van der Veere, Pieter J; Hoogland, Jeroen; Visser, Leonie NC; Van Harten, Argonde C; Rhodius-Meester, Hanneke F; Sikkes, Sietske AM; Venkatraghavan, Vikram; ... van der Flier, Wiesje M; + view all van der Veere, Pieter J; Hoogland, Jeroen; Visser, Leonie NC; Van Harten, Argonde C; Rhodius-Meester, Hanneke F; Sikkes, Sietske AM; Venkatraghavan, Vikram; Barkhof, Frederik; Teunissen, Charlotte E; van de Giessen, Elsmarieke; Berkhof, Johannes; van der Flier, Wiesje M; - view fewer (2024) Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia. Neurology , 103 (3) , Article e209605. 10.1212/WNL.0000000000209605.

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Abstract

Background and Objectives Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia. Methods From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE e4 dose, CSF β-amyloid (Aβ) 1–42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2). Results In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1–4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1–42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1–42, age, APOE e4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1–42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4–6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%. Discussion We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.

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