背景和目标:目前,对于接受左旋多巴-卡比多巴肠凝胶(LCIG)治疗的晚期帕金森病(PD)患者,目前尚无预测临床结局的工具.这项研究的目的是开发一种新的深度神经网络模型,以预测LCIG治疗两年后晚期PD患者的临床结果。材料和方法:这是一个纵向的,2019年9月至2021年9月,在LCIG治疗的多中心注册表中,对59名晚期PD患者进行了24个月的观察性研究,其中包括43个运动障碍中心。数据集包括649个患者的测量值,形成不规则的时间序列,在预处理阶段,它们被转换成规则的时间序列。运动状态通过统一帕金森病评定量表(UPDRS)第III部分(关闭)和IV进行评估。通过NMS问卷(NMSQ)和老年抑郁量表(GDS)评估NMS,PDQ-39的生活质量以及Hoehn和Yahr的严重程度(HY)。多元线性回归,阿丽玛,SARIMA,使用长短期记忆-递归神经网络(LSTM-RNN)模型。结果:LCIG显著改善运动障碍持续时间和生活质量,男性进步了19%,女性进步了10%,分别。多元线性回归模型显示,PDQ-39和UPDRS-IV指数每增加一个单位,UPDRS-III减少1.5和4.39个单位,分别。尽管ARIMA-(2,0,2)模型是AIC标准101.8和验证标准MAE=0.25,RMSE=0.59和RS=0.49的最佳模型,但它无法长期预测PD患者的特征。在所有的时间序列模型中,LSTM-RNN模型以最高的准确度预测这些临床特征(MAE=0.057,RMSE=0.079,RS=0.0053,均方误差=0.0069).结论:LSTM-RNN模型预测,以最高的精度,LCIG治疗2年后晚期PD患者的性别依赖性临床结局.
Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson\'s disease (PD) under
levodopa-carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical outcomes of patients with advanced PD after two years of LCIG therapy. Materials and Methods: This was a longitudinal, 24-month observational study of 59 patients with advanced PD in a multicenter registry under LCIG treatment from September 2019 to September 2021, including 43 movement disorder centers. The data set includes 649 measurements of patients, which make an irregular time series, and they are turned into regular time series during the preprocessing phase. Motor status was assessed with the Unified Parkinson\'s Disease Rating Scale (UPDRS) Parts III (off) and IV. The NMS was assessed by the NMS Questionnaire (NMSQ) and the Geriatric Depression Scale (GDS), the quality of life by PDQ-39, and severity by Hoehn and Yahr (HY). Multivariate linear regression, ARIMA, SARIMA, and Long Short-Term Memory-Recurrent NeuralNetwork (LSTM-RNN) models were used. Results: LCIG significantly improved dyskinesia duration and quality of life, with men experiencing a 19% and women a 10% greater improvement, respectively. Multivariate linear regression models showed that UPDRS-III decreased by 1.5 and 4.39 units per one-unit increase in the PDQ-39 and UPDRS-IV indexes, respectively. Although the ARIMA-(2,0,2) model is the best one with AIC criterion 101.8 and validation criteria MAE = 0.25, RMSE = 0.59, and RS = 0.49, it failed to predict PD patients\' features over a long period of time. Among all the time series models, the LSTM-RNN model predicts these clinical characteristics with the highest accuracy (MAE = 0.057, RMSE = 0.079, RS = 0.0053, mean square error = 0.0069). Conclusions: The LSTM-RNN model predicts, with the highest accuracy, gender-dependent clinical outcomes in patients with advanced PD after two years of LCIG therapy.