Evidence map›Paper›PMID 35984661›Full record

ArticleJAMA network open2022

Analysis of Specialty Nephrology Care Among Patients With Chronic Kidney Disease and High Risk of Disease Progression.

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

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 10% 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, 18 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Addressing Unmet Needs in the Management of Chronic Kidney Disease.International journal of general medicine · 2026
    Review
  5. The Emerging Nephrology Physician Workforce in the United States: Defining an Impending Shortfall.Clinical journal of the American Society of Nephrology : CJASN · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Bridging the gap of referral to nephrology care.Nature reviews. Nephrology · 2023
    Article
  14. 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

5 authors at 4 institutions in 1 country.

Maggie WangDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California.
Samson S PeterSchool of Medicine, Stanford University, Stanford, California.
Chi D ChuDepartment of Medicine, University of California, San Francisco.
Delphine S TuotDepartment of Medicine, University of California, San Francisco.
Jonathan H ChenStanford Center for Biomedical Informatics Research and Division of Hospital Medicine, Department of Medicine, Stanford University, Stanford, California.
Stanford University · USCenter for Innovation · USStanford Medicine · USUniversity of California, San Francisco · US

Funding

Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant BacteriaR01AI178121 · NIAID · STANFORD UNIVERSITY · PI JONATHAN H. CHEN · 2023 to 2026
$3.1M
Machine Learning Clinical Order Recommendations for Specialty Consultation CareR56LM013365 · NLM · STANFORD UNIVERSITY · PI CHEN, JONATHAN H. · 2020 to 2020
$394k
NIAID NIH HHS R01 AI178121NLM NIH HHS R56 LM013365
6 · The paper itself

Abstract

Importance: Identification of patients with chronic kidney disease (CKD) with high risk of progression to kidney failure can help ensure they receive appropriate and effective nephrology care. Objective: To examine whether patients with CKD at various levels of kidney failure risk receive nephrology care within 1 year of established risk. Design, Setting, and Participants: This population-based, retrospective cohort study collected nationwide administrative health claims data from 156 733 adult patients who met the Kidney Disease: Improving Global Outcomes initiative CKD diagnostic criteria between January 1, 2012, and December 31, 2019, and had an available urine albumin to creatinine ratio within 90 days of a serum creatinine laboratory test. Patients with a history of dialysis or kidney transplant, a prior visit with a nephrologist in the past year, or palliative care billing codes or those who died or disenrolled within 1 year of the albumin to creatinine ratio measurement were excluded. Data analysis was performed from September 10, 2022, to February 14, 2022. Exposures: Kidney failure risk computed with the 5-year Kidney Failure Risk Equation. Main Outcomes and Measures: The main outcome was nephrology care rates across tiers of kidney failure risk, estimated as the proportion of individuals having a nephrologist visit within 1 year after index time. Results: The study population consisted of 156 733 patients with CKD (mean [SD] age, 74.6 [8.4] years; 91 906 [58.6%] female; 86 457 [55.2%] White). A total of 106 004 patients (67.6%) had a low (≤1%) 5-year risk of kidney failure. Nephrology visit rates increased with higher kidney failure risk. Among the 137 highest-risk patients, 79 (57.7%; 95% CI, 48.4%-64.7%) had a nephrology visit. Among 7730 patients with risk above a 10% threshold, 3208 (41.5%; 95% CI, 40.3%-42.4%) had a nephrology visit. Conclusions and Relevance: This study's findings suggest that nearly half of patients with CKD at high risk of progressing to kidney failure do not have a nephrologist visit within 1 year of established risk. These findings have implications in the design of risk-based guidelines for referral and in the practice of delivering nephrology care to patients with CKD.

Indexed as

NephrologyRenal Insufficiency, ChronicAdultAgedAlbuminsCreatinineDisease ProgressionFemaleHumansMaleRenal DialysisRetrospective StudiesAlbuminsCreatinine

Identifiers

PMID35984661
PMCPMC9391959
OpenAlexW4292369322

What OpenQuestion holds

Textmetadata
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.