Evidence map›Paper›PMID 30467996›Full record

ArticleNephrology (Carlton, Vic.)2019

Epidemiology of chronic kidney disease in Australian general practice: National Prescribing Service MedicineWise MedicineInsight dataset.

Janette Radford, Alex Kitsos, Jim Stankovich, Ronald Castelino, Masuma Khanam, Matthew Jose, Gregory Peterson, Timothy Saunder, Barbara Wimmer, Tabish Razizaidi

Open access · bronzeAbstract read
In one paragraph

Article in Nephrology (Carlton, Vic.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.

  1. Global health inequalities of chronic kidney disease: a meta-analysis.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024
    Pooled it
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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

10 authors at 2 institutions in 1 country.

Janette RadfordSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.ORCID https://orcid.org/0000-0002-5751-0488
Alex KitsosSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Jim StankovichSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Ronald CastelinoSydney Nursing School, University of Sydney, Sydney, New South Wales, Australia.
Masuma KhanamSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Matthew JoseSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Gregory PetersonSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Timothy SaunderSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Barbara WimmerSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
Tabish RazizaidiSchool of Medicine, University of Tasmania, Launceston, Tasmania, Australia.
University of Tasmania · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo describe sociodemographic characteristics and comorbidities of a large cohort of Australian general practice-based patients identified as having chronic kidney disease (CKD), using data from National Prescribing Service (NPS) MedicineWise's MedicineInsight dataset, and compare this dataset to the 2011-2012 Australian Health Survey's (AHS) CKD prevalence estimates.

methodsThis was a cohort study using deidentified, longitudinal, electronic health record data collected from 329 practices and 1 483 416 patients distributed across Australia, from 1 June 2013 until 1 June 2016. Two methods were used to calculate the CKD prevalence. One used the same method as used by the 2011-2012 AHS, based on one estimate of the estimated glomerular filtration rate (eGFR) or albumin/creatinine ratios (ACR). The other defined CKD more rigorously using eGFR or ACR results at least 90 days apart.

resultsIn 2016, of 1 310 602 active patients, 710 674 (54.2%) did not have an eGFR or ACR test, while 524 961 (40.1%) had an eGFR or ACR test but did not meet AHS criteria for CKD. Age-sex adjusted rates of CKD (compared to AHS) were CKD 1-0.45% (3.9%), CKD 2-0.62% (2.5%), CKD 3a: 3.1% (2.7%), CKD 3b: 1.14% (0.6%), CKD 4-5: 0.41% (0.3%). The CKD cohort defined more rigorously using eGFR and ACR measures >90 days apart, had comorbidities of atrial fibrillation (30.5%), cardiovascular disease (25.0%), diabetes mellitus (17.1%) and hypertension (14.8%).

conclusionThe MedicineInsight dataset contains valuable and timely information about Australian patients with CKD, and provides prevalence estimates similar to those from AHS data.

Indexed as

General PracticeRenal Insufficiency, ChronicAgedAustraliaCardiovascular DiseasesCohort StudiesComorbidityDiabetes MellitusElectronic Health RecordsFemaleGlomerular Filtration RateHumansMaleMiddle AgedPractice Patterns, Physicians'Prevalencechronic kidney diseaseclinical epidemiologycomorbidity

Identifiers

PMID30467996
PMCPMC6790712
OpenAlexW2905043725

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.