Evidence map›Paper›PMID 31163981›Full record

ArticleCirculation. Cardiovascular quality and outcomes2019

Clinic-Based Strategies to Reach United States Million Hearts 2022 Blood Pressure Control Goals.

Brandon K Bellows, Natalia Ruiz-Negrón, Kirsten Bibbins-Domingo, Jordan B King, Mark J Pletcher, Andrew E Moran, Valy Fontil

Registry-linked trialOpen access · bronzeAbstract readValidation Study
In one paragraph

Article in Circulation. Cardiovascular quality and outcomes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05347823 (Study of Adherence to Antihypertensive Drugs and Statins in the Population of Subjects With Hypertension Followed at SIIA Centers, Using an Innovative Monitoring Based on Hair Analysis), which is not on this map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 4% 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.

NCT05347823 unknown statusnot on this mapstarted 2022, after this paper: background citation

Study of Adherence to Antihypertensive Drugs and Statins in the Population of Subjects With Hypertension Followed at SIIA Centers, Using an Innovative Monitoring Based on Hair Analysis

TypeobservationalSponsorAzienda Ospedaliera Universitaria Integrata VeronaRan2022 to 2023Enrolled200ConditionsArterial Hypertension, Dyslipidemias, Antihypertensive Drugs, StatinsArmsHair analysis for drug concentration
3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.

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  17. Managing Hypertension in Older Adults.Current hypertension reports · 2024
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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

7 authors at 5 institutions in 2 countries.

Brandon K BellowsColumbia University, Division of General Medicine, New York, NY (B.K.B., A.E.M.).
Natalia Ruiz-NegrónUniversity of Utah, Department of Pharmacotherapy, Salt Lake City, UT (N.R.-N.).
Kirsten Bibbins-DomingoDepartment of Epidemiology and Biostatistics, University of California at San Francisco (K.B.-D., M.J.P.).
Jordan B KingDepartment of Population Health Sciences, University of Utah, Salt Lake City, UT (J.B.K.).
Mark J PletcherDepartment of Epidemiology and Biostatistics, University of California at San Francisco (K.B.-D., M.J.P.).
Andrew E MoranColumbia University, Division of General Medicine, New York, NY (B.K.B., A.E.M.).
Valy FontilDivision of General Medicine, University of California at San Francisco (V.F.).
Columbia University · USUniversity of California, San Francisco · USMurray State University · USSan Francisco General Hospital · USUniversity of Utah · US

Funding

Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
The Discovering Healthcare Innovations to Address Disparties in Stroke (DIADS) prU54NS081760 · NINDS · KAISER FOUNDATION RESEARCH INSTITUTE · PI BIBBINS-DOMINGO, KIRSTEN, SIDNEY, STEPHEN · 2012 to 2016
$10.5M
Patient Level Prediction of Clinical Outcomes and Cost-Effectiveness in SPRINT (Optimize-SPRINT)R01HL139837 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BRESS, ADAM P, MORAN, ANDREW EDWARD · 2018 to 2021
$3.2M
Model Based Approach to Improving Hypertension Control in PopulationsR01HL130500 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MORAN, ANDREW EDWARD · 2016 to 2019
$2.4M
Cigarette Smoke and Susceptibility to Influenza InfectionR01HL095163 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JASPERS, ILONA, NOAH, TERRY L · 2009 to 2013
$2.0M
Adjunctive Use of Apyrase to Fibrinolytic TherapyR44HL095169 · NHLBI · APT THERAPEUTICS, INC. · PI CHEN, RIDONG · 2012 to 2013
$1.3M
Hypertension Management in Minority and Low-income PopulationsK24DK103992 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BIBBINS-DOMINGO, KIRSTEN · 2014 to 2018
$917k
Bring it Down San Francisco: Harnessing cloud-based technology to improve hypertension management in a low-income underserved populationK23HL136899 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FONTIL, VALY · 2018 to 2022
$887k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDYN01HC095166 · HC · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI TRACY, RUSSELL P · 1999 to 2001
$758k
Comparing the value of intensive systolic blood pressure treatment implementation strategiesK01HL140170 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BELLOWS, BRANDON K · 2018 to 2022
$709k
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NHLBI NIH HHS HHSN268201500003CNHLBI NIH HHS HHSN268201500003INHLBI NIH HHS K01 HL140170NHLBI NIH HHS K23 HL136899NHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL130500NHLBI NIH HHS R01 HL139837NIDDK NIH HHS K24 DK103992NINDS NIH HHS U54 NS081760
6 · The paper itself

Abstract

backgroundThe Centers for Disease Control and Prevention's Million Hearts initiative includes an ambitious ≥80% blood pressure control goal in US adults with hypertension by 2022. We used the validated Blood Pressure Control Model to quantify changes in clinic-based hypertension management processes needed to attain ≥80% blood pressure control. METHODS AND

resultsThe Blood Pressure Control Model simulates patient blood pressures weekly using 3 key modifiable hypertension management processes: office visit frequency, clinician treatment intensification given uncontrolled blood pressure, and continued antihypertensive medication use (medication adherence rate). We compared blood pressure control rates (using the Seventh Joint National Committee on hypertension targets) achieved over 4 years between usual care and the best-observed values for management processes identified from the literature (1-week return visit interval, 20%-44% intensification rate, and 76% adherence rate). We determined the management process values needed to achieve ≥80% blood pressure control in US adults. In adults with uncontrolled blood pressure, usual care achieved 45.6% control (95% uncertainty interval, 39.6%-52.5%) and literature-based best-observed values achieved 79.7% control (95% uncertainty interval, 79.3%-80.1%) over 4 years. Increasing treatment intensification rates to 62% of office visits with an uncontrolled blood pressure resulted in ≥80% blood pressure control, even when the return visit interval and adherence remained at usual care values. Improving to best-observed values for all 3 management processes would achieve 78.1% blood pressure control in the overall US population with hypertension, approaching the ≥80% Million Hearts 2022 goal.

conclusionsAchieving the Million Hearts blood pressure control goal by 2022 will require simultaneously increasing visit frequency, overcoming therapeutic inertia, and improving patient medication adherence. As the relative importance of each of these 3 processes will depend on local characteristics, simulation models like the Blood Pressure Control Model can help local healthcare systems tailor strategies to reach local and national benchmarks.

Indexed as

Computer SimulationAntihypertensive AgentsBenchmarkingBlood PressureCenters for Disease Control and Prevention, U.S.Drug ResistanceDrug Therapy, CombinationHumansHypertensionMedication AdherenceOffice VisitsQuality ImprovementQuality Indicators, Health CareReproducibility of ResultsTime FactorsTreatment OutcomeAntihypertensive Agentsadultsblood pressurecardiovascular diseasehypertensionmedication adherence

Identifiers

PMID31163981
PMCPMC6768426
OpenAlexW2956105944

What OpenQuestion holds

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Registered trials

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.