Evidence map›Paper›PMID 30606109›Full record

ArticleBMC nephrology2019

Validating laboratory defined chronic kidney disease in the electronic health record for patients in primary care.

Martin Frigaard, Anna Rubinsky, Lo Lowell, Anna Malkina, Leah Karliner, Michael Kohn, Carmen A Peralta

Open access · goldAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nephrology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
2.6field-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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

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  8. Chronic Kidney Disease in Balkan Countries-A Call for Optimal Multidisciplinary Management.International journal of environmental research and public health · 2025
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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 2 institutions in 1 country.

Martin FrigaardKidney health research collaborative (KHRC), University of California, 4150 Clement St. Building 2, Room 145, San Francisco, CA, 94121, USA. martin.frigaard@ucsf.edu.ORCID 0000-0001-9660-7162
Anna RubinskyKidney health research collaborative (KHRC), University of California, 4150 Clement St. Building 2, Room 145, San Francisco, CA, 94121, USA.
Lo LowellNephrology and Hypertension at Parnassus, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94122, USA.
Anna MalkinaNephrology and Hypertension at Parnassus, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94122, USA.
Leah KarlinerNephrology and Hypertension at Parnassus, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94122, USA.
Michael KohnNephrology and Hypertension at Parnassus, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94122, USA.
Carmen A PeraltaNephrology and Hypertension at Parnassus, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94122, USA.
University of California, San Francisco · USCommunity Health Resource Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectronic health record (EHR) data is increasingly used to identify patients with chronic kidney disease (CKD). EHR queries used to capture CKD status, identify comorbid conditions, measure awareness by providers, and track adherence to guideline-concordant processes of care have not been validated.

methodsWe extracted EHR data for primary-care patients with two eGFRcreat 15-59 mL/min/1.73 m^2 at least 90 days apart. Two nephrologists manually reviewed a random sample of 50 charts to determine CKD status, associated comorbidities, and physician awareness of CKD. We also assessed the documentation of a CKD diagnosis with guideline-driven care.

resultsComplete data were available on 1767 patients with query-defined CKD of whom 822 (47%) had a CKD diagnosis in their chart. Manual chart review confirmed the CKD diagnosis in 34 or 50 (68%) patients. Agreement between the reviewers and the EHR diagnoses on the presence of comorbidities was good (κ > 0.70, p < 0.05), except for congestive heart failure, (κ = 0.45, p < 0.05). Reviewers felt the providers were aware of CKD in 23 of 34 (68%) of the confirmed CKD cases. A CKD diagnosis was associated with higher odds of guideline-driven care including CKD-specific laboratory tests and prescriptions for statins. After adjustment, CKD diagnosis documentation was not significantly associated with ACE/ARB prescription.

conclusionsIdentifying CKD status by historical eGFRs overestimates disease prevalence. A CKD diagnosis in the patient chart was a reasonable surrogate for provider awareness of disease status, but CKD awareness remains relatively low. CKD in the patient chart was associated with higher rates of albuminuria testing and use of statins, but not use of ACE/ARB.

Indexed as

Electronic Health RecordsPrimary Health CareAgedCohort StudiesComorbidityFemaleHumansMaleMiddle AgedPractice Guidelines as TopicRenal Insufficiency, ChronicSampling StudiesSocioeconomic FactorsChronic kidney diseaseElectronic health record phenotypeValidation

Identifiers

PMID30606109
PMCPMC6318865
OpenAlexW2919674785

What OpenQuestion holds

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LicenceCC BY
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.