A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing.

ABSTRACT:

Introduction

Physicians are often required to make treatment decisions for patients with Crohn's disease on the basis of limited objective information about the state of the patient's gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn's disease with a responder classifier to predict temporal changes in mucosal health.

Methods

A hybrid mechanistic-statistical platform was developed to predict biomarker and tissue health time courses in patients with Crohn's disease. Eligible patients from the VERSIFY study (n = 69) were classified into archetypical response cohorts using a decision tree based on early treatment data

SUBMITTER: Venkatapurapu SP 

PROVIDER: S-EPMC9239932 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature