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A Machine Learning Approach to Predict Weight Change in ART-Experienced People Living With HIV

Lookup NU author(s): Professor Paolo MissierORCiD, Professor Federica Mandreoli

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Abstract

Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.INTRODUCTION: The objective of the study was to develop machine learning (ML) models that predict the percentage weight change in each interval of time in antiretroviral therapy-experienced people living with HIV. METHODS: This was an observational study that comprised consecutive people living with HIV attending Modena HIV Metabolic Clinic with at least 2 visits. Data were partitioned in an 80/20 training/test set to generate 10 progressively parsimonious predictive ML models. Weight gain was defined as any weight change >5%, at the next visit. SHapley Additive exPlanations values were used to quantify the positive or negative impact of any single variable included in each model on the predicted weight changes. RESULTS: A total of 3,321 patients generated 18,322 observations. At the last observation, the median age was 50 years and 69% patients were male. Model 1 (the only 1 including body composition assessed with dual-energy x-ray absorptiometry) had an accuracy greater than 90%. This model could predict weight at the next visit with an error of <5%. CONCLUSIONS: ML models with the inclusion of body composition and metabolic and endocrinological variables had an excellent performance. The parsimonious models available in standard clinical evaluation are insufficient to obtain reliable prediction, but are good enough to predict who will not experience weight gain.

Publication metadata

Author(s): Motta F, Milic J, Gozzi L, Belli M, Sighinolfi L, Cuomo G, Carli F, Dolci G, Iadisernia V, Burastero G, Mussini C, Missier P, Mandreoli F, Guaraldi G

Publication type: Article

Publication status: Published

Journal: Journal of Acquired Immune Deficiency Syndromes

Year: 2023

Volume: 94

Issue: 5

Pages: 474-481

Print publication date: 12/12/2023

Acceptance date: 02/04/2023

ISSN (print): 1525-4135

ISSN (electronic): 1944-7884

Publisher: Lippincott Williams & Wilkins

URL: https://doi.org/10.1097/QAI.0000000000003302

DOI: 10.1097/QAI.0000000000003302

PubMed id: 37949448

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