Alzheimer's & dementia (Amsterdam, Netherlands) · 2026
A scalable workflow using digital cognitive assessment and pTau217 for MCI detection.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Vairavan Srinivasan, Griffin Nicholas, Wilson David, Triana-Baltzer Gallen, Brugada-Ramentol Victoria, Vera-Campuzano Elena, Alcolea Daniel, Fallone Silvia, Mackintosh Lucy, Mette Lindsey, Doecke James D, Robertson Joanne, Ward Larry, Benavides Gonzalo Sánchez, Minguillon Carolina, Mas Clàudia Porta, Tort-Merino Adrià, Balasa Mircea, Rivera Oriol Grau, Calvet Marc Suárez, Taylor Lewis, Rodríguez-Baz Íñigo, Arranz Javier, Fortea Juan, Tarnanas Ioannis, Jones Marc, Wittenberg Gayle, Streel Emmanuel, Iulita Maria Florencia
Аннотация
INTRODUCTION: Blood biomarkers like phosphorylated tau (p-tau)217 offer high diagnostic accuracy for Alzheimer's disease (AD) but face implementation challenges in low-prevalence settings. We evaluated the Altoida NeuroMarker Platform as a digital cognitive triage tool before plasma p-tau217 testing.
METHODS: XGBoost models were trained to predict mild cognitive impairment (MCI) and p-tau217 status in 688 individuals across Australia, Spain, and the United States using the Altoida NeuroMarker. p-tau217 status was dichotomized using a threshold of 0.04 pg/mL (LucentAD). We modeled a two-step workflow (Altoida, plasma p-tau217) to enrich downstream biomarker testing, assuming 30% prevalence of AD pathology.
RESULTS: The Altoida NeuroMarker accurately identified MCI (receiver operating characteristic area under the curve [ROC AUC] = 0.89 ± 0.01) and p-tau217 elevation (ROC AUC = 0.77 ± 0.08). Predicted MCI was significantly associated with elevated pTau217 (p = 0.004). When used upstream, the Altoida NeuroMarker ruled out 64.4% of participants (negative predictive value 90.2%), shifting the pre-test probability of AD pathology to 66.6%, and improving the modeled positive predictive value of plasma p-tau217 from 81.7% to 95.3%.
DISCUSSION: This workflow outlines a scalable path to enrich blood-based biomarker testing after digital cognitive screening.
METHODS: XGBoost models were trained to predict mild cognitive impairment (MCI) and p-tau217 status in 688 individuals across Australia, Spain, and the United States using the Altoida NeuroMarker. p-tau217 status was dichotomized using a threshold of 0.04 pg/mL (LucentAD). We modeled a two-step workflow (Altoida, plasma p-tau217) to enrich downstream biomarker testing, assuming 30% prevalence of AD pathology.
RESULTS: The Altoida NeuroMarker accurately identified MCI (receiver operating characteristic area under the curve [ROC AUC] = 0.89 ± 0.01) and p-tau217 elevation (ROC AUC = 0.77 ± 0.08). Predicted MCI was significantly associated with elevated pTau217 (p = 0.004). When used upstream, the Altoida NeuroMarker ruled out 64.4% of participants (negative predictive value 90.2%), shifting the pre-test probability of AD pathology to 66.6%, and improving the modeled positive predictive value of plasma p-tau217 from 81.7% to 95.3%.
DISCUSSION: This workflow outlines a scalable path to enrich blood-based biomarker testing after digital cognitive screening.