Evidence map›Paper›PMID 32729919›Full record

ArticleJAMA network open2020

Validation of a Health System Measure to Capture Intensive Medication Treatment of Hypertension in the Veterans Health Administration.

Lillian Min, Jin-Kyung Ha, Timothy P Hofer, Jeremy Sussman, Kenneth Langa, William C Cushman, Mary Tinetti, Hyungjin Myra Kim, Matthew L Maciejewski, Leah Gillon and 3 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 26% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 1 country.

Lillian MinVA Ann Arbor Medical Center, Geriatric Research, Education, and Clinical Center, Ann Arbor, Michigan.
Jin-Kyung HaDivision of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor.
Timothy P HoferVA Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan.
Jeremy SussmanVA Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan.
Kenneth LangaVA Ann Arbor Medical Center, Geriatric Research, Education, and Clinical Center, Ann Arbor, Michigan.
William C CushmanPreventive Medicine Section, Memphis VA Medical Center, Memphis, Tennessee.
Mary TinettiDepartment of Medicine, Section of Geriatrics, Yale School of Medicine, New Haven, Connecticut.
Hyungjin Myra KimConsulting for Statistics, Computing and Analytics Research, University of Michigan, Ann Arbor.
Matthew L MaciejewskiAccelerate Discovery and Practice Transformation Center of Innovation, VA Health Care System, Durham, North Carolina.
Leah GillonVA Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan.
Angela LarkinVA Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan.
Chiao-Li ChanDivision of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor.
Eve KerrVA Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan.
University of Michigan–Ann Arbor · USVA Center for Clinical Management Research · USHealth Services Research & Development · USDuke University · USGeriatric Research Education and Clinical Center · USUniversity of Tennessee Health Science Center · USYale University · US

Funding

Research Education CoreP30AG024824 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Neil Alexander · 2004 to 2026
$29.2M
Physician Scientist Training in Age-Related DiseasesT32AG062403 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MIN, LILLIAN CHIANG, MODY, LONA · 2020 to 2025
$2.7M
Optimizing Treatment of Older Adults with Hypertension: A Net Benefit Analysis of Falls Injury vs Cardiovascular OutcomesR01AG047178 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MIN, LILLIAN CHIANG · 2015 to 2019
$1.7M
Assessing Hypertension Care for Aged Veterans: Balancing Risks and BenefitsI01HX001611 · VA · VETERANS HEALTH ADMINISTRATION · PI MIN, LILLIAN CHIANG · 2020 to 2020
–
HSRD VA I01 HX001611NIA NIH HHS P30 AG024824NIA NIH HHS R01 AG047178NIA NIH HHS T32 AG062403
6 · The paper itself

Abstract

Importance: Blood pressure (BP) targets are the main measure of high-quality hypertension care in health systems. However, BP alone does not reflect intensity of pharmacological treatment, which should be carefully managed in older patients. Objectives: To develop and validate an electronic health record (EHR) data-only algorithm using pharmacy and BP data to capture intensive hypertension care (IHC), defined as 3 or more BP medications and BP less than 120 mm Hg, and to identify conditions associated with greater IHC, either through greater algorithm false-positive IHC, or by contributing clinically to delivering more IHC. Design, Setting, and Participants: This cross-sectional study was conducted among 319 randomly selected patients aged 65 years or older receiving IHC from the Veterans Health Administration (VHA) from July 1, 2011, to June 30, 2013. Data were collected from a total of 3625 primary care visits. Data were analyzed from January 2017 to March 2020. Exposures: Calibration and measurement of the algorithm for IHC (algorithm IHC). Main Outcomes and Measures: For each primary care visit, the reference standard, clinical IHC, was determined by detailed review of free-text clinical notes. The correlation in BP medication count between the EHR-only algorithm vs the reference standard and the sensitivity and specificity of the algorithm IHC were calculated. In addition, presence vs absence of contributing conditions acting in combination with hypertension management were measured to examine incidence of IHC associated with contributing conditions, including an acute condition that lowered BP (eg, dehydration), another condition requiring a BP target lower than the standard 140 mm Hg (eg, diabetes), or the patient needing a BP-lowering medication for a nonhypertension condition (eg, β-blocker for atrial fibrillation) resulting in low BP. Results: Among 319 patients with 3625 visits (mean [SD] age, 75.6 [7.2] years; 3592 [99.1%] men), 911 visits (25.1%) had clinical IHC by the reference standard. The algorithm for determining medication count was highly correlated with the reference standard (r = 0.84). Sensitivity of detecting clinical IHC was 92.2% (95% CI, 89.3%-95.1%), and specificity was 97.2% (95% CI, 96.1%-98.3%), suggesting that clinical IHC can be identified from routinely collected data. Only 75 visits (2.1%) were algorithm IHC false positives, 55 visits (1.5%) involved IHC with contributing conditions, and 125 visits (3.5%) involved either false-positive or IHC with contributing conditions. Among select contributing conditions, congestive heart failure (37 patients [5.2%]) was most associated with a prespecified combined false-positive or IHC with contributing conditions rate higher than 5%. Conclusions and Relevance: These findings suggest that health system data can be used reliably to estimate IHC.

Indexed as

AgedAged, 80 and overAntihypertensive AgentsBlood Pressure DeterminationCross-Sectional StudiesElectronic Health RecordsFemaleHumansHypertensionMaleMedication Therapy ManagementQuality Indicators, Health CareReproducibility of ResultsVeterans HealthAntihypertensive Agents

Identifiers

PMID32729919
PMCPMC9374172
OpenAlexW3046077708

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.