Evidence map›Paper›PMID 29580660›Full record

ArticleAmerican journal of kidney diseases : the official journal of the National Kidney Foundation2018

CKD Self-management: Phenotypes and Associations With Clinical Outcomes.

Sarah J Schrauben, Jesse Y Hsu, Sylvia E Rosas, Bernard G Jaar, Xiaoming Zhang, Rajat Deo, Georges Saab, Jing Chen, Swati Lederer, Radhika Kanthety and 6 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of kidney diseases : the official journal of the National Kidney Foundation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

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, 1 synthesis or guideline pooled it.

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  17. Mobile Health (mHealth) Technology: Assessment of Availability, Acceptability, and Use in CKD.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2021
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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

16 authors.

Sarah J SchraubenDivision of Renal, Electrolyte, and Hypertension, University of Pennsylvania, Philadelphia, PA. Electronic address: sarah.schrauben@uphs.upenn.edu.
Jesse Y HsuCenter for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
Sylvia E RosasJoslin Diabetes Center, Harvard Medical School, Boston, MA.
Bernard G JaarDivision of Nephrology, Johns Hopkins University, Baltimore, MD; Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD.
Xiaoming ZhangCenter for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
Rajat DeoCenter for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA; Division of Cardiology, University of Pennsylvania, Philadelphia, PA.
Georges SaabDivision of Nephrology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH.
Jing ChenDepartment of Medicine, Tulane University, New Orleans, LA.
Swati LedererDepartment of Medicine, University of Texas Southwestern, Dallas, TX.
Radhika KanthetyDepartment of Medicine, Case Western University, Cleveland, OH.
L Lee HammDepartment of Medicine, Tulane University, New Orleans, LA.
Ana C RicardoDepartment of Medicine, University of Illinois at Chicago, Chicago, IL.
James P LashDepartment of Medicine, University of Illinois at Chicago, Chicago, IL.
Harold I FeldmanDivision of Renal, Electrolyte, and Hypertension, University of Pennsylvania, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
Amanda H AndersonCenter for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
CRIC Study Investigators

Funding

UCSF CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024131 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JOHNSTON, S. CLAIBORNE · 2006 to 2011
$111.2M
Tracking & Evaluation CoreU54GM104940 · NIGMS · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI Peter Todd Katzmarzyk · 2012 to 2026
$69.1M
Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
Scientific and Data Coordinating Center(SDCC) for the P*U01DK060990 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI FELDMAN, HAROLD I · 2001 to 2017
$50.1M
Clinical and Translational Science Collaborative of ClevelandUL1TR000439 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI KONSTAN, MICHAEL W. · 2012 to 2016
$50.0M
Michigan Institute for Clinical and Health Research (MCHR)UL1TR000433 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MASHOUR, GEORGE ALEXANDER · 2012 to 2016
$49.9M
Institutional Clinical and Translational Science AwardUL1TR000003 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2012 to 2015
$38.9M
Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
Strengthening Stakeholder Engagement in Human Research Protections.UL1TR002003 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KARNIK, NIRANJAN SUBHASH, MERMELSTEIN, ROBIN J. · 2016 to 2024
$33.7M
Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI JING CHEN · 2016 to 2026
$25.3M
Continuation of the Coordinating Center for the Chronic Renal Insufficiency Cohort (CRIC) StudyU24DK060990 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Amanda Hyre Anderson, Laura M Dember · 2018 to 2026
$21.5M
Zonulin Antibodies in Intestinal Permeabilitiy in IDDMM01RR016500 · NCRR · UNIVERSITY OF MARYLAND BALTIMORE · PI OSWALD, LYNN M · 2002 to 2009
$20.2M
NCATS NIH HHS UL1 TR000424NCATS NIH HHS UL1 TR000433NCATS NIH HHS UL1 TR000439NCATS NIH HHS UL1 TR002003NCATS NIH HHS UL1 TR002240NCATS NIH HHS UL1 TR002548NCRR NIH HHS M01 RR016500NCRR NIH HHS UL1 RR024131NCRR NIH HHS UL1 RR029879NIDDK NIH HHS F32 DK113681NIDDK NIH HHS K23 DK094829NIDDK NIH HHS R01 DK072231NIDDK NIH HHS T32 DK007785NIDDK NIH HHS U01 DK060902NIDDK NIH HHS U01 DK060963NIDDK NIH HHS U01 DK060980NIDDK NIH HHS U01 DK060984NIDDK NIH HHS U01 DK060990NIDDK NIH HHS U01 DK061021NIDDK NIH HHS U01 DK061022NIDDK NIH HHS U01 DK061028NIDDK NIH HHS U24 DK060990NIGMS NIH HHS P20 GM109036NIGMS NIH HHS U54 GM104940
6 · The paper itself

Abstract

backgroundTo slow chronic kidney disease (CKD) progression and its complications, patients need to engage in self-management behaviors. The objective of this study was to classify CKD self-management behaviors into phenotypes and assess the association of these phenotypes with clinical outcomes. STUDY

designProspective cohort study. SETTING &

participantsAdults with mild to moderate CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study. 3,939 participants in the CRIC Study recruited between 2003 and 2008 served as the derivation cohort and 1,560 participants recruited between 2013 and 2015 served as the validation cohort. PREDICTORS: CKD self-management behavior phenotypes. OUTCOMES: CKD progression, atherosclerotic events, heart failure events, death from any cause. MEASUREMENTS: Latent class analysis stratified by diabetes was used to identify CKD self-management phenotypes based on measures of body mass index, diet, physical activity, blood pressure, smoking status, and hemoglobin A

results3 identified phenotypes varied according to the extent of implementation of recommended CKD self-management behaviors: phenotype I characterized study participants with the most recommended behaviors; phenotype II, participants with a mixture of recommended and not recommended behaviors; and phenotype III, participants with minimal recommended behaviors. In multivariable-adjusted models for those with and without diabetes, phenotype III was strongly associated with CKD progression (HRs of 1.82 and 1.49), death (HRs of 1.95 and 4.14), and atherosclerotic events (HRs of 2.54 and 1.90; each P < 0.05). Phenotype II was associated with atherosclerotic events and death among those with and without diabetes. LIMITATIONS: No consensus definition of CKD self-management; limited to baseline behavior data.

conclusionsThere are potentially 3 CKD self-management behavior phenotypes that distinguish risk for clinical outcomes. These phenotypes may inform the development of studies and guidelines regarding optimal self-management.

Indexed as

PhenotypeAdultAgedCohort StudiesFemaleHumansMaleMiddle AgedProspective StudiesRenal Insufficiency, ChronicSelf-ManagementTreatment OutcomeYoung Adultall-cause deathatherosclerotic eventsblood pressure controlchronic kidney disease (CKD)Chronic renal failureCKD progression: cardiovascular outcomesdiabeteshealthy behaviorsheart failuremodifiable risk factorspatient engagementself-careself-managementsmoking

Identifiers

PMID29580660
PMCPMC6109611

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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.