[Registers as central real world data source: the experience of the Italian Multiple Sclerosis and Related Disorders Register].

Registers collecting data from clinical practice (real world data) have gained increasing interest in recent years in the scientific, administrative, and regulatory fields. The value of longitudinal data collection in deepening knowledge about a specific pathology and its healthcare complexity is increasingly recognized. This article describes the development, organizational structure, and technical characteristics of the Italian Multiple Sclerosis and Related Disorders Register (RISM). This multicentre and prospective study gathers demographic, clinical, and epidemiological data from the Italian population with multiple sclerosis and related diseases. The study, officially launched in 2015, but containing data collected since the 1990's, currently involves the active participation of 136 specialized clinical centres and more than 80,000 enrolled patients. The analysis of data in RISM allows for a detailed description of the characteristics of multiple sclerosis and related diseases, providing new insights useful for healthcare planning, cost evaluation, treatment efficacy and safety assessment, and scientific research studies. The main demographic and clinical data of enrolled patients are reported, with a focus on specific study cohorts. In a continuous effort to improve data quality, RISM has implemented specific quality indicators. Starting from the RISM experience, crucial aspects such as the institutional recognition of the disease register, the contribution that register can provide in pharmacovigilance studies, the organizational and management challenges, and privacy issues are discussed.

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Publication Details

Journal
PubMed
Published
2024-09-27
DOI
https://doi.org/10.19191/ep24.4-5.a734.074
Citations
1
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.23
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article

[Registers as central real world data source: the experience of the Italian Multiple Sclerosis and Related Disorders Register].

Donatella Corrado, María Trojano, A D'Ettorre, Michela Ponzio et al.
1 citations
PubMed
Biomedical Text Mining and Ontologies
0.23
article

[Registers as central real world data source: the experience of the Italian Multiple Sclerosis and Related Disorders Register].

Donatella Corrado, María Trojano, A D'Ettorre, Michela Ponzio, Marco Salivetto, Mario Alberto Battaglia
article en
1 citations

Abstract

Registers collecting data from clinical practice (real world data) have gained increasing interest in recent years in the scientific, administrative, and regulatory fields. The value of longitudinal data collection in deepening knowledge about a specific pathology and its healthcare complexity is increasingly recognized. This article describes the development, organizational structure, and technical characteristics of the Italian Multiple Sclerosis and Related Disorders Register (RISM). This multicentre and prospective study gathers demographic, clinical, and epidemiological data from the Italian population with multiple sclerosis and related diseases. The study, officially launched in 2015, but containing data collected since the 1990's, currently involves the active participation of 136 specialized clinical centres and more than 80,000 enrolled patients. The analysis of data in RISM allows for a detailed description of the characteristics of multiple sclerosis and related diseases, providing new insights useful for healthcare planning, cost evaluation, treatment efficacy and safety assessment, and scientific research studies. The main demographic and clinical data of enrolled patients are reported, with a focus on specific study cohorts. In a continuous effort to improve data quality, RISM has implemented specific quality indicators. Starting from the RISM experience, crucial aspects such as the institutional recognition of the disease register, the contribution that register can provide in pharmacovigilance studies, the organizational and management challenges, and privacy issues are discussed.

PubMedVol. 48(4-5)
Mario Negri Institute for Pharmacological Research (IT), Associazione Italiana Sclerosi Multipla (IT)
Openalex Percentile: Top 42%
Biomedical Text Mining and Ontologies
0.23
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