Background and Objective

Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment.

Methods

The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index.

Results

Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %.

Conclusions

Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making “second-opinion” suggestions to decision support users.

中文翻译：

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帕金森氏病患者药物治疗的决策支持。

背景与目的

帕金森氏病（PD）是中枢神经系统退行性疾病，目前尚无法治愈。其治疗需要长期的跨学科疾病管理，并需要使用典型的药物，包括左旋多巴，多巴胺激动剂和酶，例如MAO-B抑制剂。疾病管理的主要目标是延长患者的独立性并保持他们的生活质量。除了现有的合并症之外，由于PD患者所遭受的运动和非运动症状的不同组合，药物的更换及其组合也是困难且针对患者的。为了帮助医生，我们为PD管理开发了两个决策支持模型，它们提出了如何更改药物治疗的建议。

方法

这些模型是使用DEX方法开发的，该方法将定性多标准决策模型与基于规则的专家系统集成在一起。两种DEX模型在定义决策规则的方式上有所不同。在第一个模型中，决策规则是基于对神经科医生的访谈（DEX专家模型），而在第二个模型中，决策规则是由过去药物变更决策的数据库（DEX数据模型）形成的。我们在帕金森氏病进展指标计划（PPMI）上以及在来自欧洲4个国家/地区的17位神经学家回答的问卷中使用准确性测量和Jaccard指数评估了这两种模型。

结果

这两个模型都包括15个子模型，这些子模型根据给定患者的当前状态来解决可能的药物治疗变化。特别是，这些模型结合了患者运动症状（运动障碍强度，运动障碍持续时间，关闭持续时间），精神问题（冲动，认知，幻觉和偏执狂），流行病学数据（患者年龄，活动水平）和合并症（心血管疾病）的当前状态变化。问题，高血压和低血压）。对于15种药物治疗变化，已开发的子模型的最高准确性在69.31％至99.06％之间。

结论

结果表明，DEX专家模型优于DEX数据模型。结果表明，所构建的模型足够充分，因此适合于向决策支持用户提出“第二意见”的建议。