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Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary
Ritabrata Dutta1, Karim Zouaoui Boudjeltia2, Christos Kotsalos3
1University of Warwick, United Kingdom.
Insights
This study introduces a new model for cardio/cerebrovascular diseases (CVD) detection, improving accuracy by analyzing platelet behavior. This personalized approach offers better diagnostics and treatment strategies for patients.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Cardiovascular Research
Background:
- Cardio/cerebrovascular diseases (CVD) pose significant health challenges.
- Current pathology tests for CVD are limited, failing to account for platelet activation dynamics and inter-individual variability.
Purpose of the Study:
- To develop a novel stochastic platelet deposition model for enhanced CVD detection.
- To create an inferential scheme for estimating biologically meaningful model parameters.
- To enable personalized pathology testing and treatment for CVD.
Main Methods:
- Developed a stochastic platelet deposition model.
- Employed approximate Bayesian computation with a discriminating summary statistic for parameter inference.
- Collected and analyzed data from healthy volunteers and diverse patient groups.
Main Results:
- Successfully inferred specific biological parameters differentiating patient types.
- Identified biological reasoning behind dysfunction in various patient cohorts.
- Demonstrated the model's capability to capture inter-individual variability in platelet dynamics.
Conclusions:
- The proposed model and inferential scheme offer a pathway to personalized CVD pathology tests.
- This approach allows for a deeper understanding of the biological basis of CVD in individual patients.
- Opens new avenues for targeted medical treatments for cardio/cerebrovascular diseases.
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.
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