Olfactory loss, motor impairment, anxiety/depression, and REM-sleep behaviour disorder (RBD) are prodromal Parkinson’s disease (PD) features. PD risk prediction models typically dichotomize test results and apply likelihood ratios (LRs) to scores above and below cut-offs. We investigate whether LRs for specific test values could enhance classification between PD and controls. PD patient data on smell (UPSIT), possible RBD (RBD Screening Questionnaire), and anxiety/depression (LADS) were taken from the Tracking Parkinson’s study (n = 1046). For motor impairment (BRAIN test) in PD cases, published data were supplemented (n = 87). Control data (HADS for anxiety/depression) were taken from the PREDICT-PD pilot study (n = 1314). UPSIT, RBDSQ, and anxiety/depression data were analysed using logistic regression to determine which items were associated with PD. Gaussian distributions were fitted to BRAIN test scores. LRs were calculated from logistic regression models or score distributions. False-positive rates (FPRs) for specified detection rates (DRs) were calculated. Sixteen odours were associated with PD; LRs for this set ranged from 0.005 to 5511. Six RBDSQ and seven anxiety/depression questions were associated with PD; LRs ranged from 0.35 to 69 and from 0.002 to 402, respectively. BRAIN test LRs ranged from 0.16 to 1311. For a 70% DR, the FPR was 2.4% for the 16 odours, 4.6% for anxiety/depression, 16.0% for the BRAIN test, and 20.0% for the RBDSQ. Specific selections of (prodromal) PD marker features rather than dichotomized marker test results optimize PD classification. Such optimized classification models could improve the ability of algorithms to detect prodromal PD; however, prospective studies are needed to investigate their value for PD-prediction models.

嗅觉丧失、运动障碍、焦虑/抑郁和 REM 睡眠行为障碍 (RBD) 是帕金森病 (PD) 的前驱特征。PD 风险预测模型通常将测试结果分为两部分，并将似然比 (LR) 应用于高于和低于临界值的分数。我们调查特定测试值的 LR 是否可以增强 PD 和对照之间的分类。PD 患者关于气味 (UPSIT)、可能的 RBD（RBD 筛查问卷）和焦虑/抑郁 (LADS) 的数据取自 Tracking Parkinson 研究 ( n  = 1046)。对于 PD 病例中的运动障碍（大脑测试），补充了已发布的数据（n  = 87）。对照数据（焦虑/抑郁的 HADS）取自 PREDICT-PD 试点研究（n = 1314）。UPSIT、RBDSQ 和焦虑/抑郁数据使用逻辑回归分析以确定哪些项目与 PD 相关。高斯分布拟合 BRAIN 测试分数。LRs 是从逻辑回归模型或分数分布计算的。计算特定检出率 (DR) 的假阳性率 (FPR)。16 种气味与 PD 相关；这组的 LR 范围从 0.005 到 5511。六个 RBDSQ 和七个焦虑/抑郁问题与 PD 相关；LR 的范围分别为 0.35 至 69 和 0.002 至 402。BRAIN 测试 LR 范围从 0.16 到 1311。对于 70% 的 DR，16 种气味的 FPR 为 2.4%，焦虑/抑郁为 4.6%，BRAIN 测试为 16.0%，RBDSQ 为 20.0%。特定选择的（前驱）PD 标记特征而不是二分标记测试结果优化了 PD 分类。这种优化的分类模型可以提高算法检测前驱PD的能力；然而，需要前瞻性研究来研究它们对 PD 预测模型的价值。