Evidence map›Paper›PMID 27276913›Full record

ArticleBMC nephrology2016

Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study.

Yuxiang Xie, Marlena Maziarz, Delphine S Tuot, Glenn M Chertow, Jonathan Himmelfarb, Yoshio N Hall

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

Article in BMC nephrology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
0.7field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Towards the best kidney failure prediction tool: a systematic review and selection aid.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2020
    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

6 authors at 3 institutions in 1 country.

Yuxiang XieKidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA.
Marlena MaziarzKidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA.
Delphine S TuotDivision of Nephrology, University of California San Francisco and San Francisco General Hospital, San Francisco, CA, USA.
Glenn M ChertowDivision of Nephrology, School of Medicine, Stanford University, Palo Alto, CA, USA.
Jonathan HimmelfarbKidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA.
Yoshio N HallKidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA. ynhall@uw.edu.
University of Washington · USSan Francisco General Hospital · USStanford University · US

Funding

Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
Institute of Translational Health SciencesUL1TR000423 · NCATS · UNIVERSITY OF WASHINGTON · PI DISIS, MARY L. · 2012 to 2016
$49.4M
Mentoring Patient-Oriented Clinical Investigators in NephrologyK24DK085446 · NIDDK · STANFORD UNIVERSITY · PI CHERTOW, GLENN MATTHEW · 2010 to 2022
$1.4M
Chronic Kidney Disease in Disadvantaged PopulationsK23DK087900 · NIDDK · UNIVERSITY OF WASHINGTON · PI HALL, YOSHIO N. · 2010 to 2014
$865k
CKD awareness and self-management: Interventions for Safety-Net PatientsK23DK094850 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TUOT, DELPHINE · 2012 to 2015
$754k
Processes and Outcomes of Chronic Kidney Disease in the Urban PoorR03DK099487 · NIDDK · UNIVERSITY OF WASHINGTON · PI HALL, YOSHIO N. · 2013 to 2014
$155k
NCATS NIH HHS UL1 TR000423NCATS NIH HHS UL1 TR002319NIDDK NIH HHS K23 DK087900NIDDK NIH HHS K23 DK094850NIDDK NIH HHS K24 DK085446NIDDK NIH HHS R03 DK099487
6 · The paper itself

Abstract

backgroundThe capacity of electronic health record (EHR) data to guide targeted surveillance in chronic kidney disease (CKD) is unclear. We sought to leverage EHR data for predicting risk of progressing from CKD to end-stage renal disease (ESRD) to help inform surveillance of CKD among vulnerable patients from the healthcare safety-net.

methodsWe conducted a retrospective cohort study of adults (n = 28,779) with CKD who received care within 2 regional safety-net health systems during 1996-2009 in the Western United States. The primary outcomes were progression to ESRD and death as ascertained by linkage with United States Renal Data System and Social Security Administration Death Master files, respectively, through September 29, 2011. We evaluated the performance of 3 models which included demographic, comorbidity and laboratory data to predict progression of CKD to ESRD in conditions commonly targeted for disease management (hypertension, diabetes, chronic viral diseases and severe CKD) using traditional discriminatory criteria (AUC) and recent criteria intended to guide population health management strategies.

resultsOverall, 1730 persons progressed to end-stage renal disease and 7628 died during median follow-up of 6.6 years. Performance of risk models incorporating common EHR variables was highest in hypertension, intermediate in diabetes and chronic viral diseases, and lowest in severe CKD. Surveillance of persons who were in the highest quintile of ESRD risk yielded 83-94 %, 74-95 %, and 75-82 % of cases who progressed to ESRD among patients with hypertension, diabetes and chronic viral diseases, respectively. Similar surveillance yielded 42-71 % of ESRD cases among those with severe CKD. Discrimination in all conditions was universally high (AUC ≥0.80) when evaluated using traditional criteria.

conclusionsRecently proposed discriminatory criteria account for varying risk distribution and when applied to common clinical conditions may help to inform surveillance of CKD in diverse populations.

Indexed as

Disease ProgressionModels, StatisticalAdultAgedArea Under CurveChronic DiseaseComorbidityDiabetes MellitusElectronic Health RecordsFemaleHumansHypertensionKidney Failure, ChronicMaleMiddle AgedPopulation Surveillance

Identifiers

PMID27276913
PMCPMC4898308
OpenAlexW2410032089

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.