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.

HIV 相关神经认知障碍 (NCI) 的诊断仍然是一个临床挑战。本研究的目的是利用临床和磁共振成像 (MRI) 衍生的特征，开发 HIV 感染者 NCI 的预测模型。样本包括 101 名患有慢性 HIV 疾病的成年人。 NCI 是使用由七个领域组成的标准化神经心理学测试组来确定的。 MRI 特征包括高分辨率解剖扫描的灰质体积和扩散加权成像的白质完整性。临床特征包括人口统计、物质使用和常规实验室测试。最小绝对收缩和选择算子 Logistic 回归用于对 MRI 特征进行变量选择。这些特征随后被用来训练支持向量机 (SVM) 来预测 NCI。执行了三种不同的分类任务：一种仅使用临床特征；另一种仅使用临床特征。第二个仅使用选定的 MRI 特征；第三个使用临床和选定的 MRI 特征。模型性能通过十倍交叉验证的受试者工作特征曲线下面积（AUC）、准确性、敏感性和特异性进行评估。将选定的 MRI 与临床特征相结合的 SVM 分类器优于单独使用临床特征或 MRI 特征的模型（AUC：0.83 vs. 0.62 vs. 0.79；准确度：0.80 vs. 0.65 vs. 0.72；敏感性：0.86 vs. 0.85 vs. 0.86 ；特异性：0.71 vs. 0.37 vs. 0.52)。我们的结果提供了初步证据，表明结合临床和 MRI 特征可以提高预测 NCI 的准确性，并可以开发为 HIV 临床实践中 NCI 诊断的潜在工具。