Inflammatory Neuropathy Consortium base (INCbase) : a protocol of a global prospective observational cohort study for the development of a prediction model for treatment response in chronic inflammatory demyelinating polyneuropathy
Michael, Milou R. (University of Amsterdam)
Wieske, Luuk (University of Amsterdam)
Allen, J.A (University of Minnesota)
Lunn, Michael P. (National Hospital for Neurology and Neurosurgery)
Doppler, Kathrin (University Hospital Würzburg)
Tan, Cheng-Yin (University of Malaya)
Koike, Haruki (Saga University)
Markvardsen, Lars K. (Aarhus University Hospital (Aarhus, Dinamarca))
Kapoor, Mahima (Alfred Hospital)
Hsieh, Sung-Tsang (National Taiwan University Hospital)
Nobile-Orazio, Eduardo (University of Milan)
Jacobs, Bart C. (Erasmus MC University Medical Center)
Rajabally, Yusuf A. (Queen Elizabeth Hospital Birmingham)
Basta, Ivana (University of Belgrade)
Ripellino, Paolo (Università della Svizzera Italiana)
Querol, Luis (Institut de Recerca Sant Pau)
Eftimov, Filip (University of Amsterdam)
Castro, D.
Vytopil, M.
Gable, K.
Stino, A.M.
Pasnoor, M.
Hoke, A.
Lai, K.L.
Ro, L.S.
Sung, J.Y.
Lascano, A.
Nagy, S.
Hundsberger, T.
Humm, A.
Theaudin, M.
Llaurado-Gayete, A.
Martinez-Hernandez, E.
Moris, G.
Sevilla, T.
Pascual, I.J.
Gutiérrez-Gutiérrez, G.
Universitat Autònoma de Barcelona

Data:	2024
Resum:	Background: INCbase is an international, multicenter prospective observational study using a customizable web-based modular registry to study the clinical, biological and electrophysiological variation and boundaries of chronic inflammatory demyelinating polyneuropathy (CIDP). The primary objective of INCbase is to develop and validate a clinical prediction model for treatment response. Methods: All patients meeting clinical criteria for CIDP can be included in INCbase. Collected data include demographics, clinical history, diagnostics and various domains of clinical outcomes. Data is collected at a minimum of every 6 months for two years, and more frequently at the discretion of the investigational site to allow for assessment of unexpected changes in treatment response or clinical status. Participants can be enrolled in various sub-studies designed to capture data relevant to specific groups of interest. Data is entered directly into the web-based data entry system by local investigators and/or participants. Collection and local storage of biomaterial is optional. To develop a clinical prediction model for treatment response, newly diagnosed patients with active disease warranting start of first-line treatment will be included. The study population will be split into a development and validation cohort. Univariate and multivariate logistic regression analysis will be used to identify and combine predictors at start of treatment for treatment response at six months. Model performance will be assessed through discrimination and calibration in an external validation cohort. The externally validated prediction model will be made available to researchers and clinicians on the INCbase website. Discussion: With this study, we aim to create a clinically relevant and implementable prediction model for treatment response to first line treatments in CIDP. INCbase enrollment started in April 2021, with 29 centers across 8 countries and 303 patients participating to date. This collaborative effort between academia, patient advocacy organizations and pharmaceutical industry will deepen our understanding of how to diagnose and treat CIDP.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Biomarkers ; Chronic inflammatory demyelinating polyneuropathy ; Clinimetrics ; Diagnosis ; Outcome ; Prediction model ; Prognosis ; Treatment
Publicat a:	BMC Neurology, Vol. 24 Núm. 1 (december 2024) , p. 415, ISSN 1471-2377

DOI: 10.1186/s12883-024-03903-w
PMID: 39455929

10 p, 2.9 MB

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