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Evaluation of the PREDIGT score’s performance in identifying newly diagnosed Parkinson’s patients without motor examination
npj Parkinson's Disease ( IF 9.304 ) Pub Date : 2022-07-29 , DOI: 10.1038/s41531-022-00360-5
Juan Li 1, 2, 3 , Tiago A Mestre 1, 2, 3, 4, 5 , Brit Mollenhauer 6 , Mark Frasier 7 , Julianna J Tomlinson 1, 3, 8 , Claudia Trenkwalder 6 , Tim Ramsay 2, 9, 10 , Douglas Manuel 2, 9, 11 , Michael G Schlossmacher 1, 3, 4, 5, 8
Affiliation  

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.



中文翻译:

在没有运动检查的情况下评估 PREDIGT 评分在识别新诊断的帕金森病患者中的表现

最近的几篇出版物描述了识别帕金森病 (PD) 受试者的算法。在创建“预测评分”时,我们之前开发了一个假设驱动、简单易用的公式来潜在地计算 PD 的发生率。在这里,我们测试了它在“新帕金森研究”(DeNoPa)和“帕金森进展标志物倡议”(PPMI)中的表现;后者包括来自“跟踪患有神经系统疾病的人”(FOUND)队列的参与者。评估了 563 名新诊断的 PD 患者和 306 名健康对照受试者的基线数据。基于 13 个变量,原始 PREDIGT 评分确定了 DeNoPa、PPMI + FOUND 和合并队列中最近诊断的 PD 患者,曲线下面积 (AUC) 值为 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)。简化版本(8 个变量)在 DeNoPa、PPMI 和汇总队列中分别生成 0.92(95% CI 0.89–0.95)、0.84(95% CI 0.81–0.87)和 0.87(0.84–0.89)的 AUC 值. 在两步筛选型方法中,自我报告的问卷答案(步骤 1)将 PD 患者与 AUC 为 0.81(95% CI 0.75-0.86)的对照组区分开来。添加一个单一的、客观的测试(步骤 2)进一步改进了分类。在探索的七种生物标志物中,嗅觉减退是信息量最大的。复合 AUC 值在 DeNoPa 中测量为 0.9 (95% CI 0.88–0.91),在 PPMI 中测量为 0.89 (95% CI 0.84–0.94)。这些结果揭示了原始 PREDIGT 评分的稳健表现,以区分两个已建立队列中新诊断的 PD 患者与对照组。

更新日期:2022-07-29
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