Optimizing identification of resistant hypertension: Computable phenotype development and validation

McDonough, Caitrin W.; Babcock, Kyle; Chucri, Kristen; Crawford, Dana C.; Bian, Jiang; Modave, François; Cooper‐DeHoff, Rhonda M.; Hogan, William R.

doi:10.1002/pds.5095

Cited by 15 publications

(17 citation statements)

References 54 publications

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“…In contrast, most other CKD validation studies defined CKD as KDIGO stage 3a or higher (eGFR < 60 ml/min/1.73m2) -using this common definition of CKD, our algorithm had a sensitivity of 93% and specificity of 97%, and was comparable to existing studies with sensitivities ranging from 93 to 100% and specificities ranging from 0 to 99% [8,10,11]. Our algorithm sensitivity and specificity for diabetes [31][32][33], hypertension [34,35], and cardiovascular disease [36,37] also have comparable accuracy to that of previously published studies.…”

Section: Discussionsupporting

confidence: 51%

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

et al. 2022

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Background Electronic health records can be used for population-wide identification and monitoring of disease. The Territory Kidney Care project developed algorithms to identify individuals with chronic kidney disease (CKD) and several commonly comorbid chronic diseases. This study aims to describe the development and validation of our algorithms for CKD, diabetes, hypertension, and cardiovascular disease. A secondary aim of the study was to describe data completeness of the Territory Kidney Care database. Methods The Territory Kidney Care database consolidates electronic health records from multiple health services including public hospitals (n = 6) and primary care health services (> 60) across the Northern Territory, Australia. Using the database (n = 48,569) we selected a stratified random sample of patients (n = 288), which included individuals with mild to end-stage CKD. Diagnostic accuracy of the algorithms was tested against blinded manual chart reviews. Data completeness of the database was also described. Results For CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR < 60 ml/min/1.732, including renal replacement therapy) overall algorithm sensitivity was 93% (95%CI 89 to 96%) and specificity was 73% (95%CI 64 to 82%). For CKD defined as CKD stage 3a or higher (eGFR < 60 ml/min/1.732) algorithm sensitivity and specificity were 93% and 97% respectively. Among the CKD 1 to 5 staging algorithms, the CKD stage 5 algorithm was most accurate with > 99% sensitivity and specificity. For related comorbidities – algorithm sensitivity and specificity results were 75% and 97% for diabetes; 85% and 88% for hypertension; and 79% and 96% for cardiovascular disease. Conclusions We developed and validated algorithms to identify CKD and related chronic diseases within electronic health records. Validation results showed that CKD algorithms have a high degree of diagnostic accuracy compared to traditional administrative codes. Our highly accurate algorithms present new opportunities in early kidney disease detection, monitoring, and epidemiological research.

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Section: Discussionsupporting

confidence: 51%

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

et al. 2022

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“…We used our previously validated computable phenotype algorithms to classify patients as aTRH, stable controlled HTN, or other HTN in the OneFlorida and REACHnet datasets [18]. Briefly, adult patients (≥18 years) with an outpatient HTN diagnosis, prescription records, and BP measurements during the study period were included in the data preparation steps.…”

Section: Atrh and Stable Controlled Htn Computable Phenotype Algorithmsmentioning

confidence: 99%

“…Hence, leveraging our validated aTRH computable phenotype algorithm [18], we aimed to identify characteristics and predictors of aTRH within electronic health records (EHRs)-based data of large, racially and ethnically diverse populations from the OneFlorida Data Trust and the Research Action for Health Network (REACHnet). We used OneFlorida as a discovery population and REACHnet as a validation cohort.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Predictors of Apparent Treatment Resistant Hypertension in Real-World Populations Using Electronic Health Record-Based Data

Jafari

Cooper‐DeHoff

Effron

et al. 2023

Preprint

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Background: Apparent treatment-resistant hypertension (aTRH) is defined as uncontrolled blood pressure (BP) despite using ≥3 antihypertensive classes or controlled BP while using ≥4 antihypertensive classes. Patients with aTRH have a higher risk for adverse cardiovascular outcomes compared to patients with controlled hypertension. Although there have been prior reports on the prevalence, characteristics, and predictors of aTRH, these have been broadly derived from smaller datasets, randomized controlled trials, or closed healthcare systems. Methods: We extracted patients with hypertension defined by ICD 9 and 10 codes during 1/1/2015-12/31/2018, from two large electronic health record databases: the OneFlorida Data Trust (n=223,384) and Research Action for Health Network (REACHnet) (n=175,229). We applied our previously validated aTRH and stable controlled hypertension (HTN) computable phenotype algorithms and performed univariate and multivariate analyses to identify the prevalence, characteristics, and predictors of aTRH in these real-world populations. Results: The prevalence of aTRH in OneFlorida (16.7%) and REACHnet (11.3%) was similar to prior reports. Both populations had a significantly higher proportion of black patients with aTRH compared to those with stable controlled HTN. aTRH in both populations shared similar significant predictors, including black race, diabetes, heart failure, chronic kidney disease, cardiomegaly, and higher body mass index. In both populations, aTRH was significantly associated with similar comorbidities, when compared with stable controlled HTN. Conclusion: In two large, diverse real-world populations, we observed similar comorbidities and predictors of aTRH as prior studies. In the future, these results may be used to improve healthcare professionals' understanding of aTRH predictors and associated comorbidities.

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“…In this case, researchers may develop computable phenotypes that combine various datatypes (eg, diagnosis codes, text mentions, medications) to increase the specificity and/or sensitivity of the phenotype. 32,33 Ideally, a computable phenotype should be designed with input from both data scientists and clinical experts and within the context of a specific study. A clinical domain expert understands the diagnostic, laboratory, and pharmacological markers for a disease and the specific characteristics that distinguish similar conditions.…”

Section: Computable Phenotypes and Phenotyping Strategiesmentioning

confidence: 99%

Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets

Bastarache

Brown

Cimino

et al. 2021

Learning Health Systems

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Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

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Optimizing identification of resistant hypertension: Computable phenotype development and validation

Cited by 15 publications

References 54 publications

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

Characteristics and Predictors of Apparent Treatment Resistant Hypertension in Real-World Populations Using Electronic Health Record-Based Data

Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets

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