui Hu
- Channing Division of Network Medicine at Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven T DeKosky
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Matthew Farrer
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
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Yang S, Varghese P, Stephenson E, Tu K, Gronsbell J. Machine learning approaches for electronic health records phenotyping: a methodical review. J Am Med Inform Assoc 2023; 30:367-381. [PMID: 36413056 PMCID: PMC9846699 DOI: 10.1093/jamia/ocac216] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS AND METHODS We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies. RESULTS Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. DISCUSSION Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released. CONCLUSION Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.
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Affiliation(s)
- Siyue Yang
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | | | - Ellen Stephenson
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen Tu
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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Oh W, Nadkarni GN. Federated Learning in Health care Using Structured Medical Data. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:4-16. [PMID: 36723280 PMCID: PMC10208416 DOI: 10.1053/j.akdh.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way to access a vast amount of data, sharing data outside the institutes involves legal, business, and technical challenges. Federated learning (FL) is a newly proposed machine learning framework for multicenter studies, tackling data-sharing issues across participant institutes. The promise of FL is simple. FL facilitates multicenter studies without losing data access control and allows the construction of a global model by aggregating local models trained from participant institutes. This article reviewed recently published studies that utilized FL in clinical studies with structured medical data. In addition, challenges and open questions in FL in clinical studies with structured medical data were discussed.
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Affiliation(s)
- Wonsuk Oh
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
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Lake AM, Goleva SB, Samuels LR, Carpenter LM, Davis LK. Sex Differences in Health Conditions Associated with Sexual Assault in a Large Hospital Population. Complex Psychiatry 2023; 8:80-89. [PMID: 36660008 PMCID: PMC10288064 DOI: 10.1159/000527363] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/25/2022] [Indexed: 01/21/2023] Open
Abstract
Introduction Sexual assault is an urgent public health concern with both immediate and long-lasting health consequences, affecting 44% of women and 25% of men during their lifetimes. Large studies are needed to understand the unique healthcare needs of this patient population. Methods We mined clinical notes to identify patients with a history of sexual assault in the electronic health record (EHR) at Vanderbilt University Medical Center (VUMC), a large university hospital in the Southeastern USA, from 1989 to 2021 (N = 3,376,424). Using a phenome-wide case-control study, we identified diagnoses co-occurring with disclosures of sexual assault. We performed interaction tests to examine whether sex modified any of these associations. Association analyses were restricted to a subset of patients receiving regular care at VUMC (N = 833,185). Results The phenotyping approach identified 14,496 individuals (0.43%) across the VUMC-EHR with documentation of sexual assault and achieved a positive predictive value of 93.0% (95% confidence interval = 85.6-97.0%), determined by manual patient chart review. Out of 1,703 clinical diagnoses tested across all subgroup analyses, 465 were associated with sexual assault. Sex-by-trauma interaction analysis revealed 55 sex-differential associations and demonstrated increased odds of psychiatric diagnoses in male survivors. Discussion This case-control study identified associations between disclosures of sexual assault and hundreds of health conditions, many of which demonstrated sex-differential effects. The findings of this study suggest that patients who have experienced sexual assault are at risk for developing wide-ranging medical and psychiatric comorbidities and that male survivors may be particularly vulnerable to developing mental illness.
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Affiliation(s)
- Allison M. Lake
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Slavina B. Goleva
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lauren R. Samuels
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - Laura M. Carpenter
- Department of Sociology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Joo YY, Pacheco JA, Thompson WK, Rasmussen-Torvik LJ, Rasmussen LV, Lin FTJ, Andrade MD, Borthwick KM, Bottinger E, Cagan A, Carrell DS, Denny JC, Ellis SB, Gottesman O, Linneman JG, Pathak J, Peissig PL, Shang N, Tromp G, Veerappan A, Smith ME, Chisholm RL, Gawron AJ, Hayes MG, Kho AN. Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm. PLoS One 2023; 18:e0283553. [PMID: 37196047 DOI: 10.1371/journal.pone.0283553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 03/09/2023] [Indexed: 05/19/2023] Open
Abstract
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.
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Affiliation(s)
- Yoonjung Yoonie Joo
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - William K Thompson
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Frederick T J Lin
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Mariza de Andrade
- College of Medicine, Mayo Clinic, Rochester, MN, United States of America
| | | | - Erwin Bottinger
- Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Andrew Cagan
- Partners Healthcare, Charlestown, MA, United States of America
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States of America
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, United States of America
| | - Stephen B Ellis
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - James G Linneman
- Office of Research Computing and Analytics, Marshfield Clinic Research Institute, Marshfield, WI, United States of America
| | - Jyotishman Pathak
- Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, United States of America
| | - Peggy L Peissig
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, United States of America
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University, New York, NY, United States of America
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Annapoorani Veerappan
- Department of Medicine, Gastroenterology, Duke University, Durham, NC, United States of America
| | - Maureen E Smith
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Andrew J Gawron
- Division of Gastroenterology, Hepatology & Nutrition, University of Utah, Salt Lake City, UT, United States of America
| | - M Geoffrey Hayes
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
- Department of Anthropology, Northwestern University, Evanston, IL, United States of America
| | - Abel N Kho
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
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Lamprea-Montealegre JA, Madden E, Tummalapalli SL, Chu CD, Peralta CA, Du Y, Singh R, Kong SX, Tuot DS, Shlipak MG, Estrella MM. Prescription Patterns of Cardiovascular- and Kidney-Protective Therapies Among Patients With Type 2 Diabetes and Chronic Kidney Disease. Diabetes Care 2022; 45:2900-2906. [PMID: 36156061 PMCID: PMC9998844 DOI: 10.2337/dc22-0614] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/29/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To assess the prevalence and correlates of prescription of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and/or glucagon-like peptide 1 receptor agonists (GLP1-RA) in individuals with type 2 diabetes mellitus (T2DM) with and without chronic kidney disease (CKD). RESEARCH DESIGN AND METHODS This was a cross-sectional analyses of SGLT2i and GLP1-RA prescriptions from 1 January 2019 to 31 December 2020 in the Veterans Health Administration System. The likelihood of prescriptions was examined by the presence or absence of CKD and by predicted risks of atherosclerotic cardiovascular disease (ASCVD) and end-stage kidney disease (ESKD). RESULTS Of 1,197,880 adults with T2DM, SGLT2i and GLP1-RA were prescribed to 11% and 8% of patients overall, and to 12% and 10% of those with concomitant CKD, respectively. In adjusted models, patients with severe albuminuria were less likely to be prescribed SGLT2i or GLP1-RA versus nonalbuminuric patients with CKD, with odds ratios (ORs) of 0.91 (95% CI 0.89, 0.93) and 0.97 (0.94, 1.00), respectively. Patients with a 10-year ASCVD risk >20% (vs. <5%), had lower odds of SGLT2i use (OR 0.66 [0.61, 0.71]) and GLP1-RA prescription (OR 0.55 [0.52, 0.59]). A 5-year ESKD risk >5%, compared with <1%, was associated with lower likelihood of SGLT2i prescription (OR 0.63 [0.59, 0.67]) but higher likelihood of GLP1-RA prescription (OR 1.53 [1.46, 1.61]). CONCLUSIONS Among a large cohort of patients with T2DM, prescription of SGLT2i and GLP1-RA was low in those with CKD. We observed a "risk-treatment paradox," whereby patients with higher risk of adverse outcomes were less likely to receive these therapies.
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Affiliation(s)
- Julio A. Lamprea-Montealegre
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
- San Francisco Veterans Administration Health Care System, San Francisco, CA
| | - Erin Madden
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
- San Francisco Veterans Administration Health Care System, San Francisco, CA
| | - Sri Lekha Tummalapalli
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
- San Francisco Veterans Administration Health Care System, San Francisco, CA
- Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Chi D. Chu
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
| | - Carmen A. Peralta
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
- Cricket Health, Inc., San Francisco, CA
| | - Yuxian Du
- Bayer Healthcare U.S. LLC, Whippany, NJ
| | | | | | - Delphine S. Tuot
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
| | - Michael G. Shlipak
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
- San Francisco Veterans Administration Health Care System, San Francisco, CA
| | - Michelle M. Estrella
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Kidney Health Research Collaborative, University of California, San Francisco, San Francisco, CA
- San Francisco Veterans Administration Health Care System, San Francisco, CA
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Ahuja Y, Wen J, Hong C, Xia Z, Huang S, Cai T. A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record. Sci Rep 2022; 12:17737. [PMID: 36273240 PMCID: PMC9588081 DOI: 10.1038/s41598-022-22585-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 10/17/2022] [Indexed: 01/18/2023] Open
Abstract
While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP's feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50-100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.
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Affiliation(s)
- Yuri Ahuja
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA. .,Harvard Medical School, Boston, MA, USA. .,Department of Medicine, NYU Langone Health, New York, NY, USA.
| | - Jun Wen
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Chuan Hong
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Zongqi Xia
- grid.21925.3d0000 0004 1936 9000Department of Neurology, University of Pittsburgh, Pittsburgh, PA USA
| | - Sicong Huang
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA USA ,grid.410370.10000 0004 4657 1992VA Boston Healthcare System, Boston, MA USA
| | - Tianxi Cai
- grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115 USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA ,grid.410370.10000 0004 4657 1992VA Boston Healthcare System, Boston, MA USA
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Berchuck SI, Jammal AA, Page D, Somers TJ, Medeiros FA. A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm. Transl Vis Sci Technol 2022; 11:6. [PMID: 36180026 PMCID: PMC9547354 DOI: 10.1167/tvst.11.10.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/14/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose In patients with ophthalmic disorders, psychosocial risk factors play an important role in morbidity and mortality. Proper and early psychiatric screening can result in prompt intervention and mitigate its impact. Because screening is resource intensive, we developed a framework for automating screening using an electronic health record (EHR)-derived artificial intelligence (AI) algorithm. Methods Subjects came from the Duke Ophthalmic Registry, a retrospective EHR database for the Duke Eye Center. Inclusion criteria included at least two encounters and a minimum of 1 year of follow-up. Presence of distress was defined at the encounter level using a computable phenotype. Risk factors included available EHR history. At each encounter, risk factors were used to discriminate psychiatric status. Model performance was evaluated using area under the receiver operating characteristic (ROC) curve and area under the precision-recall curve (PR AUC). Variable importance was presented using odds ratios (ORs). Results Our cohort included 358,135 encounters from 40,326 patients with an average of nine encounters per patient over 4 years. The ROC and PR AUC were 0.91 and 0.55, respectively. Of the top 25 predictors, the majority were related to existing distress, but some indicated stressful conditions, including chemotherapy (OR = 1.36), esophageal disorders (OR = 1.31), central pain syndrome (OR = 1.25), and headaches (OR = 1.24). Conclusions Psychiatric distress in ophthalmology patients can be monitored passively using an AI algorithm trained on existing EHR data. Translational Relevance When paired with an effective referral and treatment program, such algorithms may improve health outcomes in ophthalmology.
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Affiliation(s)
| | - Alessandro A. Jammal
- Duke Eye Center and Department of Ophthalmology, Duke University, Durham, NC, USA
| | - David Page
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Tamara J. Somers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Felipe A. Medeiros
- Duke Eye Center and Department of Ophthalmology, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
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McCoy SS, Hetzel S, VanWormer JJ, Bartels CM. Sex hormones, body mass index, and related comorbidities associated with developing Sjögren's disease: a nested case-control study. Clin Rheumatol 2022; 41:3065-3074. [PMID: 35701626 PMCID: PMC9610811 DOI: 10.1007/s10067-022-06226-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Sjögren's disease (SjD), a highly female predominant systemic autoimmune disease, peaks in perimenopause. Prior studies lack details on timing or type of sex hormone exposure. We examined SjD risk using endogenous and exogenous hormone exposure and related comorbidities. METHODS We performed a retrospective case-control study of adult women, nested within a population cohort. Cases had SjD diagnosed by a rheumatology provider or two SjD diagnoses from a non-rheumatology provider with a positive anti-SSA antibody or salivary gland biopsy. Cases were age-matched to three SjD-free controls. We calculated modified composite estrogen scores (mCES) and collected demographics, comorbidities, and endogenous and exogenous hormone exposures. Risk ratios were adjusted for demographics. RESULTS Of 546 SjD cases and 1637 age-matched controls, mCES was not significantly associated with SjD in adjusted models. The top individual hormone exposures associated with SjD included estrogen replacement therapy (ERT; RR 1.78 [95% CI 1.47-2.14]), polycystic ovarian syndrome (1.65 [1.28-2.12]), and hysterectomy without bilateral oophorectomy (1.51 [1.13-2.03]). We identified comorbidities preceding SjD including fibromyalgia, pulmonary disease, diabetes, lymphoma, osteoporosis, peripheral vascular disease, and renal disease. Taking comorbidities into account, we developed a predictive model for SjD that included fibromyalgia (2.50 [1.93-3.25]), osteoporosis (1.84 [1.27-2.66]), hormone replacement therapy (HRT) (1.61 [1.22-2.12]), diabetes (0.27 [0.13-0.50]), and body mass index (BMI) (0.97 [0.95-0.99]). CONCLUSIONS We report a novel algorithm to improve identifying patients at risk for SjD and describe sex hormone association with SjD. Finally, we report new comorbidities associated with SjD decrease, BMI and diabetes, and increase, lymphoma and osteoporosis.. Key Points •Given female predominance and typical perimenopausal onset, sex hormones should be considered when studying comorbidities in Sjögren's disease. •The top exposures associated with developing Sjögren's disease included fibromyalgia, osteoporosis, and use of hormone replacement therapy. Possible protective factors included prior diabetes and higher body mass index. •We used our newly identified exposures to generate a predictive algorithm, which has potential to improve diagnosis and pathogenic insights into Sjögren's disease.
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Affiliation(s)
- Sara S McCoy
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin, 1685 Highland Avenue, Madison, WI, 53705-2281, USA.
| | - Scott Hetzel
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Population Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Jeffrey J VanWormer
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, WI, USA
| | - Christie M Bartels
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin, 1685 Highland Avenue, Madison, WI, 53705-2281, USA
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Hamidi B, Flume PA, Simpson KN, Alekseyenko AV. Not all phenotypes are created equal: covariates of success in e-phenotype specification. J Am Med Inform Assoc 2022; 30:213-221. [PMID: 36069977 PMCID: PMC9846689 DOI: 10.1093/jamia/ocac157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/31/2022] [Accepted: 08/22/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results. MATERIALS AND METHODS Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models. RESULTS E-phenotype validation rates varied according to experts' domains and query characteristics (mean = 61%, range 20-100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate. DISCUSSIONS AND CONCLUSIONS Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users' confidence, but rather on creating highly specific e-phenotypes.
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Affiliation(s)
- Bashir Hamidi
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina 29425, USA
| | - Patrick A Flume
- Department of Medicine, Medical University of South Carolina, Charleston, South Carolina 29425, USA
| | - Kit N Simpson
- Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston, South Carolina 29425, USA
| | - Alexander V Alekseyenko
- Corresponding Author: Alexander V. Alekseyenko, PhD, 22 WestEdge St, Rm WG213, MSC 200, Charleston, SC 29403, USA;
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Lamprea-Montealegre JA, Madden E, Tummalapalli SL, Peralta C, Neilands TB, Garcia PK, Muiru A, Karliner L, Shlipak MG, Estrella MM. Association of Race and Ethnicity With Prescription of SGLT2 Inhibitors and GLP1 Receptor Agonists Among Patients With Type 2 Diabetes in the Veterans Health Administration System. JAMA 2022; 328:861-871. [PMID: 36066519 PMCID: PMC9449794 DOI: 10.1001/jama.2022.13885] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
IMPORTANCE Novel therapies for type 2 diabetes can reduce the risk of cardiovascular disease and chronic kidney disease progression. The equitability of these agents' prescription across racial and ethnic groups has not been well-evaluated. OBJECTIVE To investigate differences in the prescription of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) among adult patients with type 2 diabetes by racial and ethnic groups. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional analysis of data from the US Veterans Health Administration's Corporate Data Warehouse. The sample included adult patients with type 2 diabetes and at least 2 primary care clinic visits from January 1, 2019, to December 31, 2020. EXPOSURES Self-identified race and self-identified ethnicity. MAIN OUTCOMES AND MEASURES The primary outcomes were prevalent SGLT2i or GLP-1 RA prescription, defined as any active prescription during the study period. RESULTS Among 1 197 914 patients (mean age, 68 years; 96% men; 1% American Indian or Alaska Native, 2% Asian, Native Hawaiian, or Other Pacific Islander, 20% Black or African American, 71% White, and 7% of Hispanic or Latino ethnicity), 10.7% and 7.7% were prescribed an SGLT2i or a GLP-1 RA, respectively. Prescription rates for SGLT2i and GLP-1 RA, respectively, were 11% and 8.4% among American Indian or Alaska Native patients; 11.8% and 8% among Asian, Native Hawaiian, or Other Pacific Islander patients; 8.8% and 6.1% among Black or African American patients; and 11.3% and 8.2% among White patients, respectively. Prescription rates for SGLT2i and GLP-1 RA, respectively, were 11% and 7.1% among Hispanic or Latino patients and 10.7% and 7.8% among non-Hispanic or Latino patients. After accounting for patient- and system-level factors, all racial groups had significantly lower odds of SGLT2i and GLP-1 RA prescription compared with White patients. Black patients had the lowest odds of prescription compared with White patients (adjusted odds ratio, 0.72 [95% CI, 0.71-0.74] for SGLT2i and 0.64 [95% CI, 0.63-0.66] for GLP-1 RA). Patients of Hispanic or Latino ethnicity had significantly lower odds of prescription (0.90 [95% CI, 0.88-0.93] for SGLT2i and 0.88 [95% CI, 0.85-0.91] for GLP-1 RA) compared with non-Hispanic or Latino patients. CONCLUSIONS AND RELEVANCE Among patients with type 2 diabetes in the Veterans Health Administration system during 2019 and 2020, prescription rates of SGLT2i and GLP-1 RA medications were low, and individuals of several different racial groups and those of Hispanic ethnicity had statistically significantly lower odds of receiving prescriptions for these medications compared with individuals of White race and non-Hispanic ethnicity. Further research is needed to understand the mechanisms underlying these differences in rates of prescribing and the potential relationship with differences in clinical outcomes.
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Affiliation(s)
- Julio A. Lamprea-Montealegre
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
| | - Erin Madden
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
| | - Sri Lekha Tummalapalli
- Kidney Health Research Collaborative, University of California, San Francisco
- Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Carmen Peralta
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
- Cricket Health Inc, San Francisco, California
| | - Torsten B. Neilands
- Department of Medicine, University of California, San Francisco
- San Francisco Center for Aging in Diverse Communities and the Multiethnic Health Equity Research Center, University of California, San Francisco
| | - Paola K. Garcia
- Division of Nephrology, Department of Medicine, Pontificia Universidad Javeriana School of Medicine, Bogotá, Colombia
| | - Anthony Muiru
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
| | - Leah Karliner
- Department of Medicine, University of California, San Francisco
- San Francisco Center for Aging in Diverse Communities and the Multiethnic Health Equity Research Center, University of California, San Francisco
| | - Michael G. Shlipak
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
| | - Michelle M. Estrella
- Department of Medicine, University of California, San Francisco
- Kidney Health Research Collaborative, University of California, San Francisco
- San Francisco VA Health Care System, San Francisco, California
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Levenson M, He W, Chen L, Dharmarajan S, Izem R, Meng Z, Pang H, Rockhold F. Statistical consideration for fit-for-use real-world data to support regulatory decision making in drug development. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2120533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
| | - Weili He
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Li Chen
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | | | - Rima Izem
- Novartis Institutes for BioMedical Research Basel, Basel, Basel-Stadt, CH
| | | | | | - Frank Rockhold
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
- Duke Clinical Research Institute, Duke University, Durham, NC
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Ahuja Y, Zou Y, Verma A, Buckeridge D, Li Y. MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record. J Biomed Inform 2022; 134:104190. [PMID: 36058522 DOI: 10.1016/j.jbi.2022.104190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 08/27/2022] [Accepted: 08/28/2022] [Indexed: 01/18/2023]
Abstract
Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.
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Affiliation(s)
- Yuri Ahuja
- Department of Biostatistics, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.
| | - Yuesong Zou
- School of Computer Science, McGill University, 3480 Rue University, Montreal, QC H3A 2A7, Canada
| | - Aman Verma
- School of Population and Global Health, McGill University, 2001 McGill College Avenue, Montreal, Québec H3A 1G1, Canada
| | - David Buckeridge
- School of Population and Global Health, McGill University, 2001 McGill College Avenue, Montreal, Québec H3A 1G1, Canada.
| | - Yue Li
- School of Computer Science, McGill University, 3480 Rue University, Montreal, QC H3A 2A7, Canada.
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Martin TR, Zemans RL, Ware LB, Schmidt EP, Riches DWH, Bastarache L, Calfee CS, Desai TJ, Herold S, Hough CL, Looney MR, Matthay MA, Meyer N, Parikh SM, Stevens T, Thompson BT. New Insights into Clinical and Mechanistic Heterogeneity of the Acute Respiratory Distress Syndrome: Summary of the Aspen Lung Conference 2021. Am J Respir Cell Mol Biol 2022; 67:284-308. [PMID: 35679511 PMCID: PMC9447141 DOI: 10.1165/rcmb.2022-0089ws] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/09/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical and molecular heterogeneity are common features of human disease. Understanding the basis for heterogeneity has led to major advances in therapy for many cancers and pulmonary diseases such as cystic fibrosis and asthma. Although heterogeneity of risk factors, disease severity, and outcomes in survivors are common features of the acute respiratory distress syndrome (ARDS), many challenges exist in understanding the clinical and molecular basis for disease heterogeneity and using heterogeneity to tailor therapy for individual patients. This report summarizes the proceedings of the 2021 Aspen Lung Conference, which was organized to review key issues related to understanding clinical and molecular heterogeneity in ARDS. The goals were to review new information about ARDS phenotypes, to explore multicellular and multisystem mechanisms responsible for heterogeneity, and to review how best to account for clinical and molecular heterogeneity in clinical trial design and assessment of outcomes. The report concludes with recommendations for future research to understand the clinical and basic mechanisms underlying heterogeneity in ARDS to advance the development of new treatments for this life-threatening critical illness.
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Affiliation(s)
- Thomas R. Martin
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Rachel L. Zemans
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine and Program in Cellular and Molecular Biology, University of Michigan School of Medicine, Ann Arbor, Michigan
| | - Lorraine B. Ware
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine and
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Eric P. Schmidt
- Division of Pulmonary Sciences and Critical Care, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado
| | - David W. H. Riches
- Division of Pulmonary Sciences and Critical Care, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado
- Program in Cell Biology, Department of Pediatrics, National Jewish Health, Denver, Colorado
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Carolyn S. Calfee
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Medicine
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Anesthesia
| | - Tushar J. Desai
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Stem Cell Institute, Stanford University School of Medicine, Stanford, California
| | - Susanne Herold
- Department of Internal Medicine VI and Cardio-Pulmonary Institute (CPI), Universities of Giessen and Marburg Lung Center (UGMLC), Giessen, Germany
| | - Catherine L. Hough
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Oregon Health & Science University, Portland, Oregon
| | | | - Michael A. Matthay
- Departments of Medicine and Anesthesia, Cardiovascular Research Institute, University of California San Francisco, San Francisco, California
| | - Nuala Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Samir M. Parikh
- Center for Vascular Biology Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
- Division of Nephrology, University of Texas Southwestern, Dallas, Texas
| | - Troy Stevens
- Department of Physiology and Cell Biology, College of Medicine, Center for Lung Biology, University of South Alabama, Mobile, Alabama; and
| | - B. Taylor Thompson
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts
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Mayer DA, Rasmussen LV, Roark CD, Kahn MG, Schilling LM, Wiley LK. ReviewR: a light-weight and extensible tool for manual review of clinical records. JAMIA Open 2022; 5:ooac071. [PMID: 35936991 PMCID: PMC9350014 DOI: 10.1093/jamiaopen/ooac071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 02/01/2023] Open
Abstract
Objectives Manual record review is a crucial step for electronic health record (EHR)-based research, but it has poor workflows and is error prone. We sought to build a tool that provides a unified environment for data review and chart abstraction data entry. Materials and Methods ReviewR is an open-source R Shiny application that can be deployed on a single machine or made available to multiple users. It supports multiple data models and database systems, and integrates with the REDCap API for storing abstraction results. Results We describe 2 real-world uses and extensions of ReviewR. Since its release in April 2021 as a package on CRAN it has been downloaded 2204 times. Discussion and Conclusion ReviewR provides an easily accessible review interface for clinical data warehouses. Its modular, extensible, and open source nature afford future expansion by other researchers.
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Affiliation(s)
- David A Mayer
- Department of Biomedical Informatics, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Christopher D Roark
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lisa M Schilling
- Division of General Internal Medicine, Department of Medicine, Data Science to Patient Value Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Laura K Wiley
- Corresponding Author: Laura K. Wiley, PhD, Department of Biomedical Informatics, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, 1890 N. Revere Court, Aurora, CO 80045, USA;
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Kutney-Lee A, Khazanov GK, Carpenter JG, Griffin H, Kinder D, Shreve ST, Smith D, Thorpe JM, Ersek M. Palliative Care and Documented Suicide: Association Among Veterans With High Mortality Risk. J Pain Symptom Manage 2022; 64:e63-e69. [PMID: 35489665 DOI: 10.1016/j.jpainsymman.2022.04.179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/11/2022] [Accepted: 04/17/2022] [Indexed: 11/17/2022]
Abstract
CONTEXT Palliative care consultations (PCCs) are associated with reduced physical and psychological symptoms that are related to suicide risk. Little is known, however, about the association between PCCs and death from suicide among patients at high risk of short-term mortality. OBJECTIVE To examine the association between the number of PCCs and documentation of suicide in a cohort of Veterans at high risk of short-term mortality, before and after accounting for Veterans' sociodemographic characteristics and clinical conditions. METHODS An observational cohort study was conducted using linked Veterans Affairs clinical and administrative databases for 580,620 decedents with high risk of one-year mortality. Logistic regression models were used to examine the association between number of PCCs and documentation of suicide. RESULTS Higher percentages of Veterans who died by suicide were diagnosed with chronic pulmonary disease as well as mental health/substance use conditions compared with Veterans who died from other causes. In adjusted models, one PCC in the 90 days prior to death was significantly associated with a 71% decrease in the odds of suicide (OR = 0.29, 95% CI = 0.23-0.37, P < 0.001) and two or more PCCs were associated with a 78% decrease (OR = 0.22, 95% CI = 0.15-0.33, P < 0.001). Associated "number needed to be exposed" estimates suggest that 421 Veterans in this population would need to receive at least one PCC to prevent one suicide. CONCLUSION While acknowledging the importance of specialized mental health care in reducing suicide among high-risk patients, interventions delivered in the context of PCCs may also play a role.
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Affiliation(s)
- Ann Kutney-Lee
- Veteran Experience Center (A.K.L., J.G.C., H.G., D.K., D.S., J.M.T.,), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA; Center for Health Equity Research and Promotion (A.K.L., M.E.), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA; University of Pennsylvania School of Nursing (A.K.L., M.E.), Philadelphia, Pennsylvania, USA.
| | - Gabriela K Khazanov
- Mental Illness Research, Education, and Clinical Center of the Veterans Integrated Service Network 4 (G.K.K.), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Joan G Carpenter
- Veteran Experience Center (A.K.L., J.G.C., H.G., D.K., D.S., J.M.T.,), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA; University of Maryland School of Nursing (J.G.C.), Baltimore, Maryland, USA
| | - Hilary Griffin
- Veteran Experience Center (A.K.L., J.G.C., H.G., D.K., D.S., J.M.T.,), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Daniel Kinder
- Veteran Experience Center (A.K.L., J.G.C., H.G., D.K., D.S., J.M.T.,), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Scott T Shreve
- Palliative and Hospice Care Program Office (S.T.S.), US Department of Veterans Affairs, Washington, District of Columbia, USA; Penn State College of Medicine (S.T.S.), Hershey, Pennsylvania, USA
| | - Dawn Smith
- Veteran Experience Center (A.K.L., J.G.C., H.G., D.K., D.S., J.M.T.,), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Joshua M Thorpe
- Veteran Experience Center (A.K.L., J.G.C., H.G., D.K., D.S., J.M.T.,), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA; Center for Health Equity Research and Promotion (J.M.T.), Pittsburgh VA Medical Center, Pittsburgh, Pennsylvania, USA; University of North Carolina School of Pharmacy (J.M.T.), Chapel Hill, North Carolina, USA
| | - Mary Ersek
- Center for Health Equity Research and Promotion (A.K.L., M.E.), Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA; University of Pennsylvania School of Nursing (A.K.L., M.E.), Philadelphia, Pennsylvania, USA
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Brandt PS, Pacheco JA, Adekkanattu P, Sholle ET, Abedian S, Stone DJ, Knaack DM, Xu J, Xu Z, Peng Y, Benda NC, Wang F, Luo Y, Jiang G, Pathak J, Rasmussen LV. Design and validation of a FHIR-based EHR-driven phenotyping toolbox. J Am Med Inform Assoc 2022; 29:1449-1460. [PMID: 35799370 PMCID: PMC9382394 DOI: 10.1093/jamia/ocac063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 04/04/2022] [Accepted: 06/17/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms. MATERIALS AND METHODS We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation. We validated the performance of the tool by executing a thrombotic event phenotype definition at 3 sites, Mayo Clinic (MC), Northwestern Medicine (NM), and Weill Cornell Medicine (WCM), and used manual review to determine precision and recall. RESULTS An initial version of the PhEMA Workbench has been released, which supports phenotype authoring, execution, and publishing to a shared phenotype definition repository. The resulting thrombotic event phenotype definition consisted of 11 CQL statements, and 24 value sets containing a total of 834 codes. Technical validation showed satisfactory performance (both NM and MC had 100% precision and recall and WCM had a precision of 95% and a recall of 84%). CONCLUSIONS We demonstrate that the PhEMA Workbench can facilitate EHR-driven phenotype definition, execution, and phenotype sharing in heterogeneous clinical research data environments. A phenotype definition that integrates with existing standards-compliant systems, and the use of a formal representation facilitates automation and can decrease potential for human error.
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Affiliation(s)
- Pascal S Brandt
- Corresponding Author: Pascal S. Brandt, Department of Biomedical Informatics & Medical Education, University of Washington, Box 358047, Seattle, WA 98195, USA;
| | - Jennifer A Pacheco
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Prakash Adekkanattu
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Evan T Sholle
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Sajjad Abedian
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Daniel J Stone
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - David M Knaack
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Jie Xu
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Zhenxing Xu
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Yifan Peng
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Natalie C Benda
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Fei Wang
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Jyotishman Pathak
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Krantz MS, Kerchberger VE, Wei WQ. Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics). THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2022; 10:1757-1762. [PMID: 35487368 PMCID: PMC9624141 DOI: 10.1016/j.jaip.2022.04.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.
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Affiliation(s)
- Matthew S Krantz
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn.
| | - V Eric Kerchberger
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tenn
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tenn
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Binkheder S, Wu HY, Quinney SK, Zhang S, Zitu MM, Chiang CW, Wang L, Jones J, Li L. PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature. J Biomed Semantics 2022; 13:17. [PMID: 35690873 PMCID: PMC9188713 DOI: 10.1186/s13326-022-00272-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/18/2022] [Indexed: 12/28/2022] Open
Abstract
Background Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. Results Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. Conclusions The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications. Supplementary Information The online version contains supplementary material available at 10.1186/s13326-022-00272-6.
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Affiliation(s)
- Samar Binkheder
- Department of Biohealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA.,Medical Informatics Unit, Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Heng-Yi Wu
- Development Science Informatics, Genentech, South San Francisco, CA, USA
| | - Sara K Quinney
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shijun Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Md Muntasir Zitu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Josette Jones
- Department of Biohealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA. .,, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
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Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet 2022; 23:665-679. [PMID: 35581355 DOI: 10.1038/s41576-022-00487-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2022] [Indexed: 01/02/2023]
Abstract
Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.
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