Background: clinically relevant methods to identify individuals at risk for impaired daily living abilities secondary to neurocognitive impairment (ADLs) remain elusive. This is especially true for complex clinical conditions such as HIV-Associated Neurocognitive Disorders (HAND). The aim of this study was to identify novel and modifiable factors that have potential to improve diagnostic accuracy of ADL risk, with the long-term goal of guiding future interventions to minimize ADL disruption. Methods: study participants included 79 people with HIV (PWH; mean age = 63; range = 55À80) enrolled in neuroHIV studies at University California San Francisco (UCSF) between 2016 and 2019. All participants were virally suppressed and exhibited objective evidence of neurocognitive impairment. ADL status was defined as either normative (n = 39) or at risk (n = 40) based on a task-based protocol. Gradient boosted multivariate regression (GBM) was employed to identify the combination of variables that differentiated ADL subgroup classification. Predictor variables included demographic factors, HIV disease severity indices, brain white matter integrity quantified using diffusion tensor imaging, cognitive test performance, and health co-morbidities. Model performance was examined using average Area Under the Curve (AUC) with repeated fivefold cross validation. Findings: the univariate GBM yielded an average AUC of 83% using Wide Range Achievement test 4 (WRAT-4) reading score, self-reported thought confusion and difficulty reading, radial diffusivity (RD) in the left external capsule, fractional anisotropy (FA) in the left cingulate gyrus, and Stroop performance. The model allowing for two-way interactions modestly improved classification performance (AUC of 88%) and revealed synergies between race, reading ability, cognitive performance, and neuroimaging metrics in the genu and uncinate fasciculus. Conversion of Neuropsychological Assessment Battery Daily Living Module (NAB-DLM) performance from raw scores into T scores amplified differences between White and non-White study participants. Interpretation: demographic and sociocultural factors are critical determinants of ADL risk status among older PWH who meet diagnostic criteria for neurocognitive impairment. Task-based ADL assessment that relies heavily on reading proficiency may artificially inflate the frequency/severity of ADL impairment among diverse clinical populations. Culturally relevant measures of ADL status are needed for individuals with acquired neurocognitive disorders, including HAND.
ObjectiveWe examined individual differences in CD4/CD8 T-cell ratio trajectories and associated risk profiles from acute HIV infection (AHI) through 144 weeks of antiretroviral therapy (ART) using a data-driven approach.MethodsA total of 483 AHI participants began ART during Fiebig I–V and completed follow-up evaluations for 144 weeks. CD4+, CD8+, and CD4/CD8 T-cell ratio trajectories were defined followed by analyses to identify associated risk variables.ResultsParticipants had a median viral load (VL) of 5.88 copies/ml and CD4/CD8 T-cell ratio of 0.71 at enrollment. After 144 weeks of ART, the median CD4/CD8 T-cell ratio was 1.3. Longitudinal models revealed five CD4/CD8 T-cell ratio subgroups: group 1 (3%) exhibited a ratio >1.0 at all visits; groups 2 (18%) and 3 (29%) exhibited inversion at enrollment, with normalization 4 and 12 weeks after ART, respectively; and groups 4 (31%) and 5 (18%) experienced CD4/CD8 T-cell ratio inversion due to slow CD4+ T-cell recovery (group 4) or high CD8+ T-cell count (group 5). Persistent inversion corresponded to ART onset after Fiebig II, higher VL, soluble CD27 and TIM-3, and lower eosinophil count. Individuals with slow CD4+ T-cell recovery exhibited higher VL, lower white blood cell count, lower basophil percent, and treatment with standard ART, as well as worse mental health and cognition, compared with individuals with high CD8+ T-cell count.ConclusionsEarly HIV disease dynamics predict unfavorable CD4/CD8 T-cell ratio outcomes after ART. CD4+ and CD8+ T-cell trajectories contribute to inversion risk and correspond to specific viral, immune, and psychological profiles during AHI. Adjunctive strategies to achieve immune normalization merit consideration.
Background: A subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-tomiddle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV. Methods:Analyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity. Results: The analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors. Conclusion: Machine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.
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