Mendes Alexandre, Gonçalves Alexandra, Vila-Chã Nuno, Calejo Margarida, Moreira Inês, Fernandes Joana, Damásio Joana, Teixeira-Pinto Armando, Krack Paul, Lima António Bastos, Cavaco Sara
Serviço de Neurologia, Centro Hospitalar do Porto, Porto, Portugal.
Unidade Multidisciplinar de Investigação Biomédica, Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal.
J Parkinsons Dis. 2016 Oct 19;6(4):793-804. doi: 10.3233/JPD-160877.
The rate of Parkinson's disease (PD) progression varies widely between patients. Current knowledge does not allow to accurately predict the evolution of symptoms in a given individual over time.
To develop regression-based models of PD progression and to explore its predictive value in a three-year follow-up.
At baseline, 300 consecutive PD patients were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) - subscales II and III, Hoehn & Yahr (H&Y) and Schwab and England Independence Scale (S&E); and the Freezing of Gait Questionnaire (FOG-Q). UPDRS-III and H&Y were applied in OFF and ON medication conditions. An axial index was derived from the UPDRS-III. Based on multiple linear regression coefficients, algorithms were developed to adjust test scores to the characteristics of each individual. Sixty-eight patients were reevaluated three years later.
In the construction of the models, disease duration, age ≥70, age at disease onset ≥55, tremor as the first symptom alone, and medication description explained between 35% (UPDRS-III in ON) and 57% (axial index in ON) of the variance of test scores. The predictive r2 of the models in a 10-fold cross-validation ranged between 33% (UPDRS-III in ON) and 55% (axial index in ON and S&E in OFF). All measures, except UPDRS-III OFF, H&Y ON, and S&E ON, had moderate/good absolute agreement (intraclass correlation coefficient between 0.60 and 0.72) between baseline and follow-up.
A cross-sectional assessment of a PD population allowed the development of models of disease progression, whose predictive value was validated on a three-year longitudinal study.
帕金森病（PD）患者的病情进展速度差异很大。目前的知识水平尚无法准确预测特定个体症状随时间的演变情况。
建立基于回归分析的PD病情进展模型，并在三年随访中探索其预测价值。
在基线时，使用统一帕金森病评定量表（UPDRS）的第二和第三部分、霍恩和雅尔分级（H&Y）以及施瓦布和英格兰独立量表（S&E）对300例连续的PD患者进行评估；并使用步态冻结问卷（FOG-Q）。在药物开期和关期分别应用UPDRS-III和H&Y。从UPDRS-III得出一个轴向指数。基于多元线性回归系数，开发算法以根据每个个体的特征调整测试分数。三年后对68例患者进行了重新评估。
在模型构建中，病程、年龄≥70岁、发病年龄≥55岁、仅以震颤为首发症状以及药物描述解释了测试分数方差的35%（开期UPDRS-III）至57%（开期轴向指数）。模型在10倍交叉验证中的预测r2在33%（开期UPDRS-III）至55%（开期轴向指数和关期S&E）之间。除了关期UPDRS-III、开期H&Y和开期S&E外，所有测量指标在基线和随访之间均具有中度/良好的绝对一致性（组内相关系数在0.60至0.72之间）。
对PD患者群体的横断面评估使得能够建立病情进展模型，其预测价值在一项为期三年的纵向研究中得到了验证。
