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. 2018 Feb 16:4:76-88.
doi: 10.1016/j.trci.2018.01.001. eCollection 2018.

Alzheimer's disease Archimedes condition-event simulator: Development and validation

Anuraag R Kansal  1 , Ali Tafazzoli  1 , K Jack Ishak  2 , Stanmira Krotneva  2
Affiliations

Alzheimer's disease Archimedes condition-event simulator: Development and validation

Anuraag R Kansal et al. Alzheimers Dement (N Y). .

Abstract

Introduction: Several advances have been made in Alzheimer's Disease (AD) modeling, however, there remains a need for a simulator that represents the full scope of disease progression and can be used to study new disease-modifying treatments for early-stage and even prodromal AD.

Methods: We developed AD Archimedes condition-event simulator, a patient-level simulator with a focus on simulating the effects of early interventions through changes in biomarkers of AD. The simulator incorporates interconnected predictive equations derived from longitudinal data sets.

Results: The results of external validations on AD Archimedes condition-event simulator showed that it provides reasonable estimates once compared to literature results on transition to dementia AD, institutionalization, and mortality. A case study comparing a disease-modifying treatment and a symptomatic treatment also showcases the benefits of early treatment.

Discussion: The AD Archimedes condition-event simulator is designed to perform economic evaluation on various interventions through close tracking of disease progression and the related clinical outcomes.

Keywords: Alzheimer's disease (AD); Biomarkers; Cost-effectiveness analysis; Disease-modifying treatments; Predictive equations; Prodromal AD; Simulation.

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Figures

Fig. 1
Fig. 1
Influence diagram outlining the key relationships in the AD ACE simulator. Abbreviations: ADAS-Cog13, Alzheimer's Disease Assessment Scale-Cognitive Subscale 13; ADL, Activities of Daily Living; APOE ɛ4, Apolipoprotein E4; CDR-SB, Clinical Dementia Rating Sum of Boxes; CSF Aβ1–42, Cerebrospinal Fluid β amyloid; CSF t-tau, Cerebrospinal Fluid total-tau; DAD, Disability Assessment scale for Dementia; DS, Dependence Scale; FDG-PET, Fluorodeoxyglucose–positron emission tomography; IADL, Instrumental Activities of Daily Living; MMSE, Mini-Mental State Examination; NPI-Q12, Neuropsychiatric Inventory Questionnaire 12.
Fig. 2
Fig. 2
Relationships among markers in the disease model from ADNI Data. Abbreviations: A-Beta, amyloid β; ADAS-Cog, Alzheimer's Disease Assessment Scale–Cognitive Subscale; CDRSB, Clinical Dementia Rating Sum of Boxes; FDG, fluorodeoxyglucose; Hip. Vol., hippocampal volume; MMSE, Mini-Mental State Examination; NPI, The Neuropsychiatric Inventory Questionnaire; T-Tau, total-tau.
Fig. 3
Fig. 3
Between-patient (left figure) and within-patient (right figure) heterogeneity for predicted MMSE trajectories in an MCI patient. Abbreviations: MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination.
Fig. 4
Fig. 4
Simulated rates of institutionalization and survival versus Razlighi et al. . Abbreviation: AD ACE, Alzheimer's disease Archimedes condition-event simulator.
Fig. 5
Fig. 5
Simulated survival versus Stubendorff et al. . Abbreviation: AD ACE, Alzheimer's disease Archimedes condition-event simulator.
Fig. 6
Fig. 6
Simulated trajectories of MMSE for varying combinations of ADNI and AHEAD equations over a range of MMSE score thresholds. Abbreviations: ADNI, Alzheimer's Disease Neuroimaging Initiative; AHEAD, Assessment of Health Economics in Alzheimer's Disease II; MMSE, Mini-Mental State Examination.
Fig. 7
Fig. 7
MMSE trajectories for DMT, symptomatic treatment, and placebo. Abbreviations: DMT, disease-modifying treatment; MMSE, Mini-Mental State Examination; Symp Tx, Symptomatic treatment.
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