Detection of clinical relapses in multiple sclerosis cohorts: construction and validation of a model based on administrative data

Multiple sclerosis (MS) is the main cause of chronic disability in young people during their most productive years of life and therefore carries a high social and economic burden. The present study aimed to: (1) verify the capacity of an administrative data source to furnish data for constructing a...

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Published in:Neurological sciences 2014-02, Vol.35 (2), p.265-269
Main Authors: Bergamaschi, Roberto, Agnello, Mauro, Colombo, Elena, Della Giovanna, Mirosa, Montomoli, Cristina, Nava, Antonio, Perotti, Pietro, Riva, Maurizio, Ruggeri, Isabella, Scola, Renato, Silvestri, Maria Grazia, Tringali, Michele, Uggeri, Babila, Violante, Giovandomenico, Zaffaroni, Mauro
Format: Article
Language:English
Subjects:
Databases, Factual
Female
Humans
Italy
Male
Medicine
Medicine & Public Health
Models, Neurological
Multiple Sclerosis - diagnosis
Multiple Sclerosis - drug therapy
Multiple Sclerosis, Relapsing-Remitting - diagnosis
Multiple Sclerosis, Relapsing-Remitting - drug therapy
Neurology
Neuroradiology
Neurosciences
Neurosurgery
Original Article
Psychiatry
Recurrence
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Summary:Multiple sclerosis (MS) is the main cause of chronic disability in young people during their most productive years of life and therefore carries a high social and economic burden. The present study aimed to: (1) verify the capacity of an administrative data source to furnish data for constructing a model able to detect the occurrence of clinical relapses in MS patients and (2) validate the constructed theoretical model on a set of real-world data. Two MS experts identified some administrative variables as proxies of clinical relapses. Thereafter, the two MS experts analysed 889 events in 100 MS patients, considering only the administrative data relating to these patients, while a third neurologist independently analysed the real-world data (documented medical history) of the same patients in the same period. Absolute concordance between the theoretical model and the real-world data was found in 86 % of the events. The model we propose is easily and rapidly applicable, requiring the collection of just a few variables that are already present in local health authority administrative databases in Italy. It can be used to estimate, with a good level of reliability, the occurrence of relapses in various settings. Moreover, the model is also exportable to different and larger MS cohorts and could be useful for healthcare planning and for evaluating the efficacy of drugs in the real-world, thus favouring better resource allocation and management.
ISSN:1590-1874
1590-3478
DOI:10.1007/s10072-013-1503-9