Personalized prediction of depression in patients with newly diagnosed Parkinson's disease: A prospective cohort study
Personalized prediction of depression in patients with newly diagnosed Parkinson's disease: A prospective cohort study
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DOI：
10.1016/j.jad.2020.02.046
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发表时间：
2020-05-01
影响因子：
6.6
通讯作者：
Ye, Qing
中科院分区：
文献类型：
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作者：
Gu, Si-Chun;Zhou, Jie;Ye, Qing
背景：帕金森病患者的抑郁障碍(DPD)已被认为是帕金森病(PD)患者生活质量的最重要决定因素。在临床试验中，需要在病程早期对有抑郁风险的患者进行分类，以预测预后和对参与者进行分层。方法：应用一种名为极端梯度增强(XGBoost)的机器学习算法和Logistic回归技术来预测临床上有意义的抑郁(定义为15项老年抑郁量表[GDS-15]>；=5)，使用前瞻性队列研究，从帕金森进展标志物倡议(PPMI)数据库中对312名新诊断为帕金森病的未用药患者进行了2年的随访。对所建立的模型进行样本外验证，将样本按7：3的比例划分为训练样本和测试样本。2通过两个模型确定PD特异性因素(发病年龄、病程)和4个非特异性因素(基线GDS-15评分、状态特质焦虑量表(STAI)评分、快速眼动睡眠行为筛查问卷(RBDSQ)评分和抑郁史)是重要的预测因素。限制：获得的几个变量受到数据库的限制。结论：在这项纵向研究中，我们开发了有前景的工具来提供早期PD抑郁的个性化估计，并研究了PD特异性和非特异性预测因子的相对贡献，构成了对目前对DPD的理解的重要补充。
Background: Depressive disturbances in Parkinson's disease (dPD) have been identified as the most important determinant of quality of life in patients with Parkinson's disease (PD). Prediction models to triage patients at risk of depression early in the disease course are needed for prognosis and stratification of participants in clinical trials.Methods: One machine learning algorithm called extreme gradient boosting (XGBoost) and the logistic regression technique were applied for the prediction of clinically significant depression (defined as The 15-item Geriatric Depression Scale [GDS-15] >= 5) using a prospective cohort study of 312 drug-naive patients with newly diagnosed PD during 2-year follow-up from the Parkinson's Progression Markers Initiative (PPMI) database. Established models were assessed with out-of-sample validation and the whole sample was divided into training and testing samples by the ratio of 7:3.Results: Both XGBoost model and logistic regression model achieved good discrimination and calibration. 2 PD-specific factors (age at onset, duration) and 4 nonspecific factors (baseline GDS-15 score, State Trait Anxiety Inventory [STAI] score, Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire [RBDSQ] score, and history of depression) were identified as important predictors by two models.Limitations: Access to several variables was limited by database.Conclusions: In this longitudinal study, we developed promising tools to provide personalized estimates of depression in early PD and studied the relative contribution of PD-specific and nonspecific predictors, constituting a substantial addition to the current understanding of dPD.