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Abstract Details

Predictive Models for Neurocognitive Decline in HIV+ Youth in Zambia Using a Machine Learning Approach
General Neurology
S11 - Global Health and Neuroepidemiology (11:39 AM-11:51 AM)
003

An estimated 66,000 children in Zambia are HIV-infected. Despite combination antiretroviral therapy (cART), HIV-associated neurocognitive disorders (HAND) remain a significant complication. Data-driven machine learning tools can help elucidate factors that predict cognitive decline and can support clinical interventions for HAND in Zambia.

1. Develop machine learning models to predict neurocognitive decline in HIV-infected children in Zambia.

2. Compare standard regression-based (SRB) and group-based trajectory modeling (GBTM) techniques for cognitive decline using data-driven approaches.

This is a sub-study of the HIV-Associated Neurocognitive Disorders in Zambia (HANDZ) longitudinal prospective study. Data from 208 perinatally infected HIV+ children and 208 HIV-exposed, uninfected controls over a 2-year period were used to train logistic regression with LASSO regularization (LR), random forests (RF) and support vector machine (SVM) algorithms. Cognitive status was the outcome of interest assessed via a comprehensive neuropsychological testing battery and modelled using SRB and GBTM. Model performance was measured as area under the receiver operating characteristic curve (AUC-ROC).

With SRB modeling, LR performed the best (AUC = 0.795) on average followed by SVM (AUC = 0.790) and RF (AUC = 0.755) while with GBTM, LR performed the best (AUC = 0.743) followed by RF (AUC = 0.709) and SVM (AUC = 0.673). There were no statistically significant differences in performance (p > 0.05) between the two modeling techniques. The addition of HIV-specific variables along with non-specific features improved model performance. Worst recorded WHO stage, CD4 counts, and nadir CD4 counts were the most predictive HIV-specific factors while school performance, height and weight percentiles, grade, history of stunting and socioeconomic status index were the most predictive non-HIV specific variables.

Machine learning can help elucidate factors that predict neurocognitive decline in HIV+ vs. HEU youth in Zambia. Incorporating imaging and inflammatory biomarkers might improve these models.
Authors/Disclosures
Mohammed Mehdi Shahid
PRESENTER
Mr. Shahid has nothing to disclose.
Gauri Patil (Ichan School of Medicine at Mount Sinai) Ms. Patil has nothing to disclose.
Esau Mbewe Esau G. Mbewe has received research support from Research was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number K23NS117310. .
No disclosure on file
No disclosure on file
Alexandra Buda Ms. Buda has nothing to disclose.
No disclosure on file
Heather Adams The institution of Heather Adams has received research support from Current: NIH; Past: Abeona; Batten Research Alliance; American University Centers on Disabilities. An immediate family member of Heather Adams has received publishing royalties from a publication relating to health care. Heather Adams has received personal compensation in the range of $500-$4,999 for serving as a Consultant with Critical Path Institute.
No disclosure on file
Gretchen Birbeck (University of Rochester/CHET) An immediate family member of Dr. Birbeck has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Various. Dr. Birbeck has a non-compensated relationship as a Ambassador for Zambia with RSTMH that is relevant to AAN interests or activities.
David Bearden (University of Rochester School of Medicine) Dr. Bearden has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biogen. Dr. Bearden has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Praxis. Dr. Bearden has received personal compensation in the range of $100,000-$499,999 for serving as an Expert Witness for law firms.