ArticleBMJ open2021
Multicomponent intervention to improve blood pressure management in chronic kidney disease: a protocol for a pragmatic clinical trial.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03679247 (Electronic Tools to Increase Recognition and Improve Primary Care Management for Hypertension in Chronic Kidney Disease), which is not on this map. Cited by 4 papers.
What it found
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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.
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.
Electronic Tools to Increase Recognition and Improve Primary Care Management for Hypertension in Chronic Kidney Disease
Who cites it
4 citing papers in PubMed.
- User Actions within a Clinical Decision Support Alert for the Management of Hypertension in Chronic Kidney Disease.Applied clinical informatics · 2025Trial
- Pragmatic Clinical Decision Support Trial of Pre-checked Default Orders: Monitoring Safety and Adverse Clinical Outcomes.Journal of general internal medicine · 2025Article
- Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study.Applied clinical informatics · 2023Article
- Human-centered design of clinical decision support for management of hypertension with chronic kidney disease.BMC medical informatics and decision making · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
introductionThe purpose of this study is to incorporate behavioural economic principles and user-centred design principles into a multicomponent intervention for the management of uncontrolled hypertension (HTN) in chronic kidney disease (CKD) in primary care. METHODS AND ANALYSIS: This is a multicentre, pragmatic, controlled trial cluster-randomised at the clinician level at The Brigham and Women's Practice -Based Research Network of 15 practices. Of 220 total clinicians, 184 were eligible to be enrolled, and the remainder were excluded (residents and clinicians who see urgent care or walk-in patients); no clinicians opted out. The intervention consists of a clinical decision support system based in behavioural economic and user-centred design principles that will: (1) synthesise existing laboratory tests, medication orders and vital sign data; (2) increase recognition of CKD, (3) increase recognition of uncontrolled HTN in CKD patients and (4) deliver evidence-based CKD and HTN management recommendations. The primary endpoint is the change in mean systolic blood pressure between baseline and 6 months compared across arms. We will use the Reach Effectiveness Adoption Implementation Maintenance framework. At the conclusion of this study, we will have: (1) validated an intervention that combines laboratory tests, medication records and clinical information collected by electronic health records to recognise uncontrolled HTN in CKD patients and recommend a course of care, (2) tested the effectiveness of said intervention and (3) collected information about the implementation of the intervention that will aid in dissemination of the intervention to other practice settings. ETHICS AND DISSEMINATION: The Human Subjects Institutional Review Board at Brigham and Women's Hospital provided an expedited review and approval for this study protocol, and a Data Safety Monitoring Board will ensure the ongoing safety of the trial. TRIAL REGISTRATION NUMBER: NCT03679247.
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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.