Title
Machine learning–based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure
Author
de Bakker, Marie (Erasmus MC)
Petersen, Teun B. (Erasmus MC)
Rueten-Budde, Anja J. (Erasmus MC)
Akkerhuis, K. Martijn (Erasmus MC)
Umans, Victor A. (Northwest Clinics)
Brugts, Jasper J. (Erasmus MC)
Germans, Tjeerd (Northwest Clinics)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics) 
Katsikis, Peter D. (Erasmus MC)
Date
2023
Abstract
Aims Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF. Methods and results In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17–3.40) and 0.66 (0.49–0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021). Conclusion Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively ‘novel’ biomarkers for prognostication.
Subject
Elastic net
Heart failure
NT-proBNP
Prediction
Proteomics
Repeated measurements
To reference this document use:
http://resolver.tudelft.nl/uuid:219bb0d8-e146-4a74-8b71-de8d01ec0020
DOI
https://doi.org/10.1093/EHJDH/ZTAD056
Source
European Heart Journal - Digital Health, 4 (6), 444-454
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2023 Marie de Bakker, Teun B. Petersen, Anja J. Rueten-Budde, K. Martijn Akkerhuis, Victor A. Umans, Jasper J. Brugts, Tjeerd Germans, M.J.T. Reinders, Peter D. Katsikis, More Authors