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Tytuł:
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Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence.
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Autorzy:
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Kamal S; Community pharmacy, School of pharmaceutical sciences, University of Geneva, University of Lausanne, Lausanne, Switzerland.; Community pharmacy, Department of ambulatory care & community medicine, University of Lausanne, Lausanne, Switzerland.; Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.; University of California Institute for Prediction Technology, University of California, Los Angeles, CA, USA.
Urata J; Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.; University of California Institute for Prediction Technology, University of California, Los Angeles, CA, USA.
Cavassini M; Infectious Disease Service, Lausanne university hospital, University of Lausanne, Lausanne, Switzerland.
Liu H; Division of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.; Department of Medicine, Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, CA, USA.
Kouyos R; Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
Bugnon O; Community pharmacy, School of pharmaceutical sciences, University of Geneva, University of Lausanne, Lausanne, Switzerland.; Community pharmacy, Department of ambulatory care & community medicine, University of Lausanne, Lausanne, Switzerland.
Wang W; University of California Institute for Prediction Technology, University of California, Los Angeles, CA, USA.; Department of Computer Science, University of California, Los Angeles, CA, USA.
Schneider MP; Community pharmacy, School of pharmaceutical sciences, University of Geneva, University of Lausanne, Lausanne, Switzerland.; Community pharmacy, Department of ambulatory care & community medicine, University of Lausanne, Lausanne, Switzerland.
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Źródło:
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AIDS care [AIDS Care] 2021 Apr; Vol. 33 (4), pp. 530-536. Date of Electronic Publication: 2020 Apr 08.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: London : Informa Healthcare
Original Publication: Abingdon, Oxfordshire, U.K. : Carfax Pub. Co., c1989-
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MeSH Terms:
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Machine Learning*
Anti-HIV Agents/*therapeutic use
Antiretroviral Therapy, Highly Active/*methods
HIV Infections/*drug therapy
Medication Adherence/*statistics & numerical data
Viral Load/*drug effects
Adult ; Algorithms ; CD4 Lymphocyte Count ; Cohort Studies ; Female ; HIV Infections/epidemiology ; HIV Infections/psychology ; Humans ; Male ; Medication Adherence/psychology ; Retrospective Studies ; Switzerland/epidemiology ; Treatment Outcome
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Contributed Indexing:
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Keywords: AIV/AIDS; Antiretroviral adherence; machine learning; medication adherence; methods
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Substance Nomenclature:
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0 (Anti-HIV Agents)
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Entry Date(s):
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Date Created: 20200409 Date Completed: 20210426 Latest Revision: 20210426
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Update Code:
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20240105
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DOI:
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10.1080/09540121.2020.1751045
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PMID:
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32266825
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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.
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