Si-Chun Gu1, Jie Zhou2, Can-Xing Yuan3, Qing Ye4. 1. Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai, 200032, China. Electronic address: ddgsc001@163.com. 2. Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai, 200032, China. Electronic address: zhoujie80@163.com. 3. Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai, 200032, China. Electronic address: cxyuan1964@163.com. 4. Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai, 200032, China; MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 114 16th Street, Charlestown, MA, 02129, United States. Electronic address: yeqing1982889@163.com.
Abstract
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-naïve 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.
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-naïve 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.