Predictive graphical model, network-based medical tool for the prognosis of chronic hepatitis C patients treated with peg-interferon plus ribavirin.
BACKGROUND
There are few model networks to predict treatment outcome in viral hepatitis.


OBJECTIVE
To develop an easy bioinformatics platform based on algorithm decisions (Bayesian network) for a more efficient prediction of treatment response.


METHODS
Totally 385 consecutive chronic hepatitis C (CHC) treated patients were included. More than 40 variables were analysed. Data from 308 patients were used to build the variable model network using DLIFE platform based on predictive graphical models. The prediction accuracy of the bioinformatics network was compared with the true data collected in a retrospective study. The model was then validated twice with external data from CHC patients treated in other hospitals.


RESULTS
The accuracy of this bioinformatics network for treatment response in our 308 patients was 83.3%, which is higher than the accuracy obtained by physicians on the basis of study of clinical data and their own experience (50-65%). The receiver operator characteristic curve areas after validation with another cohort of patients were: 0.91 for sustained virological response, one for nonresponse, and 0.81 for relapse. DLIFE offered a diagnostic accuracy of 81.3%, which is a clear improvement compared with unassisted prognosis (50-65%).


CONCLUSIONS
This bioinformatics platform (DLIFE) accurately predicts the outcome of CHC combination therapy, improving treatment decisions and reducing costs. This bioinformatics platform allows integrating widespread data sources and permits predicting the clinical outcome of a particular patient using a general predictive graphical model.
Trapero-Marugan
M
M
Gastroenterology-Hepatology Service and CIBEREHD, Hospital Universitario de La Princesa, Universidad Autónoma de Madrid, Madrid, Spain.
Marin
M
M
Pivel
J P
JP
Del Rio
J M
JM
Nunez
O
O
Clemente
G
G
Gisbert
J P
JP
Moreno-Otero
R
R
D000465:Algorithms; D000998:Antiviral Agents / Q000627:therapeutic use*; D019295:Computational Biology / Q000191:economics / Q000379:methods*; D003661:Decision Support Techniques; D004359:Drug Therapy, Combination; D005260:Female; D019698:Hepatitis C, Chronic / Q000188:drug therapy*; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D011092:Polyethylene Glycols; D011237:Predictive Value of Tests; D012372:ROC Curve; D011994:Recombinant Proteins; D015203:Reproducibility of Results; D012254:Ribavirin / Q000627:therapeutic use*; D016896:Treatment Outcome
D000998:Antiviral Agents; D000077190:Interferon alpha-2; D016898:Interferon-alpha; D011994:Recombinant Proteins; D011092:Polyethylene Glycols; D012254:Ribavirin; C417083:peginterferon alfa-2b