Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence

ABSTRACT

Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional medication adherence program (IMAP). Sociodemographic and clinical variables were retrieved from the Swiss HIV Cohort Study (SHCS). Daily electronic medication adherence between 2008–2016 were analyzed retrospectively. Predictor variables included: RNA viral load (VL), CD4 count, duration of ART, and adherence. Random Forest, was used with 10 fold cross validation to predict the RNA class for each data observation. Classification accuracy metrics were calculated for each of the 10-fold cross validation holdout datasets. The values for each range from 0 to 1 (better accuracy). 383 HIV+ patients, 56% male, 52% white, median (Q1, Q3): age 43 (36, 50), duration of electronic monitoring of adherence 564 (200, 1333) days, CD4 count 406 (209, 533) cells/mm3, time since HIV diagnosis was 8.4 (4, 13.5) years, were included. Average model classification accuracy metrics (AUC and F1) for RNA VL were 0.6465 and 0.7772, respectively. In conclusion, combining adherence with other clinical predictors improve predictions of RNA.

KEYWORDS:

• Antiretroviral adherence
• medication adherence
• machine learning
• methods
• AIV/AIDS

Acknowledgments

The authors would like to thank Prof. Sean D. Young for hosting the first author during her mobility fellowship in the United States in his group and providing logistical support to help realize this project. SK wrote the initial version of the manuscript and all authors cooperated towards the final version. SK and JU analyzed the data. WW supervised the data analysis. HL and RK provided insights on the data analysis and manuscript writing. MC, MPS provided HIV clinical and adherence expertise. MC, MPS and OBU provided the data through their institutions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The study was supported by grant number P1GEP3-171705 from the Swiss National Science Foundation (SNSF) and grants from the Swiss National Science Foundation to the Swiss HIV Cohort Study (SHCS Project 808, grant number 148522).