Li Juan, Mestre Tiago A, Mollenhauer Brit, Frasier Mark, Tomlinson Julianna J, Trenkwalder Claudia, Ramsay Tim, Manuel Douglas, Schlossmacher Michael G
Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
NPJ Parkinsons Dis. 2022 Jul 29;8(1):94. doi: 10.1038/s41531-022-00360-5.
Several recent publications described algorithms to identify subjects with Parkinson's disease (PD). In creating the "PREDIGT Score", we previously developed a hypothesis-driven, simple-to-use formula to potentially calculate the incidence of PD. Here, we tested its performance in the 'De Novo Parkinson Study' (DeNoPa) and 'Parkinson's Progression Marker Initiative' (PPMI); the latter included participants from the 'FOllow Up persons with Neurologic Disease' (FOUND) cohort. Baseline data from 563 newly diagnosed PD patients and 306 healthy control subjects were evaluated. Based on 13 variables, the original PREDIGT Score identified recently diagnosed PD patients in the DeNoPa, PPMI + FOUND and the pooled cohorts with area-under-the-curve (AUC) values of 0.88 (95% CI 0.83-0.92), 0.79 (95% CI 0.72-0.85), and 0.84 (95% CI 0.8-0.88), respectively. A simplified version (8 variables) generated AUC values of 0.92 (95% CI 0.89-0.95), 0.84 (95% CI 0.81-0.87), and 0.87 (0.84-0.89) in the DeNoPa, PPMI, and the pooled cohorts, respectively. In a two-step, screening-type approach, self-reported answers to a questionnaire (step 1) distinguished PD patients from controls with an AUC of 0.81 (95% CI 0.75-0.86). Adding a single, objective test (Step 2) further improved classification. Among seven biological markers explored, hyposmia was the most informative. The composite AUC value measured 0.9 (95% CI 0.88-0.91) in DeNoPa and 0.89 (95% CI 0.84-0.94) in PPMI. These results reveal a robust performance of the original PREDIGT Score to distinguish newly diagnosed PD patients from controls in two established cohorts. We also demonstrate the formula's potential applicability to enriching for PD subjects in a population screening-type approach.
最近的几篇出版物描述了识别帕金森病（PD）患者的算法。在创建“PREDIGT评分”时，我们之前开发了一个基于假设、易于使用的公式，以潜在地计算PD的发病率。在此，我们在“新发帕金森病研究”（DeNoPa）和“帕金森病进展标志物倡议”（PPMI）中测试了其性能；后者纳入了“随访神经疾病患者”（FOUND）队列的参与者。对563名新诊断的PD患者和306名健康对照者的基线数据进行了评估。基于13个变量，原始的PREDIGT评分在DeNoPa、PPMI + FOUND以及合并队列中识别新诊断的PD患者，曲线下面积（AUC）值分别为0.88（95%CI 0.83 - 0.92）、0.79（95%CI 0.72 - 0.85）和0.84（95%CI 0.8 - 0.88）。一个简化版本（8个变量）在DeNoPa、PPMI和合并队列中产生的AUC值分别为0.92（95%CI 0.89 - 0.95）、0.84（95%CI 0.81 - 0.87）和0.87（0.84 - 0.89）。在一种两步筛选型方法中，问卷的自我报告答案（第一步）以0.81（95%CI 0.75 - 0.86）的AUC区分PD患者和对照。添加一项单一的客观测试（第二步）进一步改善了分类。在探索的七种生物标志物中，嗅觉减退信息含量最高。在DeNoPa中复合AUC值为0.9（95%CI 0.88 - 0.91），在PPMI中为0.89（95%CI 0.84 - 0.94）。这些结果揭示了原始PREDIGT评分在两个既定队列中区分新诊断的PD患者和对照的强大性能。我们还证明了该公式在人群筛选型方法中富集PD受试者方面的潜在适用性。
