Evidence map›Paper›PMID 39289597›Full record

ArticleBMC cardiovascular disorders2024

Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks.

Yuan Lu, Ellen C Keeley, Eric Barrette, Rhonda M Cooper-DeHoff, Sanket S Dhruva, Jenny Gaffney, Ginger Gamble, Bonnie Handke, Chenxi Huang, Harlan M Krumholz and 8 more

Abstract readMulticenter StudyComparative Study
In one paragraph

Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Yuan LuCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, 06510, USA. y.lu@yale.edu.
Ellen C KeeleyDivision of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.
Eric BarretteGlobal Health Economics & Outcomes Research, Medtronic, Inc, Washington DC, USA.
Rhonda M Cooper-DeHoffDivision of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.
Sanket S DhruvaSchool of Medicine, University of California San Francisco, San Francisco, CA, USA.
Jenny GaffneyHealth Economics, Reimbursement and Policy, Medtronic, Inc, Minneapolis, USA.
Ginger GambleCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, 06510, USA.
Bonnie HandkeHealth Economics, Reimbursement and Policy, Medtronic, Inc, Minneapolis, USA.
Chenxi HuangCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, 06510, USA.
Harlan M KrumholzCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, 06510, USA.
Caitrin W McDonoughDepartment of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Wade SchulzCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, 06510, USA.
Kathryn ShawDepartment of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
Myra SmithDepartment of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
Jennifer WoodardDepartment of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
Patrick YoungDepartment of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
Keondae ErvinMedical Device Innovation Consortium, National Evaluation System for Health Technology Coordinating Center (NESTcc), Arlington, VA, USA.
Joseph S RossCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, 06510, USA. joseph.ross@yale.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundImproving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain.

methodsIn this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension (based on either ICD-10 codes of hypertension or two elevated blood pressure [BP] measurements) receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single BP measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit).

resultsOverall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). A total of 24.1% of patients at YNHHS and 21.6% at OneFlorida had both diagnosis code for hypertension and elevated blood pressure measurements. Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar incidence rates of deaths, CVD events, and healthcare visits at 3, 6, 12, and 24 months. The two computable definitions generated consistent results.

conclusionsWhile the current EHR systems are not fully optimized for disease surveillance and stratification, our findings illustrate the potential of leveraging EHR data to conduct digital population surveillance in the realm of hypertension management.

Indexed as

Antihypertensive AgentsBlood PressureElectronic Health RecordsHypertensionAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesTime FactorsTreatment OutcomeUnited StatesAntihypertensive AgentsBlood pressureComputable algorithmElectronic health recordsHypertension

Identifiers

PMID39289597
PMCPMC11409735

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.