Gaps in Nephrology Referral Care Utilization in Patients at High-Risk of Progression to Kidney Failure.

Samson Peter, Maggie Wang, Chi D Chu, Delphine S Tuot, Arhana Chattopadhyay, Jonathan H Chen

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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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

1 citing paper in PubMed.

Effect of pre-end-stage kidney disease nephrology care on the association between neighborhood poverty and referral for kidney transplantation among patients with end-stage kidney disease.Journal of clinical epidemiology · 2025
Article

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

6 authors.

Samson PeterStanford University School of Medicine, Stanford, CA.

Maggie WangDepartment of Biomedical Data Science, Stanford University, Stanford, CA.

Chi D ChuDepartment of Medicine, University of California, San Francisco, San Francisco, CA.

Delphine S TuotDepartment of Medicine, University of California, San Francisco, San Francisco, CA.

Arhana ChattopadhyayDivision of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford CA.

Jonathan H ChenStanford Center for Biomedical Informatics Research; Division of Hospital Medicine, Department of Medicine, Stanford University, School of Medicine, Stanford, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early nephrology specialty care slows progression of chronic kidney disease (CKD) to end-stage renal disease (ESRD). However, identifying which patients are expected to progress to end-stage disease has been historically challenging to predict. With a limited supply of nephrologists, optimizing nephrology referral is essential for improving patient outcomes. The Kidney Failure Risk Equation (KFRE) provides an accurate metric to identify patients who are at high risk of progression to kidney failure. In this study, we utilize the KFRE to perform a retrospective analysis in a local health network to identify rates of nephrology referral for CKD patients stratified by risk of kidney failure progression. We found a nephrology referral gap in CKD patients at higher risk of progression and an underutilization of albuminuria testing in CKD, suggesting opportunities to improve outcomes by 1) proactively targeting high-risk patients using EHR-based informatics strategies and 2) increasing albuminuria testing as a screening tool.

Indexed as

Kidney Failure, ChronicNephrologyRenal InsufficiencyRenal Insufficiency, ChronicAlbuminuriaDisease ProgressionHumansReferral and ConsultationRetrospective Studies

Identifiers

PMID37128404

PMCPMC10148329

What OpenQuestion holds

Textmetadata

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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.