The Computational Patient has Diabetes and a COVID

TL;DR

This paper introduces a comprehensive computational model of a patient with diabetes and COVID-19, integrating multiple physiological systems to analyze disease dynamics and inform personalized treatment strategies.

Contribution

It presents a modular, extensible computational patient model that combines cardiovascular, RAS, and diabetic processes, enabling analysis of disease interactions and treatment effects.

Findings

COVID-19 and T2D significantly affect cardiovascular and inflammatory responses.

Transient dynamical responses are crucial for understanding disease states.

Guidelines for modular system design in systemic medicine are proposed.

Abstract

Medicine is moving from a curative discipline to a preventative discipline relying on personalised and precise treatment plans. The complex and multi level pathophysiological patterns of most diseases require a systemic medicine approach and are challenging current medical therapies. On the other hand, computational medicine is a vibrant interdisciplinary field that could help move from an organ-centered approach to a process-oriented approach. The ideal computational patient would require an international interdisciplinary effort, of larger scientific and technological interdisciplinarity than the Human Genome Project. When deployed, such a patient would have a profound impact on how healthcare is delivered to patients. Here we present a computational patient model that integrates, refines and extends recent mechanistic or phenomenological models of cardiovascular, RAS and diabetic processes. Our aim is twofold: analyse the modularity and composability of the model-building blocks of the computational patient and to study the dynamical properties of well-being and disease states in a broader functional context. We present results from a number of experiments among which we characterise the dynamic impact of COVID-19 and type-2 diabetes (T2D) on cardiovascular and inflammation conditions. We tested these experiments under different exercise, meal and drug regimens. We report results showing the striking importance of transient dynamical responses to acute state conditions and we provide guidelines for system design principles for the inter-relationship between modules and components in systemic medicine. Finally this initial computational Patient can be used as a toolbox for further modifications and extensions.