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Application of an Exploratory Knowledge-Discovery Pipeline Based on Machine Learning to Multi-Scale OMICS Data to Characterise Myocardial Injury in a Cohort of Patients with Septic Shock: An Observational Study

Contributors

Bollen Pinto, Bernardo; Ribas Ripoll, Vicent; Subías-Beltrán, Paula; Herpain, Antoine; Barlassina, Cristina; Oliveira, Eliandre; Pastorelli, Roberta; Braga, Daniele; Barcella, Matteo; Subirats, Laia; Bauzá-Martinez, Julia; Odena, Antonia; Ferrario, Manuela; Baselli, Giuseppe; Aletti, Federico; Bendjelid, Karim; Shockomics Consortium

Published in

Journal of clinical medicine, vol. 10, no. 19, 4354

Publication date

2021

First online date

2021-09-24

Abstract

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction.

Keywords

  • Feature selection
  • Machine learning
  • Myocardial injury
  • Septic cardiomyopathy
  • Septic shock

UNIGE affiliation entities

UNIGE research groups

Funding

  • European Commission - MULTISCALE APPROACH TO THE IDENTIFICATION OF MOLECULAR BIOMARKERS IN ACUTE HEART FAILURE INDUCED BY SHOCK [602706]

Citation (ISO format)

BOLLEN PINTO, Bernardo et al. Application of an Exploratory Knowledge-Discovery Pipeline Based on Machine Learning to Multi-Scale OMICS Data to Characterise Myocardial Injury in a Cohort of Patients with Septic Shock: An Observational Study. In: Journal of clinical medicine, 2021, vol. 10, n° 19, p. 4354. doi: 10.3390/jcm10194354

Main files (1)

Article (Published version)
CC BY-4.0

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Appendix

Identifiers

Additional URL for this publication

https://www.mdpi.com/2077-0383/10/19/4354

Journal ISSN

2077-0383
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Technical informations

Creation

15/10/2021 13:59:00

First validation

15/10/2021 13:59:00

Update

13/10/2025 15:40:10

Status update

16/03/2023 02:29:42

Last indexation

10/11/2026 17:44:27
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