ArticleClinical proteomics2018
Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure.
Article in Clinical proteomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Decoding the Heart Failure Peptidome.Circulation. Heart failure · 2026Article
- Heart failure with improved versus persistently reduced left ventricular ejection fraction: A comparison of the BIOSTAT-CHF (European) study with the ASIAN-HF registry.European journal of heart failure · 2024Article
- Proteome-wide Characterization and Pathophysiology Correlation in Non-ischemic Cardiomyopathies.Korean circulation journal · 2024Article
- Finding a Needle in a Haystack: Proteomics in Heart Failure.JACC. Basic to translational science · 2020Review
- Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets.European journal of heart failure · 2020Article
- Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
22 authors at 13 institutions in 9 countries.
Funding
Abstract
backgroundCurrent risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.
methodsA cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.
resultsAfter normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005).
conclusionsThe results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.
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