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Parkinson's disease (PD) is a progressive neurodegenerative disease with motor and non-motor manifestations
.
In the absence of curative treatment, it is essential to identify important and proven measures that predict the progression of Parkinson's disease
Although the previous literature has studied a wide range of PD-related symptoms, the heterogeneity of signs and symptoms makes it difficult to predict disease progression
.
.
In addition, the symptomatic treatment of dopaminergic therapy has a positive effect on the function of Parkinson's disease, but it conceals the problem of natural progression of the disease
.
There are several outcomes that can define the severity of PD and classify subjects
.
Among them, the Hoehn and Yahr (H&Y) scale is one of the most commonly used classification measures to assess the overall dysfunction of PD
Therefore, in many published studies, the H&Y scale has been used as an effective endpoint to measure the progress of PD
.
Goetz et al.
found that there is a significant difference in the progression of clinical injury in patients with PD in H&Y 2 and 3 stages, indicating that there is a clinical difference in balance disorder in the disease progression trajectory before and after H&Y 3 is reached
.
Although the H&Y phase 3 transition has been widely used as an indicator of disease progression since then, traditionally only baseline variables have been included in the prediction calculations
.
Currently, previous studies have not focused on the use of multiple longitudinal evaluation measures of early PD to predict the time of H&Y 3 transition
In this way, Xuehan Ren et al.
of Duke University in the United States established a prognostic model and used a variety of easily available longitudinal measurement methods to predict the time-based clinical trial of early PD from Hehn and Yahr (H&Y) Phase 1 or Phase 2 to Phase 3.
Progress
.
Determine predictive longitudinal measures of PD progression through joint modeling methods
.
The measures were extracted by multivariate functional principal component analysis and used as covariates in the Cox proportional hazard model
Compared with the model with only baseline information, their prognostic model with longitudinal information of selected clinical measures showed a clear advantage in predicting the time progression of PD (iAUC = 0.
812 vs.
0.
743)
.
The modeling results allow the development of a prognostic index, which divides PD patients into low, medium, and high-risk HY 3 progressions, and provides them to doctors and patients to discuss prognostic issues
.
Incorporating longitudinal information from a variety of clinical measures can significantly improve the predictive performance of the prognostic model
.
In addition, the proposed prognostic index enables clinicians to divide patients into different risk groups, which can be updated adaptively as new longitudinal information emerges
Original Source:
Ren X, Lin J, Stebbins GT, Goetz CG, Luo S.
Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.
Mov Disord.
Published online July 30, 2021:mds.
Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.
Mov Disord.
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