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Observational Study
. 2024 May 26;60(6):873.
doi: 10.3390/medicina60060873.

An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel

Anastasia Bougea  1 , Tajedin Derikvand  2 , Efthymia Efthimiopoulou  1
Affiliations
Observational Study

An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel

Anastasia Bougea et al. Medicina (Kaunas). .

Abstract

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.

Keywords: Geriatric Depression Scale; Long Short-Term Memory–Recurrent Neural Network; NMS Questionnaire; Non-Motor Symptoms Questionnaire; Unified Parkinson’s Disease Rating Scale; advanced Parkinson’s disease; levodopa–carbidopaintestinal gel.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Clinical outcomes after LCIG implementation in women at 2-years followup. LCIG: levodopa–carbidopa infusion gel, QoL: quality of life, UPDRSIII:Unified Parkinson’s Disease Rating Scale III.
Figure 2
Figure 2
Clinical outcomes after LCIG initiation in men at 2-years followup. LCIG: levodopa–carbidopa infusion gel, QoL: quality of life, UPDRSIII: Unified Parkinson’s Disease Rating Scale III.
Figure 3
Figure 3
Effects of LCIG treatment on dyskinesia duration in men and women (before and 2 years after the initiation of LCIG treatment). LCIG: levodopa–carbidopa intestinal gel.
Figure 4
Figure 4
Regular and irregular time series of dyskinesia duration for men and women two years after the initiation of LCIG treatmentusing cubic spline. (a,b) Are plots ofthe irregular time series with a frequency of 1 dyskinesia duration for men and women, respectively. (c,d) Are plots of their corresponding irregular time series without any outlier data. To omit possible outlier data, the lower and upper bounds are considered as 1.5 times the IQR (interquartile range), less than the first quartile, and 1.5 times the IQR, greater than the second quartile, respectively.
Figure 5
Figure 5
Comparison of the LSTM and ARIMA models to predictdyskinesia duration for patients under LCIG. LCIG: levodopa–carbidopa intestinal gel.
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