Evidence map›Paper›PMID 30760105›Full record

ArticleJournal of the American Heart Association2019

Temporal Patterns of 14 Blood Biomarker candidates of Cardiac Remodeling in Relation to Prognosis of Patients With Chronic Heart Failure-The Bio- SH i FT Study.

Elke Bouwens, Milos Brankovic, Henk Mouthaan, Sara Baart, Dimitris Rizopoulos, Nick van Boven, Kadir Caliskan, Olivier Manintveld, Tjeerd Germans, Jan van Ramshorst and 3 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of the American Heart Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed, 1 synthesis or guideline 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.

Elke Bouwens1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Milos Brankovic1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Henk Mouthaan2 Olink Proteomics Uppsala Sweden.
Sara Baart1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Dimitris Rizopoulos3 Department of Biostatistics Erasmus MC Rotterdam the Netherlands.
Nick van Boven1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Kadir Caliskan1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Olivier Manintveld1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Tjeerd Germans4 Department of Cardiology Northwest Clinics Alkmaar the Netherlands.
Jan van Ramshorst4 Department of Cardiology Northwest Clinics Alkmaar the Netherlands.
Victor Umans4 Department of Cardiology Northwest Clinics Alkmaar the Netherlands.
K Martijn Akkerhuis1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.
Isabella Kardys1 Department of Cardiology Erasmus MC Rotterdam the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Remodeling biomarkers carry high potential for predicting adverse events in chronic heart failure ( CHF ) patients. However, temporal patterns during the course of CHF , and especially the trajectory before an adverse event, are unknown. We studied the prognostic value of temporal patterns of 14 cardiac remodeling biomarker candidates in stable patients with CHF from the Bio-SHiFT (Serial Biomarker Measurements and New Echocardiographic Techniques in Chronic Heart Failure Patients Result in Tailored Prediction of Prognosis) study. Methods and Results In 263 CHF patients, we performed trimonthly blood sampling during a median follow-up of 2.2 years. For the analysis, we selected all baseline samples, the 2 samples closest to the primary end point ( PE ), or the last sample available for end point-free patients. Thus, in 567 samples, we measured suppression of tumorigenicity-2, galectin-3, galectin-4, growth differentiation factor-15, matrix metalloproteinase-2, 3, and 9, tissue inhibitor metalloproteinase-4, perlecan, aminopeptidase-N, caspase-3, cathepsin-D, cathepsin-Z, and cystatin-B. The PE was a composite of cardiovascular mortality, heart transplantation, left ventricular assist device implantation, and HF hospitalization. Associations between repeatedly measured biomarker candidates and the PE were investigated by joint modeling. Median age was 68 (interquartile range: 59-76) years with 72% men; 70 patients reached the PE . Repeatedly measured suppression of tumorigenicity-2, galectin-3, galectin-4, growth differentiation factor-15, matrix metalloproteinase-2 and 9, tissue inhibitor metalloproteinase-4, perlecan, cathepsin-D, and cystatin-B levels were significantly associated with the PE , and increased as the PE approached. The slopes of biomarker trajectories were also predictors of clinical outcome, independent of their absolute level. Associations persisted after adjustment for clinical characteristics and pharmacological treatment. Suppression of tumorigenicity-2 was the strongest predictor (hazard ratio: 7.55 per SD difference, 95% CI : 5.53-10.30), followed by growth differentiation factor-15 (4.06, 2.98-5.54) and matrix metalloproteinase-2 (3.59, 2.55-5.05). Conclusions Temporal patterns of remodeling biomarker candidates predict adverse clinical outcomes in CHF . Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 01851538.

Indexed as

Ventricular RemodelingAgedAged, 80 and overBiomarkersFemaleFollow-Up StudiesHeart FailureHumansMaleMiddle AgedNetherlandsPrognosisRetrospective StudiesSurvival RateTime FactorsBiomarkersbiomarkerscardiac remodelingheart failureprognosisrepeated measurements

Identifiers

PMID30760105
PMCPMC6405680

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.