Multimodal prognostic modeling of individual cognitive trajectories to enhance trial efficiency in preclinical Alzheimer's disease

IF 11.1 1区 医学 Q1 CLINICAL NEUROLOGY
Viswanath Devanarayan, Michael C. Donohue, Reisa A. Sperling, Keith A. Johnson, Yuanqing Ye, Arnaud Charil, Thomas Doherty, Lu Tian, Rema Raman, Paul S. Aisen, Lynn D. Kramer, Michael C. Irizarry, Shobha Dhadda
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引用次数: 0

Abstract

INTRODUCTION

Cognitive decline in asymptomatic preclinical Alzheimer's disease (AD) is slow and variable, limiting detection of treatment effects. This study developed models to forecast trajectories and improve trial efficiency.

METHODS

Models were trained on longitudinal Preclinical Alzheimer's Cognitive Composite (PACC) data up to 240 weeks from the Phase III A4 study of solanezumab. Baseline inputs included demographics, apolipoprotein E (APOE) ε4, clinical scores, amyloid positron emission tomography (PET), plasma pTau217, magnetic resonance imaging (MRI), and tau PET (sub-study). Stochastic gradient boosting was used, with evaluation via cross-validation and trial simulations.

RESULTS

The best model without tau PET used pTau217, clinical, and MRI data (R2 = 0.32; area under the receiver operating characteristic curve (AUROC) for classifying a 0.5-point PACC decline = 78.6%). Replacing MRI with tau PET improved performance (R2 = 0.42; AUROC = 83.1%). Predicted trajectories as a prognostic covariate reduced sample sizes by 35% and increased power from 80% to 94.7%.

DISCUSSION

Prognostic models can predict decline in preclinical AD and improve trial efficiency.

CLINICALTRIALS.GOV IDENTIFIERS

NCT02008357 (Clinical Trial of Solanezumab for Older Individuals Who May be at Risk for Memory Loss (A4))

Highlights

  • Models forecast 4.5-year cognitive decline in amyloid-positive preclinical Alzheimer's disease (AD).
  • Plasma pTau217 and tau positron emission tomography (PET) standardized uptake value ratios (SUVRs) in early-accumulating regions are key predictors.
  • Tau PET improves prediction beyond plasma, magnetic resonance imaging (MRI), and clinical measures.
  • Forecasted decline as a prognostic covariate improves power and cuts sample size in trial simulations.
  • Alternative models underperform yet retain practical utility when tau PET or pTau217 is unavailable.

Abstract Image

Abstract Image

个体认知轨迹的多模式预后建模以提高临床前阿尔茨海默病的试验效率
无症状临床前阿尔茨海默病(AD)的认知衰退是缓慢和可变的,限制了治疗效果的检测。本研究建立了预测轨迹的模型,以提高审判效率。
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来源期刊
Alzheimer's & Dementia
Alzheimer's & Dementia 医学-临床神经学
CiteScore
14.50
自引率
5.00%
发文量
299
审稿时长
3 months
期刊介绍: Alzheimer's & Dementia is a peer-reviewed journal that aims to bridge knowledge gaps in dementia research by covering the entire spectrum, from basic science to clinical trials to social and behavioral investigations. It provides a platform for rapid communication of new findings and ideas, optimal translation of research into practical applications, increasing knowledge across diverse disciplines for early detection, diagnosis, and intervention, and identifying promising new research directions. In July 2008, Alzheimer's & Dementia was accepted for indexing by MEDLINE, recognizing its scientific merit and contribution to Alzheimer's research.
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