Artificial neural networks to predict virological and immunological success in HIV patients under antiretroviral therapy from a nationwide cohort in Colombia, using the SISCAC database
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Objective
This study aimed to develop predictive models both for viral suppression and immunological reconstitution using a standard set of reported variables in a nationwide database system (SISCAC) from a cohort of patients living with HIV in Colombia.
Materials and Methods
We included 2.182 patients with no missing data related to the outcomes of interest, during a 12 month follow up period. We randomly assigned a 0,7 proportion of this cohort to de training dataset for 2 different predictive models (logistic regression, artificial neural networks). The AUC/ROC results were compared with those obtained through the construction of artificial neural networks with the specified parameters.
Results
From a cohort of 2182 patients, 85,79% were male and at HIV diagnosis, the mean value of the CD4 count was 342 x mm3. The logistic regression models obtained AUC/ROC accuracy for the outcomes “suppressed viral load” 0,7, “undetectable viral load” of 0,66 and “immunological reconstitution” 0,83; whereas the artificial neural network perceptron multilayer obtained AUC/ROC of 0,77, 0.69 and 0,87 for the same outcomes.
Conclusions
The selection of specific variables from a nationwide database in Colombia with quality control purposes allowed us to generate predictive models with an initial evaluation of performance regarding three predefined outcomes for virological and immunological success.
