Incorporating temporal dynamics of mutations to enhance the prediction
  capability of antiretroviral therapy's outcome for HIV-1
Giulia Di Teodoro Martin Pirkl Francesca Incardona Ilaria Vicenti Anders Sönnerborg Rolf Kaiser Laura Palagi Maurizio Zazzi Thomas Lengauer
Motivation: In predicting HIV therapy outcomes, a critical clinical question
is whether using historical information can enhance predictive capabilities
compared with current or latest available data analysis. This study analyses
whether historical knowledge, which includes viral mutations detected in all
genotypic tests before therapy, their temporal occurrence, and concomitant
viral load measurements, can bring improvements. We introduce a method to weigh
mutations, considering the previously enumerated factors and the reference
mutation-drug Stanford resistance tables. We compare a model encompassing
history (H) with one not using it (NH). Results: The H-model demonstrates
superior discriminative ability, with a higher ROC-AUC score (76.34%) than the
NH-model (74.98%). Significant Wilcoxon test results confirm that incorporating
historical information improves consistently predictive accuracy for treatment
outcomes. The better performance of the H-model might be attributed to its
consideration of latent HIV reservoirs, probably obtained when leveraging
historical information. The findings emphasize the importance of temporal
dynamics in mutations, offering insights into HIV infection complexities.
However, our result also shows that prediction accuracy remains relatively high
even when no historical information is available. Supplementary information:
Supplementary material is available.