Evidence map›Paper›PMID 38163367›Full record

SynthesisAnnals of internal medicine2024

Development and Validation of a Protein Risk Score for Mortality in Heart Failure : A Community Cohort Study.

Kayode O Kuku, Joseph J Shearer, Maryam Hashemian, Rebecca Oyetoro, Hoyoung Park, Brittany Dulek, Suzette J Bielinski, Nicholas B Larson, Peter Ganz, Daniel Levy and 3 more

Open access · greenAbstract readMeta-Analysis
In one paragraph

Synthesis in Annals of internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. 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

13 authors at 5 institutions in 1 country.

Kayode O KukuHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (K.O.K., J.J.S., M.H., R.O., H.P., V.L.R.).ORCID 0000-0003-3366-0702
Joseph J ShearerHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (K.O.K., J.J.S., M.H., R.O., H.P., V.L.R.).ORCID 0000-0002-1443-7428
Maryam HashemianHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (K.O.K., J.J.S., M.H., R.O., H.P., V.L.R.).ORCID 0000-0002-9702-9752
Rebecca OyetoroHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (K.O.K., J.J.S., M.H., R.O., H.P., V.L.R.).ORCID 0000-0002-0139-5492
Hoyoung ParkHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (K.O.K., J.J.S., M.H., R.O., H.P., V.L.R.).
Brittany DulekIntegrated Data Science Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland (B.D.).ORCID 0000-0002-6674-8009
Suzette J BielinskiDivision of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota (S.J.B.).ORCID 0000-0002-2905-5430
Nicholas B LarsonDivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota (N.B.L.).ORCID 0000-0002-3468-4215
Peter GanzZuckerberg San Francisco General Hospital, University of California, San Francisco, San Francisco, California (P.G.).ORCID 0000-0002-0437-8882
Daniel LevyLaboratory for Cardiovascular Epidemiology and Genomics, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (D.L.).
Bruce M PsatyCardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Systems and Population Health, University of Washington, Seattle, Washington (B.M.P.).
Jungnam JooOffice of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (J.J.).ORCID 0000-0001-6961-8122
Véronique L RogerHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland (K.O.K., J.J.S., M.H., R.O., H.P., V.L.R.).
National Institutes of Health · USMayo Clinic · USNational Heart Lung and Blood Institute · USSan Francisco General Hospital · USUniversity of Washington · US

Funding

Infectious Diseases Research Technologies Core - BethesdaZICAI001051 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI CHERRY, JAMES · 2009 to 2025
$226.1M
Heart failure proteomics: an epidemiology studyZIAHL006279 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI ROGER, VERONIQUE · 2022 to 2025
$8.6M
Interdisciplinary Infrastructure for Aging Research: Rochester Epidemiology ProjectR33AG058738 · NIA · MAYO CLINIC ROCHESTER · PI LEBRASSEUR, NATHAN K, OLSON, JANET E · 2020 to 2022
$2.4M
Intramural NIH HHS ZIA HL006279NIA NIH HHS R33 AG058738
6 · The paper itself

Abstract

backgroundHeart failure (HF) is a complex clinical syndrome with high mortality. Current risk stratification approaches lack precision. High-throughput proteomics could improve risk prediction. Its use in clinical practice to guide the management of patients with HF depends on validation and evidence of clinical benefit.

objectiveTo develop and validate a protein risk score for mortality in patients with HF.

designCommunity-based cohort.

settingSoutheast Minnesota.

participantsPatients with HF enrolled between 2003 and 2012 and followed through 2021. MEASUREMENTS: A total of 7289 plasma proteins in 1351 patients with HF were measured using the SomaScan Assay (SomaLogic). A protein risk score was derived using least absolute shrinkage and selection operator regression and temporal validation in patients enrolled between 2003 and 2007 (development cohort) and 2008 and 2012 (validation cohort). Multivariable Cox regression was used to examine the association between the protein risk score and mortality. The performance of the protein risk score to predict 5-year mortality risk was assessed using calibration plots, decision curves, and relative utility analyses and compared with a clinical model, including the Meta-Analysis Global Group in Chronic Heart Failure mortality risk score and

resultsThe development ( LIMITATION: Participants were predominantly of European ancestry, potentially limiting the generalizability of the findings to different patient populations.

conclusionValidation of the protein risk score demonstrated good calibration and evidence of predicted benefits to stratify the risk for death in HF superior to that of clinical methods. Further studies are needed to prospectively evaluate the score's performance in diverse populations and determine risk thresholds for interventions. PRIMARY FUNDING SOURCE: Division of Intramural Research at the National Heart, Lung, and Blood Institute of the National Institutes of Health.

Indexed as

Heart FailureAgedChronic DiseaseCohort StudiesFemaleHumansMalePrognosisRisk AssessmentRisk Factors

Identifiers

PMID38163367
PMCPMC10958437
OpenAlexW4390479992

What OpenQuestion holds

Textmetadata
LicenceTDM
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.