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Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo
Robert H Paul1, Kyu S Cho1, Andrew C Belden1
1Missouri Institute of Mental Health, University of Missouri-St. Louis, Missouri.
Insights
Machine learning accurately predicts neurocognitive trajectories in children with perinatal HIV (pHIV). Key predictors include HIV disease severity, immune markers, mental health, and hematocrit levels, enabling early risk identification.
Area of Science:
- Pediatric infectious diseases
- Neuroscience
- Machine learning applications in healthcare
Background:
- Children with perinatal HIV (pHIV) face risks of neurocognitive impairment.
- Predicting neurocognitive trajectories is crucial for timely intervention.
Purpose of the Study:
- To develop a predictive model for neurocognitive trajectories in children with pHIV.
- Identify key predictors of neurocognitive development in this population.
Main Methods:
- Machine learning (Gradient-boosted multivariate regressions) applied to longitudinal data from 285 children with pHIV.
- Analysis included demographic, clinical, immune, mental health, and complete blood count (CBC) data.
- Models were validated using five-fold cross-validation.
Main Results:
- Baseline models achieved an average AUC of 79%, identifying HIV severity and immune markers as key predictors.
- Longitudinal models improved predictive accuracy to 87-90% AUC.
- Mental health problems and hematocrit levels emerged as significant longitudinal predictors, with novel interactions identified.
Conclusions:
- Machine learning is feasible for identifying children with pHIV at risk for neurocognitive difficulties.
- Interactions between HIV disease and mental health are early indicators of later neurocognitive challenges.
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.
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