Validation of a 5-Year Prognostic Model for Parkinson's Disease.

IF 2.6 4区医学 Q2 CLINICAL NEUROLOGY

Movement Disorders Clinical Practice Pub Date : 2024-09-29 DOI:10.1002/mdc3.14215

Joana Ribeiro, Marta Camacho, Kirsten M Scott, Julia C Greenland, Jonathan R Evans, David P Breen, Ruwani S Wijeyekoon, Roger A Barker, Caroline H Williams-Gray

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引用次数: 0

Abstract

Background: A simple prognostic model was previously developed to predict the probability of recently-diagnosed patients reaching negative outcomes (postural instability, dementia or death) in a 5-year period.

Objectives: To validate this model in an independent cohort and establish utility at later time points.

Methods: Validation was performed using data collected in an incident cohort at baseline, 2 and 4 years. Predicted negative outcome probabilities were compared to actual 5-year outcomes.

Results: The model, based on age, MDS-UPDRS axial score and 60-second animal fluency, predicted poor 5-year outcome when applied at baseline, (area under the curve (AUC) 0.80), 2 years (AUC 0.82) and 4 years (AUC 0.71). Power calculations showed that selecting a subgroup with prognostic score >0.5 reduced the sample size required for a disease-modifying trial.

Conclusions: This 5-year prognostic model has good accuracy when employed up to 4 years from diagnosis and may help stratification for disease-modifying trials.

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帕金森病 5 年预后模型的验证。

背景：以前曾开发过一个简单的预后模型，用于预测新近确诊的患者在5年内出现不良后果（体位不稳、痴呆或死亡）的概率：目的：在一个独立的队列中验证该模型，并确定其在以后时间点的效用：方法：使用在基线、2 年和 4 年时收集的事件队列数据进行验证。将预测的负结果概率与 5 年实际结果进行比较：该模型基于年龄、MDS-UPDRS 轴向评分和 60 秒动物流利度，在基线（曲线下面积 (AUC) 0.80）、2 年（AUC 0.82）和 4 年（AUC 0.71）时可预测不良的 5 年结果。功率计算显示，选择预后评分大于 0.5 的亚组可减少疾病改变试验所需的样本量：结论：这一5年预后模型在诊断后4年内都具有很高的准确性，有助于对疾病改变试验进行分层。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Movement Disorders Clinical Practice CLINICAL NEUROLOGY-

CiteScore

4.00

自引率

7.50%

发文量

218

期刊介绍： Movement Disorders Clinical Practice- is an online-only journal committed to publishing high quality peer reviewed articles related to clinical aspects of movement disorders which broadly include phenomenology (interesting case/case series/rarities), investigative (for e.g- genetics, imaging), translational (phenotype-genotype or other) and treatment aspects (clinical guidelines, diagnostic and treatment algorithms)