Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.
Background: Electronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data...
| Publicado en: | Methods of Information in Medicine Vol. 60; no. 3/4; pp. 84 - 95 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153534448&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153534448 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2021 vid: 60 iid: 3/4 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 153534448 153534448 NLM34592777 10.1055/s-0041-1735619 NLM34592777 153534448 ppf: 84 ppct: 11 formats: tig: atl: Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation. aug: au: Liu, Yiyang Siddiqi, Khairul A. Cook, Robert L. Bian, Jiang Squires, Patrick J. Shenkman, Elizabeth A. Prosperi, Mattia Jayaweera, Dushyantha T. affil: PhD, MPH, Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100231, Gainesville 32610-0231, Florida, United States sug: subj: HIV Infections Diagnosis HIV Infections Drug Therapy HIV Infections Epidemiology Adult Prospective Studies Male Phenotype Algorithms Female Scales Adult: 19-44 years Male Female ab: Background: Electronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data from the OneFlorida Data Trust, a repository of linked claims data and EHRs from its clinical partners, which provide care to over 15 million patients across all 67 counties in Florida.Methods: Our computable phenotype examined information from multiple EHR domains, including clinical encounters with diagnoses, prescription medications, and laboratory tests. To identify an HIV case, the algorithm requires the patient to have at least one diagnostic code for HIV and meet one of the following criteria: have 1+ positive HIV laboratory, have been prescribed with HIV medications, or have 3+ visits with HIV diagnostic codes. The computable phenotype was validated against a subset of clinical notes.Results: Among the 15+ million patients from OneFlorida, we identified 61,313 patients with confirmed HIV diagnosis. Among them, 8.05% met all four inclusion criteria, 69.7% met the 3+ HIV encounters criteria in addition to having HIV diagnostic code, and 8.1% met all criteria except for having positive laboratories. Our algorithm achieved higher sensitivity (98.9%) and comparable specificity (97.6%) relative to existing algorithms (77-83% sensitivity, 86-100% specificity). The mean age of the sample was 42.7 years, 58% male, and about half were Black African American. Patients' average follow-up period (the time between the first and last encounter in the EHRs) was approximately 4.6 years. The median number of all encounters and HIV-related encounters were 79 and 21, respectively.Conclusion: By leveraging EHR data from multiple clinical partners and domains, with a considerably diverse population, our algorithm allows more flexible criteria for identifying patients with incomplete laboratory test results and medication prescribing history compared with prior studies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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