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Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.

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Titel: Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.
Autoren: Dutta, Ritabrata, Zouaoui Boudjeltia, Karim, Kotsalos, Christos, Rousseau, Alexandre, Ribeiro de Sousa, Daniel, Desmet, Jean-Marc, Van Meerhaeghe, Alain, Mira, Antonietta, Chopard, Bastien
Quelle: PLoS Computational Biology; 3/10/2022, Vol. 18 Issue 3, p1-20, 20p, 4 Diagrams, 3 Charts, 4 Graphs
Schlagwörter: CHRONIC obstructive pulmonary disease, BLOOD platelets, STATISTICAL learning, CEREBROVASCULAR disease, BLOOD platelet activation, PATHOLOGY
Abstract: Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment. Author summary: Cardiovascular accidents often result from blood deficiencies, such as platelets dysfunction. Current diagnosis techniques to detect such dysfunctions are not sufficiently accurate and unable to determine which platelet properties are affected. We develop a novel approach to describe in-vitro platelets deposition patterns in terms of clinically meaningful patient specific bio-physical quantities that allow for personalized clinical diagnostics. This approach combines mathematical modeling, statistical inference techniques, machine learning and high performance computation to estimate the values of these clinically relevant platelet properties. We demonstrate our approach on three classes of donors, healthy volunteers, patients subject to dialysis and patients with chronic obstructive pulmonary disease. We claim that our approach opens a paradigm shift for the treatment and diagnosis of cardiovascular diseases, leading to personalized medicine. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
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Abstract:Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment. Author summary: Cardiovascular accidents often result from blood deficiencies, such as platelets dysfunction. Current diagnosis techniques to detect such dysfunctions are not sufficiently accurate and unable to determine which platelet properties are affected. We develop a novel approach to describe in-vitro platelets deposition patterns in terms of clinically meaningful patient specific bio-physical quantities that allow for personalized clinical diagnostics. This approach combines mathematical modeling, statistical inference techniques, machine learning and high performance computation to estimate the values of these clinically relevant platelet properties. We demonstrate our approach on three classes of donors, healthy volunteers, patients subject to dialysis and patients with chronic obstructive pulmonary disease. We claim that our approach opens a paradigm shift for the treatment and diagnosis of cardiovascular diseases, leading to personalized medicine. [ABSTRACT FROM AUTHOR]
ISSN:1553734X
DOI:10.1371/journal.pcbi.1009910