Evidence map›Paper›PMID 29279299›Full record

Trial reportDiabetes care2018

Characteristics Associated With Decreased or Increased Mortality Risk From Glycemic Therapy Among Patients With Type 2 Diabetes and High Cardiovascular Risk: Machine Learning Analysis of the ACCORD Trial.

Sanjay Basu, Sridharan Raghavan, Deborah J Wexler, Seth A Berkowitz

Open access · greenAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Diabetes care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
3.5field-weighted citation impact, top 6% 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

39 citing papers in PubMed, 76 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Sanjay BasuCenter for Primary Care and Outcomes Research, Center for Population Health Sciences, Departments of Medicine and Health Research and Policy, Stanford University, Palo Alto, CA basus@stanford.edu.ORCID 0000-0002-0599-6332
Sridharan RaghavanDepartment of Veterans Affairs Eastern Colorado Healthcare System, Denver, CO.ORCID 0000-0003-0643-4873
Deborah J WexlerHarvard Medical School, Boston, MA.
Seth A BerkowitzHarvard Medical School, Boston, MA.
Harvard University · USVA Eastern Colorado Health Care System · US

Funding

Glycemia Reduction Approaches in Diabetes: A comparative effectiveness studyU01DK098246 · NIDDK · GEORGE WASHINGTON UNIVERSITY · PI KRAUSE-STEINRAUF, HEIDI, LACHIN, JOHN M · 2012 to 2022
$238.9M
Trial of strategies to communicate genetic information to different ethnic and racial subpopulationsU54MD010724 · NIMHD · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD · 2016 to 2021
$16.3M
Cohort filtering models to identify social program effects on health disparitiesDP2MD010478 · NIMHD · STANFORD UNIVERSITY · PI BASU, SANJAY · 2015 to 2015
$2.4M
Understanding & Overcoming Food Insecurity in Diabetes PatientsK23DK109200 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BERKOWITZ, SETH A · 2016 to 2020
$973k
NIDDK NIH HHS K23 DK109200NIDDK NIH HHS L30 DK103291NIDDK NIH HHS U01 DK098246NIMHD NIH HHS DP2 MD010478NIMHD NIH HHS U54 MD010724
6 · The paper itself

Abstract

objectiveIdentifying patients who may experience decreased or increased mortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovascular risk with decreased or increased mortality risk from glycemic therapy for type 2 diabetes using new methods to identify complex combinations of treatment effect modifiers. RESEARCH DESIGN AND

methodsThe machine learning method of gradient forest analysis was applied to understand the variation in all-cause mortality within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (

resultsThe analysis identified four groups defined by age, BMI, and HGI with varied risk for mortality under intensive glycemic therapy. The lowest risk group (HGI <0.44, BMI <30 kg/m

conclusionsAge, BMI, and HGI may help individualize prediction of the benefit and harm from intensive glycemic therapy.

Indexed as

Machine LearningAdultAgedBiomarkersBlood GlucoseBody Mass IndexCardiovascular DiseasesDiabetes Mellitus, Type 2Double-Blind MethodFollow-Up StudiesGlycated HemoglobinHumansMiddle AgedRisk FactorsSample SizeSensitivity and SpecificityBiomarkersBlood GlucoseGlycated Hemoglobin

Identifiers

PMID29279299
PMCPMC5829969
OpenAlexW2778596258

What Socratic holds

Textmetadata
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 Socratic graph.