ADSS: A Composite Score to Detect Disease Progression in Alzheimer's Disease.

IF 2.8 Q2 NEUROSCIENCES Journal of Alzheimer's disease reports Pub Date : 2024-02-20 eCollection Date: 2024-01-01 DOI:10.3233/ADR-230043

Guogen Shan, Xinlin Lu, Zhigang Li, Jessica Z K Caldwell, Charles Bernick, Jeffrey Cummings

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Abstract

Background: Composite scores have been increasingly used in trials for Alzheimer's disease (AD) to detect disease progression, such as the AD Composite Score (ADCOMS) in the lecanemab trial.

Objective: To develop a new composite score to improve the prediction of outcome change.

Methods: We proposed to develop a new composite score based on the statistical model in the ADCOMS, by removing duplicated sub-scales and adding the model selection in the partial least squares (PLS) regression.

Results: The new AD composite Score with variable Selection (ADSS) includes 7 cognitive sub-scales. ADSS can increase the sensitivity to detect disease progression as compared to the existing total scores, which leads to smaller sample sizes using the ADSS in trial designs.

Conclusions: ADSS can be utilized in AD trials to improve the success rate of drug development with a high sensitivity to detect disease progression in early stages.

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ADSS：检测阿尔茨海默病病情进展的综合评分。

背景：在阿尔茨海默病（AD）的试验中，越来越多地使用综合评分来检测疾病的进展情况，例如lecanemab试验中的AD综合评分（ADCOMS）：目的：开发一种新的综合评分，以提高对结果变化的预测能力：方法：我们建议在ADCOMS统计模型的基础上，通过去除重复的子量表并在偏最小二乘法（PLS）回归中加入模型选择来开发一种新的综合评分：结果：带变量选择的新AD综合评分（ADSS）包括7个认知子量表。与现有的总分相比，ADSS 可以提高检测疾病进展的灵敏度，这使得在试验设计中使用 ADSS 的样本量更小：ADSS可用于AD试验，以提高药物开发的成功率，并能在早期阶段高灵敏度地检测疾病进展。

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

Journal of Alzheimer's disease reports

CiteScore

2.80

自引率

0.00%

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