Evidence map›Paper›PMID 39886769›Full record

ArticleHypertension (Dallas, Tex. : 1979)2025

The Long-Term Blood Pressure Trends Following a Remote Hypertension Intervention: A Secondary Analysis of the Digital Care Transformation - Remotely Delivered Hypertension Management Program.

Shahzad Hassan, Alexander J Blood, David Zelle, Sanjay Kumar, Kavishwar Wagholikar, Daniel Gabovitch, Christopher P Cannon, Naomi Fisher, Benjamin M Scirica

Abstract read
In one paragraph

Article in Hypertension (Dallas, Tex. : 1979), 2025. 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
  2. Article
  3. Article
  4. Review
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

9 authors.

Shahzad HassanAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0001-7903-0226
Alexander J BloodAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0002-8391-2536
David ZelleAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0001-8193-5067
Sanjay KumarAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0009-0009-2078-2322
Kavishwar WagholikarAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0002-6219-861X
Daniel GabovitchAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0009-0007-5854-4392
Christopher P CannonAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0003-4596-2791
Naomi FisherDivision of Endocrinology, Diabetes and Hypertension (N.F.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0003-1649-1441
Benjamin M SciricaAccelerator for Clinical Transformation (S.H., A.J.B., D.Z., S.K., K.W., D.G., C.P.C., B.M.S.), Brigham and Women's Hospital, Boston, MA.ORCID 0000-0002-7093-7048

Funding

TRAINING PROGRAM IN CARDIOVASCULAR RESEARCHT32HL007604 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MARK W FEINBERG · 1985 to 2026
$19.0M
A framework to stratify patient cohorts for clinical managementR01HL151643 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI WAGHOLIKAR, KAVISHWAR B. · 2020 to 2024
$4.1M
NHLBI NIH HHS R01 HL151643NHLBI NIH HHS T32 HL007604
6 · The paper itself

Abstract

backgroundHypertension is a major cardiovascular risk factor, yet traditional care often results in suboptimal blood pressure (BP) control at the population level. We implemented a remote hypertension management program that monitored home BP and titrated medications per algorithm. This study assessed the program's long-term effects by examining participants' office BP up to 42 months post-enrollment.

methodsParticipants of the remote hypertension program were categorized into 4 groups: (1) enrolled-maintenance (achieved goal home BP of ≤130/80 mm Hg), (2) enrolled-early exit (left before achieving goal BP), (3) education-only (lifestyle modifications and medications compliance), and (4) white coat hypertension group (high office BP but normal home BP). Office BP readings of participants were collected up to 42 months post-enrollment. A linear mixed-effects regression model estimated mean BP levels and studied factors associated with above-goal systolic BP in the maintenance group.

resultsOffice BP readings from 3601 participants (mean age, 61±11 years; 57% female; 60% white; 52% atherosclerotic cardiovascular disease) were extracted from electronic health records and analyzed. All groups sustained office BP below their qualifying values (

conclusionsParticipants who achieved BP control through the remote hypertension program maintained goal systolic BP in 90% of cases up to 42 months post-enrollment. These findings highlight the long-term benefits of remote, intensive management programs for effective hypertension control.

Indexed as

Antihypertensive AgentsBlood PressureHypertensionAgedBlood Pressure DeterminationBlood Pressure Monitoring, AmbulatoryFemaleFollow-Up StudiesHumansMaleMiddle AgedTelemedicineTime FactorsWhite Coat HypertensionAntihypertensive Agentsblood pressurecardiovascular diseaseselectronic health recordshypertensionrisk factors

Identifiers

PMID39886769
PMCPMC12068792

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.