Evidence map›Paper›PMID 39899838›Full record

Trial reportJMIR formative research2025

Effectiveness of Electronic Quality Improvement Activities to Reduce Cardiovascular Disease Risk in People With Chronic Kidney Disease in General Practice: Cluster Randomized Trial With Active Control.

Jo-Anne Manski-Nankervis, Barbara Hunter, Natalie Lumsden, Adrian Laughlin, Rita McMorrow, Douglas Boyle, Patty Chondros, Shilpanjali Jesudason, Jan Radford, Megan Prictor and 4 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR formative research, 2025. 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

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

14 authors.

Jo-Anne Manski-NankervisPrimary Care and Family Medicine, Lee Kong Chian School of Medicine, Singapore, Singapore.ORCID 0000-0003-2153-3482
Barbara HunterDepartment of General Practice and Primary Care, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-1268-3166
Natalie LumsdenDepartment of General Practice and Primary Care, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-7471-2487
Adrian LaughlinDepartment of General Practice and Primary Care, University of Melbourne, Melbourne, Australia.ORCID 0000-0003-3545-4785
Rita McMorrowCentre for Research Excellence in Interactive Digital Technology to Transform Australia's Chronic Disease Outcomes, Prahan, Australia.ORCID 0000-0002-2835-9504
Douglas BoyleCentre for Research Excellence in Interactive Digital Technology to Transform Australia's Chronic Disease Outcomes, Prahan, Australia.ORCID 0000-0002-4779-7083
Patty ChondrosDepartment of General Practice and Primary Care, University of Melbourne, Melbourne, Australia.ORCID 0000-0003-0393-8734
Shilpanjali JesudasonCentral Northern Adelaide Renal and Transplantation Service, Royal Adelaide Hospital, University of Adelaide, Adelaide, Australia.ORCID 0000-0001-9695-0761
Jan RadfordLaunceston Clinical School, University of Tasmania, Launceston, Australia.ORCID 0000-0002-5751-0488
Megan PrictorMelbourne Law School, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-5244-2041
Jon EmeryDepartment of General Practice and Primary Care, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-5274-6336
Paul AmoresCentre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-3507-2982
An Tran-DuyCentre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-9558-3004
Craig NelsonWestern Health Chronic Disease Alliance, Western Health, Sunshine, Australia.ORCID 0000-0003-3548-4167

Funding

Cancer Research UK C8640/A23385
6 · The paper itself

Abstract

backgroundFuture Health Today (FHT) is a program integrated with electronic medical record (EMR) systems in general practice and comprises (1) a practice dashboard to identify people at risk of, or with, chronic disease who may benefit from intervention; (2) active clinical decision support (CDS) at the point of care; and (3) quality improvement activities. One module within FHT aims to facilitate cardiovascular disease (CVD) risk reduction in people with chronic kidney disease (CKD) through the recommendation of angiotensin-converting enzyme inhibitor inhibitors (ACEI), angiotensin receptor blockers (ARB), or statins according to Australian guidelines (defined as appropriate pharmacological therapy).

objectiveThis study aimed to determine if the FHT program increases the proportion of general practice patients with CKD receiving appropriate pharmacological therapy (statins alone, ACEI or ARB alone, or both) to reduce CVD risk at 12 months postrandomization compared with active control (primary outcome).

methodsGeneral practices recruited through practice-based research networks in Victoria and Tasmania were randomly allocated 1:1 to the FHT CKD module or active control. The intervention was delivered to practices between October 4, 2021, and September 30, 2022. Data extracted from EMRs for eligible patients identified at baseline were used to evaluate the trial outcomes at the completion of the intervention period. The primary analysis used an intention-to-treat approach. The intervention effect for the primary outcome was estimated with a marginal logistic model using generalized estimating equations with robust SE.

resultsOverall, of the 734 eligible patients from 19 intervention practices and 715 from 21 control practices, 82 (11.2%) and 70 (9.8%), respectively, had received appropriate pharmacological therapy (statins alone, ACEI or ARB alone, or both) at 12 months postintervention to reduce CVD risk, with an estimated between-trial group difference (Diff) of 2.0% (95% CI -1.6% to 5.7%) and odds ratio of 1.24 (95% CI 0.85 to 1.81; P=.26). Of the 470 intervention patients and 425 control patients that received a recommendation for statins, 61 (13%) and 38 (9%) were prescribed statins at follow-up (Diff 4.3%, 95% CI 0 to 8.6%; odds ratio 1.55, 95% CI 1.02 to 2.35; P=.04). There was no statistical evidence to support between-group differences in other secondary outcomes and general practice health care use.

conclusionsFHT harnesses the data stored within EMRs to translate guidelines into practice through quality improvement activities and active clinical decision support. In this instance, it did not result in a difference in prescribing or clinical outcomes except for small changes in statin prescribing. This may relate to COVID-19-related disruptions, technical implementation challenges, and recruiting higher performing practices to the trial. A separate process evaluation will further explore factors impacting implementation and engagement with FHT.

trial registrationACTRN12620000993998; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380119.

Indexed as

Cardiovascular DiseasesElectronic Health RecordsGeneral PracticeQuality ImprovementRenal Insufficiency, ChronicAdultAgedAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsDecision Support Systems, ClinicalFemaleHumansHydroxymethylglutaryl-CoA Reductase InhibitorsMaleMiddle AgedTasmaniaAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsHydroxymethylglutaryl-CoA Reductase Inhibitorscardiovascularcardiovascular diseasechronic kidney diseaseclinical decisionclinical decision supportdecision supportelectronic medical recordgeneral practicekidneykidney diseaselogistic modelmedical recordspharmacologicalpharmacological therapyprimary careriskrisk reductionsupport

Identifiers

PMID39899838
PMCPMC11833263

What OpenQuestion holds

Texttitle and abstract
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Registered trials

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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.