Dataset Information

Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.

ABSTRACT:

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.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8672443 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.

Liu Yiyang Y Siddiqi Khairul A KA Cook Robert L RL Bian Jiang J Squires Patrick J PJ Shenkman Elizabeth A EA Prosperi Mattia M Jayaweera Dushyantha T DT

Methods of information in medicine 20210930 3-04

<h4>Background</h4>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.<h4>Meth ...[more]

PMID: 34592777

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Project description:BackgroundElectronic health record (EHR) systems are widely used in the United States to document care delivery and outcomes. Health information exchange (HIE) networks, which integrate EHR data from the various health care providers treating patients, are increasingly used to analyze population-level data. Existing methods for population health surveillance of essential hypertension by public health authorities may be complemented using EHR data from HIE networks to characterize disease burden at the community level.ObjectiveWe aimed to derive and validate computable phenotypes (CPs) to estimate hypertension prevalence for population-based surveillance using an HIE network.MethodsUsing existing data available from an HIE network, we developed 6 candidate CPs for essential (primary) hypertension in an adult population from a medium-sized Midwestern metropolitan area in the United States. A total of 2 independent clinician reviewers validated the phenotypes through a manual chart review of 150 randomly selected patient records. We assessed the precision of CPs by calculating sensitivity, specificity, positive predictive value (PPV), F1-score, and validity of chart reviews using prevalence-adjusted bias-adjusted κ. We further used the most balanced CP to estimate the prevalence of hypertension in the population.ResultsAmong a cohort of 548,232 adults, 6 CPs produced PPVs ranging from 71% (95% CI 64.3%-76.9%) to 95.7% (95% CI 84.9%-98.9%). The F1-score ranged from 0.40 to 0.91. The prevalence-adjusted bias-adjusted κ revealed a high percentage agreement of 0.88 for hypertension. Similarly, interrater agreement for individual phenotype determination demonstrated substantial agreement (range 0.70-0.88) for all 6 phenotypes examined. A phenotype based solely on diagnostic codes possessed reasonable performance (F1-score=0.63; PPV=95.1%) but was imbalanced with low sensitivity (47.6%). The most balanced phenotype (F1-score=0.91; PPV=83.5%) included diagnosis, blood pressure measurements, and medications and identified 210,764 (38.4%) individuals with hypertension during the study period (2014-2015).ConclusionsWe identified several high-performing phenotypes to identify essential hypertension prevalence for local public health surveillance using EHR data. Given the increasing availability of EHR systems in the United States and other nations, leveraging EHR data has the potential to enhance surveillance of chronic disease in health systems and communities. Yet given variability in performance, public health authorities will need to decide whether to seek optimal balance or declare a preference for algorithms that lean toward sensitivity or specificity to estimate population prevalence of disease.

Project description:BackgroundCohort studies identifying the incidence, complications and co-morbidities associated with community acquired pneumonia (CAP) are largely based on administrative datasets and rely on International Classification of Diseases (ICD) codes; however, the reliability of ICD codes for hospital admissions for CAP in people with HIV (PWH) has not been systematically assessed.MethodsWe used data from the Veterans Aging Cohort Study survey sample (N = 6824; 3410 PWH and 3414 uninfected) to validate the use of electronic health records (EHR) data to identify CAP hospitalizations when compared to chart review and to compare the performance in PWH vs. uninfected patients. We used different EHR algorithms that included a broad set of CAP ICD-9 codes, a set restricted to bacterial and viral CAP codes, and algorithms that included pharmacy data and/or other ICD-9 diagnoses frequently associated with CAP. We also compared microbiologic workup and etiologic diagnosis by HIV status among those with CAP.ResultsFive hundred forty-nine patients were identified as having an ICD-9 code compatible with a CAP diagnosis (13% of PWH and 4% of the uninfected, p < 0.01). The EHR algorithm with the best overall positive predictive value (82%) was obtained by using the restricted set of ICD-9 codes (480-487) in primary position or secondary only to selected codes as primary (HIV disease, respiratory failure, sepsis or bacteremia) with the addition of EHR pharmacy data; this algorithm yielded PPVs of 83% in PWH and 73% in uninfected (P = 0.1) groups. Adding aspiration pneumonia (ICD-9 code 507) to any of the ICD-9 code/pharmacy combinations increased the number of cases but decreased the overall PPV. Allowing COPD exacerbation in the primary position improved the PPV among the uninfected group only (to 76%). More PWH than uninfected patients underwent microbiologic evaluation or had respiratory samples submitted.ConclusionsICD-9 code-based algorithms perform similarly to identify CAP in PLWH and uninfected individuals. Adding antimicrobial use data and allowing as primary diagnoses ICD-9 codes frequently used in patients with CAP improved the performance of the algorithms in both groups of patients. The algorithms consistently performed better among PWH.

Dataset Information

Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.

Background

Methods

Results

Conclusion

Publications

Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.

Similar Datasets

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