Kwasny Mary J, Oleske Denise M, Zamudio Jorge, Diegidio Robert, Höglinger Günter U
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Global Epidemiology, AbbVie Inc., North Chicago, IL, United States.
Front Neurol. 2021 Apr 22;12:637176. doi: 10.3389/fneur.2021.637176. eCollection 2021.
Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder that is difficult for primary care physicians to recognize due to its progressive nature and similarities to other neurologic disorders. This case-control study aimed to identify clinical features observed in general practice associated with a subsequent diagnosis of PSP. We analyzed a de-identified dataset of 152 PSP cases and 3,122 matched controls from electronic medical records of general practices in Germany. We used a random forests algorithm based on machine learning techniques to identify clinical features (medical conditions and treatments received) associated with pre-diagnostic PSP without using an hypothesis. We then assessed the relative effects of the features with the highest importance scores and generated multivariate models using clustered logistic regression analyses to identify a subset of clinical features associated with subsequent PSP diagnosis. Using the random forests approach, we identified 21 clinical features associated with pre-diagnostic PSP (odds ratio ≥2.0 in univariate analyses). From these, we constructed a multivariate model comprising 9 clinical features with ~90% likelihood of identifying a subsequent PSP diagnosis. These features included known PSP symptoms, common misdiagnoses, and 2 novel associations, diabetes mellitus and cerebrovascular disease, which are possible modifiable risk factors for PSP. In this case-control study using data from electronic medical records, we identified 9 clinical features, including 2 previously unknown factors, associated with the pre-diagnostic stage of PSP. These may be used to facilitate recognition of PSP and reduce time to referral by primary care physicians.
进行性核上性麻痹（PSP）是一种罕见的神经退行性疾病，由于其渐进性以及与其他神经系统疾病的相似性，初级保健医生很难识别。这项病例对照研究旨在确定在全科医疗中观察到的与随后诊断为PSP相关的临床特征。我们分析了来自德国全科医疗电子病历的152例PSP病例和3122例匹配对照的去识别数据集。我们使用基于机器学习技术的随机森林算法来识别与诊断前PSP相关的临床特征（医疗状况和接受的治疗），而不使用假设。然后，我们评估了重要性得分最高的特征的相对影响，并使用聚类逻辑回归分析生成多变量模型，以识别与随后PSP诊断相关的临床特征子集。使用随机森林方法，我们确定了21个与诊断前PSP相关的临床特征（单变量分析中优势比≥2.0）。从中，我们构建了一个包含9个临床特征的多变量模型，识别随后PSP诊断的可能性约为90%。这些特征包括已知的PSP症状、常见误诊以及2种新的关联，即糖尿病和脑血管疾病，它们可能是PSP的可改变风险因素。在这项使用电子病历数据的病例对照研究中，我们确定了9个与PSP诊断前阶段相关的临床特征，包括2个以前未知的因素。这些特征可用于促进PSP的识别，并减少初级保健医生的转诊时间。
