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. 2024 Jul 25;8(1):11.
doi: 10.1186/s41512-024-00172-6.

Development of a prediction model of conversion to Alzheimer's disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project

Flavia L Lombardo  1 , Patrizia Lorenzini  2 , Flavia Mayer  3 , Marco Massari  3 , Paola Piscopo  4 , Ilaria Bacigalupo  2 , Antonio Ancidoni  2 , Francesco Sciancalepore  2 , Nicoletta Locuratolo  2 , Giulia Remoli  5 , Simone Salemme  6 , Stefano Cappa  7   8 , Daniela Perani  9 , Patrizia Spadin  10 , Fabrizio Tagliavini  11 , Alberto Redolfi  12 , Maria Cotelli  13 , Camillo Marra  14   15 , Naike Caraglia  15 , Fabrizio Vecchio  16   17 , Francesca Miraglia  16   17 , Paolo Maria Rossini  15   16 , Nicola Vanacore  2 ; INTERCEPTOR Network
Collaborators, Affiliations

Development of a prediction model of conversion to Alzheimer's disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project

Flavia L Lombardo et al. Diagn Progn Res. .

Abstract

Background: In recent years, significant efforts have been directed towards the research and development of disease-modifying therapies for dementia. These drugs focus on prodromal (mild cognitive impairment, MCI) and/or early stages of Alzheimer's disease (AD). Literature evidence indicates that a considerable proportion of individuals with MCI do not progress to dementia. Identifying individuals at higher risk of developing dementia is essential for appropriate management, including the prescription of new disease-modifying therapies expected to become available in clinical practice in the near future.

Methods: The ongoing INTERCEPTOR study is a multicenter, longitudinal, interventional, non-therapeutic cohort study designed to enroll 500 individuals with MCI aged 50-85 years. The primary aim is to identify a biomarker or a set of biomarkers able to accurately predict the conversion from MCI to AD dementia within 3 years of follow-up. The biomarkers investigated in this study are neuropsychological tests (mini-mental state examination (MMSE) and delayed free recall), brain glucose metabolism ([18F]FDG-PET), MRI volumetry of the hippocampus, EEG brain connectivity, cerebrospinal fluid (CSF) markers (p-tau, t-tau, Aβ1-42, Aβ1-42/1-40 ratio, Aβ1-42/p-Tau ratio) and APOE genotype. The baseline visit includes a full cognitive and neuropsychological evaluation, as well as the collection of clinical and socio-demographic information. Prognostic models will be developed using Cox regression, incorporating individual characteristics and biomarkers through stepwise selection. Model performance will be evaluated in terms of discrimination and calibration and subjected to internal validation using the bootstrapping procedure. The final model will be visually represented as a nomogram.

Discussion: This paper contains a detailed description of the statistical analysis plan to ensure the reproducibility and transparency of the analysis. The prognostic model developed in this study aims to identify the population with MCI at higher risk of developing AD dementia, potentially eligible for drug prescriptions. The nomogram could provide a valuable tool for clinicians for risk stratification and early treatment decisions.

Trial registration: ClinicalTrials.gov NCT03834402. Registered on February 8, 2019.

Keywords: Alzheimer’s disease; Biomarker; Dementia; Longitudinal study; Mild cognitive impairment; Prediction model; Statistical analysis plan.

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Conflict of interest statement

The authors declare that they have no competing interests.

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