Evidence map›Paper›PMID 30962703›Full record

ArticleInternational journal of nephrology and renovascular disease2019

Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study.

Tian Chen, Pamela Brewster, Katherine R Tuttle, Lance D Dworkin, William Henrich, Barbara A Greco, Michael Steffes, Sheldon Tobe, Kenneth Jamerson, Karol Pencina and 6 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of nephrology and renovascular disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00081731 (Cardiovascular Outcomes in Renal Atherosclerotic Lesions), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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.

NCT00081731 phase3completednot on this map

Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL)

TypeinterventionalSponsorBaim Institute for Clinical ResearchRan2004 to 2013Enrolled947ConditionsAtherosclerosis, Cardiovascular Diseases, Hypertension, Renovascular, Renal Artery ObstructionArmsAtacand/HCT, Caduet, GENESISTM Embolic Protection Stent and Angioguard Device (Angioplasty plus stenting)
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. 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

16 authors.

Tian ChenUniversity of Toledo, Toledo, OH, USA.
Pamela BrewsterUniversity of Toledo, Toledo, OH, USA.
Katherine R TuttleProvidence Health Care, University of Washington, Spokane, WA, USA.
Lance D DworkinUniversity of Toledo, Toledo, OH, USA.
William HenrichUniversity of Texas Health Science Center, San Antonio, TX, USA.
Barbara A GrecoBaystate Health, Springfield, MA, USA.
Michael SteffesUniversity of Minnesota, Minneapolis, MN, USA.
Sheldon TobeUniversity of Toronto, Toronto, ON, Canada.
Kenneth JamersonUniversity of Michigan, Ann Arbor, MI, USA.
Karol PencinaHarvard Clinical Research Institute, Boston University, Boston, MA, USA.
Joseph M MassaroHarvard Clinical Research Institute, Boston University, Boston, MA, USA.
Ralph B D'AgostinoHarvard Clinical Research Institute, Boston University, Boston, MA, USA.
Donald E CutlipBeth Israel Deaconess Medical Center, Boston, MA, USA.
Timothy P MurphyBrown University, Providence, RI, USA.
Christopher J CooperUniversity of Toledo, Toledo, OH, USA.
Joseph I ShapiroMarshall University, Huntington, WV, USA, shapiroj@marshall.edu.

Funding

CORAL: Clinical Coordinating CenterU01HL071556 · NHLBI · UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS · PI COOPER, CHRISTOPHER J · 2004 to 2013
$19.3M
CORAL: Data Coordinating CenterU01HL072737 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CUTLIP, DONALD · 2004 to 2013
$9.4M
CORAL: Economics and QOL Core LaboratoryU01HL072736 · NHLBI · SAINT LUKE'S HOSPITAL · PI COHEN, DAVID J · 2004 to 2013
$2.6M
CORAL Angiographic Core LaboratoryU01HL072735 · NHLBI · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI MATSUMOTO, ALAN H. · 2004 to 2010
$1.4M
Coral: Central GFR and Biochemistry LaboratoryU01HL072734 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI STEFFES, MICHAEL W. · 2004 to 2012
$1.3M
NHLBI NIH HHS U01 HL071556NHLBI NIH HHS U01 HL072734NHLBI NIH HHS U01 HL072735NHLBI NIH HHS U01 HL072736NHLBI NIH HHS U01 HL072737
6 · The paper itself

Abstract

backgroundData derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study.

methodsWe identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according to an 80%:20% distribution with various seeds. Prediction models were optimized within the CARET package of R.

resultsThe best performance of the different machine learning techniques was that of the random forest method which yielded a receiver operator curve (ROC) area of 68.1%±4.2% (mean ± SD) on the testing subset with ten different seed values used to separate training and testing subsets. The four most important variables in the random forest method were SBP, serum creatinine, glycosylated hemoglobin, and DBP. Each of these variables was also important in at least some of the other methods. The treatment assignment group was not consistently an important determinant in any of the models.

conclusionPrediction of a composite cardiovascular outcome was difficult in the CORAL population, even when employing machine learning methods. Assignment to either the stenting or best medical therapy group did not serve as an important predictor of composite outcome. CLINICAL

trial registrationClinicalTrials.gov, NCT00081731.

Indexed as

cardiovascular diseasechronic kidney diseaseglomerular filtration ratehypertensionischemic renal diseaserenal artery stenosis

Identifiers

PMID30962703
PMCPMC6433104

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Registered trials

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.