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Machine learning-selected variables associated with CD4 T cell recovery under antiretroviral therapy in very advanced HIV infection

Published version
Peer-reviewed

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Authors

Romero-Rodríguez, Dámaris P. 
Ramírez, Carlos 
Imaz-Rosshandler, Iván 
Ormsby, Christopher E. 
Peralta-Prado, Amy 

Abstract

Abstract: Background: A considerable portion of the HIV pandemic is composed of people under antiretroviral therapy, many of whom get a late diagnosis. Patients starting antiretroviral therapy (ART) at a very advanced stage of HIV disease attain a low recovery of CD4 T cells. Factors associated with poor recovery are incompletely described. This study aimed at finding variables associated with CD4 T cell recovery in late-presenting HIV patients. Methods: We studied a cohort of HIV+ patients initiating ART with very low basal CD4 T cell counts. We defined immune recovery as the net increase in circulating CD4 T cell counts after one year on ART. We analyzed diverse routine laboratory determinations at different times using Least Absolute Shrinkage and Selection Operator (LASSO), adaptive LASSO and Conditional Inference Random Forest. Results: CD4/CD8 ratio, % CD4 T cells and CD8 T cell counts at different times were the main recovery correlates, validated by all approaches. Unexpectedly, basal hematocrit was a consistent predictor. Additionally, week 24 creatinine had a high lasso coefficient, and alkaline phosphatase had a high conditional inference random forest coefficients, although neither was verified by other tests. Conclusions: CD4 T cell proportions are associated with CD4 T cell recovery, independently of cell counts. Inflammation-related variables could also affect reconstitution. These accessible variables may reflect underlying mechanisms and could improve the follow up of patients starting ART with an advanced HIV infection.

Description

Keywords

Research, Machine learning, HIV infections, Antiretroviral therapy, Immune reconstitution, CD4-CD8 ratio

Journal Title

Translational Medicine Communications

Conference Name

Journal ISSN

2396-832X

Volume Title

5

Publisher

BioMed Central
Sponsorship
Consejo Nacional de Ciencia y Tecnología (24011 (P-50478))
National Institute of Allergy and Infectious Diseases (AI-1288864)
Mexican Government (Comisión de Equidad y Género de las Legislaturas LX-LXI, Comisión de Igualdad de Género de la Legislatura LXII)