The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn's Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?

Daniele Bottigliengo,Dario Gregori,Corrado Lanera,Matteo Martinato,Danila Azzolina,Paola Berchialla,Daniela Giachino,Ileana Baldi,Giulia Lorenzoni

doi:10.3390/jcm8060865

Daniele Bottigliengo, Dario Gregori + Show 7 more

Open Access

https://doi.org/10.3390/jcm8060865

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Abstract

(1) Background: The high heterogeneity of inflammatory bowel disease (IBD) makes the study of this condition challenging. In subjects affected by Crohn’s disease (CD), extra-intestinal manifestations (EIMs) have a remarkable potential impact on health status. Increasing numbers of patient characteristics and the small size of analyzed samples make EIMs prediction very difficult. Under such constraints, Bayesian machine learning techniques (BMLTs) have been proposed as a robust alternative to classical models for outcome prediction. This study aims to determine whether BMLT could improve EIM prediction and statistical support for the decision-making process of clinicians. (2) Methods: Three of the most popular BMLTs were employed in this study: Naϊve Bayes (NB), Bayesian Network (BN) and Bayesian Additive Regression Trees (BART). They were applied to a retrospective observational Italian study of IBD genetics. (3) Results: The performance of the model is strongly affected by the features of the dataset, and BMLTs poorly classify EIM appearance. (4) Conclusions: This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice.

Highlights

Material and Methods rohn’s disease; extra-iTnhteestdinaatalsemtaenmifpeslotayteiodni;nritshkis psrteuddiyctiiosnt;heBasyameseiansetmuestehdodins; Giachino et al [14]. It refers to ing techniques an observational study on the role of genetic factors for IntroduIncfltiaomnmatory bowel disease (IBD)
The ability to correctly classify the presence or absence of EIMs was evaluated by comparing several indicators: Somers’ Dxy (Somers’ D), positive and negative predicted values of model predictions (PPV and NPV, respectively), overall misclassification error (MCR, which corresponds to the percentage of observations wrongly classified) and the area under the ROC curve (AUC)
Two analyses were performed: one including only the first group of potential explanatory variables as covariates, and one including genetic variables. Such a strategy was conducted to evaluate the impact that genetic factors have on the overall ability of the models to predict the presence or absence of EIMs

Summary

Introduction

Material and Methods rohn’s disease; extra-iTnhteestdinaatalsemtaenmifpeslotayteiodni;nritshkis psrteuddiyctiiosnt;heBasyameseiansetmuestehdodins; Giachino et al [14] It refers to ing techniques an observational study on the role of genetic factors for IBD. The graphical structure of a BN can be implemented by imposing the relationships between nodes with expert opinions on the phenomenon under study or by defining the connections between the variables with learning algorithms. BART is able to provide information on the probability of occurrence of an event of interest given subject characteristics by flexibly relating the clinical endpoint to the potential explanatory variables. NB and BN are both graphical models The former imposes a fixed structure on the network and, despite its strong unrealistic assumptions, it has performed very well in several situations [32].

Statistical Analysis

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Discussion