Goal: Impulse control disorders (ICDs) are frequent non-motor symptoms occurring during the course of Parkinson’s disease (PD). The objective of this study was to estimate the predictability of the future occurrence of these disorders using longitudinal data, the first study using cross-validation and replication in an independent cohort. Methods: We used data from two longitudinal PD cohorts (training set: PPMI, Parkinson’s Progression Markers Initiative; test set: DIGPD, Drug Interaction With Genes in Parkinson’s Disease). We included 380 PD subjects from PPMI and 388 PD subjects from DIGPD, with at least two visits and with clinical and genetic data available, in our analyses. We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs. We quantified performance using the area under the receiver operating characteristic curve (ROC AUC) and average precision. We compared these models to a trivial model predicting ICDs at the next visit with the status at the most recent visit. Results: The recurrent neural network (PPMI: 0.85 [0.80 – 0.90], DIGPD: 0.802 [0.78 – 0.83]) was the only model to be significantly better than the trivial model (PPMI: ROC AUC = 0.75 [0.69 – 0.81]; DIGPD: 0.78 [0.75 – 0.80]) on both cohorts. We showed that ICDs in PD can be predicted with better accuracy with a recurrent neural network model than a trivial model. The improvement in terms of ROC AUC was higher on PPMI than on DIGPD data, but not clinically relevant in both cohorts. Conclusions: Our results indicate that machine learning methods are potentially useful for predicting ICDs, but further works are required to reach clinical relevance.

中文翻译：

---

基于机器学习的临床和遗传数据预测帕金森病的冲动控制障碍

目标：冲动控制障碍 (ICD) 是帕金森病 (PD) 病程中常见的非运动症状。本研究的目的是使用纵向数据估计这些疾病未来发生的可预测性，这是第一项在独立队列中使用交叉验证和复制的研究。方法：我们使用了来自两个纵向 PD 队列的数据（训练集：PPMI，帕金森病进展标志物倡议；测试集：DIGPD，帕金森病与基因的药物相互作用）。在我们的分析中，我们纳入了来自 PPMI 的 380 名 PD 受试者和来自 DIGPD 的 388 名 PD 受试者，至少有两次就诊，并且有可用的临床和遗传数据。我们训练了三个逻辑回归和一个递归神经网络，以使用以前与 ICD 相关的临床风险因素和遗传变异来预测下次访问时的 ICD。我们使用接收器操作特征曲线下面积 (ROC AUC) 和平均精度来量化性能。我们将这些模型与一个普通模型进行了比较，该模型在下一次访问时预测 ICD 和最近访问时的状态。结果：递归神经网络 (PPMI: 0.85 [0.80 – 0.90], DIGPD: 0.802 [0.78 – 0.83]) 是唯一明显优于普通模型的模型 (PPMI: ROC AUC = 0.75 [0.69 – 0.81]; DIGPD: 0.78 [0.75 – 0.80]）在两个队列中。我们表明，与普通模型相比，使用递归神经网络模型可以更准确地预测 PD 中的 ICD。PPMI 的 ROC AUC 改善高于 DIGPD 数据，但在两个队列中均无临床相关性。结论：我们的结果表明机器学习方法可能对预测 ICD 有用，但需要进一步的工作才能达到临床相关性。