Robert H Paul1, Kyu S Cho1, Andrew C Belden1, Claude A Mellins2, Kathleen M Malee3, Reuben N Robbins2, Lauren E Salminen4, Stephen J Kerr5,6, Badri Adhikari7, Paola M Garcia-Egan1, Jiratchaya Sophonphan2, Linda Aurpibul8, Kulvadee Thongpibul9, Pope Kosalaraksa10, Suparat Kanjanavanit11, Chaiwat Ngampiyaskul12, Jurai Wongsawat13, Saphonn Vonthanak14, Tulathip Suwanlerk5,15, Victor G Valcour16, Rebecca N Preston-Campbell1, Jacob D Bolzenious1, Merlin L Robb17, Jintanat Ananworanich17,18, Thanyawee Puthanakit5,19. 1. Missouri Institute of Mental Health, University of Missouri-St. Louis, Missouri. 2. HIV Center for Clinical and Behavioral Studies, New York State Psychiatric Institute, and Columbia University, New York. 3. Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine, Chicago, Illinois. 4. Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, California, USA. 5. HIV Netherlands Australia Thailand (HIV-NAT) Research Collaboration, Thai Red Cross AIDS Research Center. 6. Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. 7. Department of Mathematics and Computer Science, University of Missouri-St. Louis, Missouri, USA. 8. Research Institute for Health Sciences. 9. Department of Psychology, Faculty of Humanities, Chiang Mai University, Chiang Mai. 10. Department of Pediatrics, Faculty of Medicine, Khon Kaen University, Khon Kaen. 11. Nakornping Hospital, Chiang Mai. 12. Prapokklao Hospital, Chanthaburi. 13. Bamrasnaradura Infectious Diseases Institute, Nonthaburi, Thailand. 14. University of Health Science, Phnom Penh, Cambodia. 15. TREAT Asia, amfAR - The Foundation for AIDS Research, Bangkok, Thailand. 16. Memory and Aging Center, Department of Neurology, University of California, San Francisco, California. 17. Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA. 18. Department of Global Health, University of Amsterdam, Amsterdam, The Netherlands. 19. Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Abstract
OBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV). DESIGN: Machine learning analysis of baseline and longitudinal predictors derived from clinical measures utilized in pediatric HIV. METHODS: Two hundred and eighty-five children (ages 2-14 years at baseline; Mage = 6.4 years) with pHIV in Southeast Asia underwent neurocognitive assessment at study enrollment and twice annually thereafter for an average of 5.4 years. Neurocognitive slopes were modeled to establish two subgroups [above (n = 145) and below average (n = 140) trajectories). Gradient-boosted multivariate regressions (GBM) with five-fold cross validation were conducted to examine baseline (pre-ART) and longitudinal predictive features derived from demographic, HIV disease, immune, mental health, and physical health indices (i.e. complete blood count [CBC]). RESULTS: The baseline GBM established a classifier of neurocognitive group designation with an average AUC of 79% built from HIV disease severity and immune markers. GBM analysis of longitudinal predictors with and without interactions improved the average AUC to 87 and 90%, respectively. Mental health problems and hematocrit levels also emerged as salient features in the longitudinal models, with novel interactions between mental health problems and both CD4 cell count and hematocrit levels. Average AUCs derived from each GBM model were higher than results obtained using logistic regression. CONCLUSION: Our findings support the feasibility of machine learning to identify children with pHIV at risk for suboptimal neurocognitive development. Results also suggest that interactions between HIV disease and mental health problems are early antecedents to neurocognitive difficulties in later childhood among youth with pHIV.
OBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV). DESIGN: Machine learning analysis of baseline and longitudinal predictors derived from clinical measures utilized in pediatric HIV. METHODS: Two hundred and eighty-five children (ages 2-14 years at baseline; Mage = 6.4 years) with pHIV in Southeast Asia underwent neurocognitive assessment at study enrollment and twice annually thereafter for an average of 5.4 years. Neurocognitive slopes were modeled to establish two subgroups [above (n = 145) and below average (n = 140) trajectories). Gradient-boosted multivariate regressions (GBM) with five-fold cross validation were conducted to examine baseline (pre-ART) and longitudinal predictive features derived from demographic, HIV disease, immune, mental health, and physical health indices (i.e. complete blood count [CBC]). RESULTS: The baseline GBM established a classifier of neurocognitive group designation with an average AUC of 79% built from HIV disease severity and immune markers. GBM analysis of longitudinal predictors with and without interactions improved the average AUC to 87 and 90%, respectively. Mental health problems and hematocrit levels also emerged as salient features in the longitudinal models, with novel interactions between mental health problems and both CD4 cell count and hematocrit levels. Average AUCs derived from each GBM model were higher than results obtained using logistic regression. CONCLUSION: Our findings support the feasibility of machine learning to identify children with pHIV at risk for suboptimal neurocognitive development. Results also suggest that interactions between HIV disease and mental health problems are early antecedents to neurocognitive difficulties in later childhood among youth with pHIV.
Authors: Robert H Paul; Kyu S Cho; Patrick Luckett; Jeremy F Strain; Andrew C Belden; Jacob D Bolzenius; Jaimie Navid; Paola M Garcia-Egan; Sarah A Cooley; Julie K Wisch; Anna H Boerwinkle; Dimitre Tomov; Abel Obosi; Julie A Mannarino; Beau M Ances Journal: J Acquir Immune Defic Syndr Date: 2020-08-01 Impact factor: 3.731
Authors: Robert H Paul; Kyu Cho; Andrew Belden; Adam W Carrico; Eileen Martin; Jacob Bolzenius; Patrick Luckett; Sarah A Cooley; Julie Mannarino; Jodi M Gilman; Mariah Miano; Beau M Ances Journal: J Neuroimmune Pharmacol Date: 2022-01-04 Impact factor: 4.147
Authors: Yang Xiang; Kayo Fujimoto; Fang Li; Qing Wang; Natascha Del Vecchio; John Schneider; Degui Zhi; Cui Tao Journal: AIDS Date: 2021-05-01 Impact factor: 4.632