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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.
Lombardo, Flavia L; Lorenzini, Patrizia; Mayer, Flavia; Massari, Marco; Piscopo, Paola; Bacigalupo, Ilaria; Ancidoni, Antonio; Sciancalepore, Francesco; Locuratolo, Nicoletta; Remoli, Giulia; Salemme, Simone; Cappa, Stefano; Perani, Daniela; Spadin, Patrizia; Tagliavini, Fabrizio; Redolfi, Alberto; Cotelli, Maria; Marra, Camillo; Caraglia, Naike; Vecchio, Fabrizio; Miraglia, Francesca; Rossini, Paolo Maria; Vanacore, Nicola.
  • Lombardo FL; National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy. flavia.lombardo@iss.it.
  • Lorenzini P; National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.
  • Mayer F; National Center for Drug Research and Evaluation, Italian National Institute of Health, Rome, Italy.
  • Massari M; National Center for Drug Research and Evaluation, Italian National Institute of Health, Rome, Italy.
  • Piscopo P; Department of Neuroscience, Italian National Institute of Health, Rome, Italy.
  • Bacigalupo I; National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.
  • Ancidoni A; National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.
  • Sciancalepore F; National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.
  • Locuratolo N; National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.
  • Remoli G; Neurology Department and Brain Health Service, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy.
  • Salemme S; Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
  • Cappa S; University Institute of Advanced Studies IUSS Pavia, Pavia, Italy.
  • Perani D; IRCCS Mondino Foundation, Pavia, Italy.
  • Spadin P; Vita-Salute San Raffaele University, Nuclear Medicine Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Tagliavini F; "Associazione Italiana Malattia di Alzheimer" - AIMA, Milan, Italy.
  • Redolfi A; Fondazione IRCCS Istituto Neurologico "Carlo Besta", Milan, Italy.
  • Cotelli M; Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy.
  • Marra C; Neuropsychology Unit, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy.
  • Caraglia N; Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy.
  • Vecchio F; Memory Clinic, Foundation Policlinico Agostino Gemelli IRCCS, Rome, Italy.
  • Miraglia F; Memory Clinic, Foundation Policlinico Agostino Gemelli IRCCS, Rome, Italy.
  • Rossini PM; Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy.
  • Vanacore N; Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
Diagn Progn Res ; 8(1): 11, 2024 Jul 25.
Article en En | MEDLINE | ID: mdl-39049042
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, Aß1-42, Aß1-42/1-40 ratio, Aß1-42/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.
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