Evidence map›Paper›PMID 37546792›Full record

ArticlemedRxiv : the preprint server for health sciences2023

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 readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.
Ellen C KeeleyDivision of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL.
Eric BarretteGlobal Health Economics & Outcomes Research, Medtronic, Inc.
Rhonda M Cooper-DeHoffDivision of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL.
Sanket S DhruvaSchool of Medicine, University of California San Francisco, CA.
Jenny GaffneyGlobal Reimbursement & Health Economics, Coronary & Renal Denervation, Medtronic, Inc.
Ginger GambleCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.
Bonnie HandkeGlobal Health Economics & Outcomes Research, Medtronic, Inc.
Chenxi HuangCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.
Harlan M KrumholzCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.
Caitrin W McDonough RoweDepartment of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL.
Wade SchulzCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.
Kathryn ShawDepartment of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL.
Myra SmithDepartment of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL.
Jennifer WoodardDepartment of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL.
Patrick YoungCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.
Keondae ErvinNational Evaluation System for health Technology Coordinating Center (NESTcc), Medical Device Innovation Consortium, Arlington, VA, USA.
Joseph S RossCenter for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.

Funding

NESTcc Renewal -Continuation of the National Evaluation System for Health Technology (NEST) Coordinating Center (CC)U01FD006292 · FDA · MEDICAL DEVICE INNOVATION CONSORTIUM · PI SMITH, RICHARD · 2018 to 2025
$76.1M
Yale-Mayo FDA Center for Excellence in Regulatory Science and InnovationU01FD005938 · FDA · YALE UNIVERSITY · PI Joseph Solomon Ross · 2016 to 2026
$25.4M
Yale Scholars in Implementation Science (YSIS)K12HL138037 · NHLBI · YALE UNIVERSITY · PI SHARIFI, MAHNOOSH · 2017 to 2021
$3.0M
UCSF Career Development Program in Implementation Research in Heart and Lung DiseasesK12HL138046 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BIBBINS-DOMINGO, KIRSTEN, CATTAMANCHI, ADITHYA · 2017 to 2021
$2.9M
Understanding hospital value: provider, hospital and community effectsR01HS022882 · AHRQ · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI HORWITZ, LEORA · 2014 to 2023
$2.9M
Leveraging Physician Networks to Improve Care and Outcomes for Low-Income PopulationsR01HL144644 · NHLBI · YALE UNIVERSITY · PI NDUMELE, CHIMA · 2019 to 2021
$1.6M
Medical reversals: De-implementing ineffective and unsafe treatmentsR01HS025164 · AHRQ · UNIVERSITY OF MINNESOTA · PI KARACA-MANDIC, PINAR · 2017 to 2020
$1.4M
AHRQ HHS R01 HS022882AHRQ HHS R01 HS025164FDA HHS U01 FD005938FDA HHS U01 FD006292NHLBI NIH HHS K12 HL138037NHLBI NIH HHS K12 HL138046NHLBI NIH HHS R01 HL144644
6 · The paper itself

Abstract

Background: Improving hypertension control is a public health priority. However, uncertainty remains regarding the optimal way to identify patients with uncontrolled hypertension using electronic health records (EHR) data. Methods: In this retrospective cohort study, we applied 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 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 blood pressure (BP) measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit). Results: Overall, 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). 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 rates of short-term (at 3 and 6 months) and long-term (at 12 and 24 months) clinical outcomes. The two computable definitions generated consistent results. Conclusions: Computable definitions can be successfully applied to health system EHR data to conduct population surveillance for hypertension and identify patients with uncontrolled hypertension who may benefit from additional treatment.

Indexed as

Blood pressurecomputable algorithmelectronic health recordshypertension

Identifiers

PMID37546792
PMCPMC10402222

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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.