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2 results on '"Septic shock machine learning"'

1. 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

Author

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

Subjects
030204 cardiovascular system & hematology, Machine learning, computer.software_genre, Article, Sepsis, Omics data, 03 medical and health sciences, 0302 clinical medicine, feature selection, Septic shock, medicine, myocardial injury, Septic cardiomyopathy, 030304 developmental biology, 0303 health sciences, ddc:617, business.industry, Organ dysfunction, Généralités, General Medicine, medicine.disease, Omics, machine learning, Septic shock machine learning, Myocardial injury, Cohort, Feature selection, septic cardiomyopathy, septic shock, Medicine, Observational study, Artificial intelligence, medicine.symptom, business, computer
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., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2021

2. 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

Author

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

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., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2021

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