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Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data

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Abstract

Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived features. The sample included 101 adults with chronic HIV disease. NCI was determined using a standardized neuropsychological testing battery comprised of seven domains. MRI features included gray matter volume from high-resolution anatomical scans and white matter integrity from diffusion-weighted imaging. Clinical features included demographics, substance use, and routine laboratory tests. Least Absolute Shrinkage and Selection Operator Logistic regression was used to perform variable selection on MRI features. These features were subsequently used to train a support vector machine (SVM) to predict NCI. Three different classification tasks were performed: one used only clinical features; a second used only selected MRI features; a third used both clinical and selected MRI features. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity with a tenfold cross-validation. The SVM classifier that combined selected MRI with clinical features outperformed the model using clinical features or MRI features alone (AUC: 0.83 vs. 0.62 vs. 0.79; accuracy: 0.80 vs. 0.65 vs. 0.72; sensitivity: 0.86 vs. 0.85 vs. 0.86; specificity: 0.71 vs. 0.37 vs. 0.52). Our results provide preliminary evidence that combining clinical and MRI features can increase accuracy in predicting NCI and could be developed as a potential tool for NCI diagnosis in HIV clinical practice.

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Acknowledgements

This study was funded by grant R01-DA045565 and R25-AI140495 from the US National Institutes of Health.

Funding

National Institute on Drug Abuse (R01-DA045565) Dr. Christina S. Meade, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (R25-AI140495) Dr. Cliburn Chan.

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Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

    Yunan Xu, Ryan P. Bell, Sheri L. Towe, Tauseef Nadeem & Christina S. Meade

  2. Department of Statistical Science, Duke University, Durham, NC, USA

    Yizi Lin

  3. Center for Cognitive Neuroscience and Department of Psychology and Neuroscience, Duke University, Durham, NC, USA

    John M. Pearson

  4. Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, NC, USA

    John M. Pearson & Cliburn Chan

  5. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

    John M. Pearson

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  1. Yunan Xu
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Correspondence to Yunan Xu.

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Xu, Y., Lin, Y., Bell, R.P. et al. Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data. J. Neurovirol. 27, 1–11 (2021). https://doi.org/10.1007/s13365-020-00930-4

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  • DOI: https://doi.org/10.1007/s13365-020-00930-4

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