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Development and Validation of a Pragmatic Electronic Phenotype for CKD.

ABSTRACT: BACKGROUND AND OBJECTIVES:Poor identification of individuals with CKD is a major barrier to research and appropriate clinical management of the disease. We aimed to develop and validate a pragmatic electronic (e-) phenotype to identify patients likely to have CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:The e-phenotype was developed by an expert working group and implemented among adults receiving in- or outpatient care at five healthcare organizations. To determine urine albumin (UA) dipstick cutoffs for CKD to enable use in the e-phenotype when lacking urine albumin-to-creatinine ratio (UACR), we compared same day UACR and UA results at four sites. A sample of patients, spanning no CKD to ESKD, was randomly selected at four sites for validation via blinded chart review. RESULTS:The CKD e-phenotype was defined as most recent eGFR <60 ml/min per 1.73 m2 with at least one value <60 ml/min per 1.73 m2 >90 days prior and/or a UACR of ?30 mg/g in the most recent test with at least one positive value >90 days prior. Dialysis and transplant were identified using diagnosis codes. In absence of UACR, a sensitive CKD definition would consider negative UA results as normal to mildly increased (KDIGO A1), trace to 1+ as moderately increased (KDIGO A2), and ?2+ as severely increased (KDIGO A3). Sensitivity, specificity, and diagnostic accuracy of the CKD e-phenotype were 99%, 99%, and 98%, respectively. For dialysis sensitivity was 94% and specificity was 89%. For transplant, sensitivity was 97% and specificity was 91%. CONCLUSIONS:The CKD e-phenotype provides a pragmatic and accurate method for EHR-based identification of patients likely to have CKD.

SUBMITTER: Norton JM

PROVIDER: S-EPMC6730512 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Development and Validation of a Pragmatic Electronic Phenotype for CKD.

Norton Jenna M JM Ali Kaltun K Jurkovitz Claudine T CT Kiryluk Krzysztof K Park Meyeon M Kawamoto Kensaku K Shang Ning N Navaneethan Sankar D SD Narva Andrew S AS Drawz Paul P

Clinical journal of the American Society of Nephrology : CJASN 20190812 9

<h4>Background and objectives</h4>Poor identification of individuals with CKD is a major barrier to research and appropriate clinical management of the disease. We aimed to develop and validate a pragmatic electronic (e-) phenotype to identify patients likely to have CKD.<h4>Design, setting, participants, & measurements</h4>The e-phenotype was developed by an expert working group and implemented among adults receiving in- or outpatient care at five healthcare organizations. To determine urine al ...[more]

PMID: 31405830

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Project description:Rationale & objectiveMost adults with chronic kidney disease (CKD) in the United States are cared for by primary care providers (PCPs). We evaluated the feasibility and preliminary effectiveness of an electronic clinical decision support system (eCDSS) within the electronic health record with or without pharmacist follow-up to improve the management of CKD in primary care.Study designPragmatic cluster-randomized trial.Setting & participants524 adults with confirmed creatinine-based estimated glomerular filtration rates of 30 to 59mL/min/1.73m2 cared for by 80 PCPs at the University of California San Francisco. Electronic health record data were used for patient identification, intervention deployment, and outcomes ascertainment.InterventionsEach PCP's eligible patients were randomly assigned as a group into 1 of 3 treatment arms: (1) usual care; (2) eCDSS: testing of creatinine, cystatin C, and urinary albumin-creatinine ratio with individually tailored guidance for PCPs on blood pressure, potassium, and proteinuria management, cardiovascular risk reduction, and patient education; or (3) eCDSS plus pharmacist counseling (eCDSS-PLUS).OutcomesThe primary clinical outcome was change in blood pressure over 12 months. Secondary outcomes were PCP awareness of CKD and use of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker and statin therapy.ResultsAll 80 eligible PCPs participated. Mean patient age was 70 years, 47% were nonwhite, and mean estimated glomerular filtration rate was 56±0.6mL/min/1.73m2. Among patients receiving eCDSS with or without pharmacist counseling (n=336), 178 (53%) completed laboratory measurements and 138 (41%) had laboratory measurements followed by a PCP visit with eCDSS deployment. eCDSS was opened by the PCP for 102 (74%) patients, with at least 1 suggested order signed for 83 of these 102 (81%). Changes in systolic blood pressure were-2.1±1.5mm Hg with usual care, -2.8±1.8mm Hg with eCDSS, and -1.1±1.1 with eCDSS-PLUS (P=0.7). PCP awareness of CKD was 16% with usual care, 26% with eCDSS, and 32% for eCDSS-PLUS (P=0.09). In as-treated analyses, PCP awareness of CKD was significantly greater with eCDSS and eCDSS-PLUS (73% and 69%) versus usual care (47%; P=0.002).LimitationsRecruitment of smaller than intended sample size and limited uptake of the testing component of the intervention.ConclusionsAlthough we were unable to demonstrate the effectiveness of eCDSS to lower blood pressure and uptake of the eCDSS was limited by low testing rates, eCDSS use was high when laboratory measurements were available and was associated with higher PCP awareness of CKD.FundingGrants from government (National Institutes of Health) and not-for-profit (American Heart Association) entities.Trial registrationRegistered at ClinicalTrials.gov with study number NCT02925962.

Project description:BackgroundElectronic 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.MethodsOur 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.ResultsAmong 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.ConclusionBy 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.

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.

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Development and Validation of a Pragmatic Electronic Phenotype for CKD.

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Development and Validation of a Pragmatic Electronic Phenotype for CKD.

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