Nephrology Referral Based on Laboratory Values, Kidney Failure Risk, or Both: A Study Using Veterans Affairs Health System Data.


Journal

American journal of kidney diseases : the official journal of the National Kidney Foundation
ISSN: 1523-6838
Titre abrégé: Am J Kidney Dis
Pays: United States
ID NLM: 8110075

Informations de publication

Date de publication:
Mar 2022
Historique:
received: 16 11 2020
accepted: 16 06 2021
pubmed: 28 8 2021
medline: 8 4 2022
entrez: 27 8 2021
Statut: ppublish

Résumé

Current guidelines for nephrology referral are based on laboratory criteria. We sought to evaluate whether nephrology referral patterns reflect current clinical practice guidelines and to estimate the change in referral volume if they were based on the estimated risk of kidney failure. Observational cohort. Retrospective study of 399,644 veterans with chronic kidney disease (October 1, 2015 through September 30, 2016). Laboratory referral criteria based on Veterans Affairs/Department of Defense guidelines, categories of predicted risk for kidney failure using the Kidney Failure Risk Equation, and the combination of laboratory referral criteria and predicted risk. Number of patients identified for referral. We evaluated the number of patients who were referred and their predicted 2-year risk for kidney failure. For each exposure, we estimated the number of patients who would be identified for referral. There were 66,276 patients who met laboratory indications for referral. Among these patients, 11,752 (17.7%) were referred to nephrology in the following year. The median 2-year predicted risk of kidney failure was 1.5% (interquartile range, 0.3%-4.7%) among all patients meeting the laboratory referral criteria. If referrals were restricted to patients with a predicted risk of ≥1% in addition to laboratory indications, the potential referral volume would be reduced from 66,276 to 38,229 patients. If referrals were based on predicted risk alone, a 2-year risk threshold of 1% or higher would identify a similar number of patients (72,948) as laboratory-based criteria with median predicted risk of 2.3% (interquartile range, 1.4%-4.6%). Missing proteinuria measurements. The current laboratory-based guidelines for nephrology referral identify patients who are, on average, at low risk for progression, most of whom are not referred. As an alternative, referral based on a 2-year kidney failure risk exceeding 1% would identify a similar number of patients but with a higher median risk of kidney failure.

Identifiants

pubmed: 34450193
pii: S0272-6386(21)00792-7
doi: 10.1053/j.ajkd.2021.06.028
pmc: PMC9973036
mid: NIHMS1874208
pii:
doi:

Types de publication

Journal Article Observational Study Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

347-353

Subventions

Organisme : HSRD VA
ID : I01 HX001262
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

Published by Elsevier Inc.

Références

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pubmed: 23617782
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pubmed: 25441435
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pubmed: 33752804

Auteurs

Vishal Duggal (V)

Veterans Affairs Palo Alto Health Care System, Palo Alto, California; Department of Medicine, Division of Nephrology, School of Medicine, Stanford University, Stanford, California; Center for Primary Care and Outcomes Research, Stanford University, Stanford, California. Electronic address: duggal.vishal@gmail.com.

Maria E Montez-Rath (ME)

Department of Medicine, Division of Nephrology, School of Medicine, Stanford University, Stanford, California.

I-Chun Thomas (IC)

Geriatric Research and Education Clinical Center, Palo Alto, California.

Mary K Goldstein (MK)

Veterans Affairs Palo Alto Health Care System, Palo Alto, California; Center for Primary Care and Outcomes Research, Stanford University, Stanford, California.

Manjula Kurella Tamura (MK)

Geriatric Research and Education Clinical Center, Palo Alto, California; Veterans Affairs Palo Alto Health Care System, Palo Alto, California; Department of Medicine, Division of Nephrology, School of Medicine, Stanford University, Stanford, California.

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Classifications MeSH