Evidence map›Paper›PMID 33934542›Full record

Trial reportESC heart failure2021

Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes.

Andreas B Gevaert, Semra Tibebu, Mamas A Mamas, Neal G Ravindra, Shun Fu Lee, Tariq Ahmad, Dennis T Ko, James L Januzzi, Harriette G C Van Spall

Registry-linked trialAbstract readMulticenter StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in ESC heart failure, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02112227 (Patient-centered Care Transitions in Heart Failure), which is not on this map. Cited by 44 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 2 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.

NCT02112227 nacompletednot on this map

Patient-centered Care Transitions in Heart Failure: A Pragmatic Cluster

TypeinterventionalSponsorPopulation Health Research InstituteRan2015 to 2016Enrolled3,500ConditionsHeart FailureArmsPACT-HF Model
3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 2 syntheses or guidelines 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

9 authors.

Andreas B GevaertResearch Group Cardiovascular Diseases, GENCOR Department, University of Antwerp, Antwerp, Belgium.
Semra TibebuICES, McMaster University, Hamilton, Ontario, Canada.
Mamas A MamasKeele Cardiovascular Research Group, Centre for Prognosis Research, Keele University, Stoke on Trent, UK.
Neal G RavindraSection of Cardiovascular Medicine, School of Medicine, Yale University, New Haven, CT, USA.
Shun Fu LeeDepartment of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
Tariq AhmadSection of Cardiovascular Medicine, School of Medicine, Yale University, New Haven, CT, USA.
Dennis T KoICES, University of Toronto, Hamilton, Ontario, Canada.
James L JanuzziCardiology Division, Massachusetts General Hospital, Boston, MA, USA.
Harriette G C Van SpallICES, McMaster University, Hamilton, Ontario, Canada.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
CIHR 135917NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

aimsHeart failure (HF) guidelines place patients into 3 discrete groups according to left ventricular ejection fraction (LVEF): reduced (<40%), mid-range (40-49%), and preserved LVEF (≥50%). We assessed whether clinical phenogroups offer better prognostication than LVEF. METHODS AND

resultsThis was a sub-study of the Patient-Centered Care Transitions in HF trial. We analysed baseline characteristics of hospitalized patients in whom LVEF was recorded. We used unsupervised machine learning to identify clinical phenogroups and, thereafter, determined associations between phenogroups and outcomes. Primary outcome was the composite of all-cause death or rehospitalization at 6 and 12 months. Secondary outcome was the composite cardiovascular death or HF rehospitalization at 6 and 12 months. Cluster analysis of 1693 patients revealed six discrete phenogroups, each characterized by a predominant comorbidity: coronary heart disease, valvular heart disease, atrial fibrillation (AF), sleep apnoea, chronic obstructive pulmonary disease (COPD), or few comorbidities. Phenogroups were LVEF independent, with each phenogroup encompassing a wide range of LVEFs. For the primary composite outcome at 6 months, the hazard ratios (HRs) for phenogroups ranged from 1.25 [95% confidence interval (CI) 1.00-1.58 for AF] to 2.04 (95% CI 1.62-2.57 for COPD) (log-rank P < 0.001); and at 12 months, the HRs for phenogroups ranged from 1.15 (95% CI 0.94-1.41 for AF) to 1.87 (95% 1.52-3.20 for COPD) (P < 0.002). LVEF-based classifications did not separate patients into different risk categories for the primary outcomes at 6 months (P = 0.69) and 12 months (P = 0.30). Phenogroups also stratified risk of the secondary composite outcome at 6 and 12 months more effectively than LVEF.

conclusionAmong patients hospitalized for HF, clinical phenotypes generated by unsupervised machine learning provided greater prognostic information for a composite of clinical endpoints at 6 and 12 months compared with LVEF-based categories.

trial registrationClinicalTrials.gov Identifier: NCT02112227.

Indexed as

Heart FailurePulmonary Disease, Chronic ObstructiveHumansPrognosisStroke VolumeVentricular Function, LeftAetiologyCharacteristicsClinical StudiesComorbiditiesEjection fractionHeart failureHFmrEFHFpEFMachine LearningMortality/SurvivalPhenogroupsPrognosisRisk Factors

Identifiers

PMID33934542
PMCPMC8318507

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.