Multiple sclerosis (MS) is a disease in which the patientÂ’s own immune system disturbs message transmission in the central nervous system (CNS). Most MS patients experience alternating periods of relapses, i.e. the appearance and disappearance of disease symptoms. Disease-modifying treatments (DMTs) target the immune system to reduce the frequency of relapses and slow the worsening of CNS functioning. MS patients have a higher risk of infection than the general population, and some DMTs that have received market authorization since 2006 are associated with an increased risk of infection. Our primary aim will be to investigate how the occurrence of infections in MS patients has changed over the past 15 years, alongside the changing availability of DMTs. We will investigate the frequency of infections during the year before and the years after MS diagnosis. Our secondary aim is to increase insight in which patient characteristics may predict the risk of infection in MS patients, with particular focus on use of the drugs to treat relapses through their effect on the immune system. The CPRD data will be used to study the numbers of MS patients who experience an infection, and whether they have characteristics related to the number of infections that they experience. The expected benefit of this study is that MS patients and their physicians can make better-informed treatment decisions, and it will provide insights for regulatory authorities into the risk of infections in MS patients.
The aim is to describe the pattern and predictors of infections over the past 15 years in MS patients. A dynamic cohort of MS patients will be studied between January 2003 up until December 2020, who are at least 20 years old and free from malignancy at the moment of recorded diagnosis. In addition, two control groups will be studied with the same age/comorbidity eligibility criteria as above: i) patients with a diagnosis of rheumatoid arthritis (RA) and ii) a random sample of patients from the general population. The control groups will be matched to the MS cohort on age and sex. We will present annual incidence rates of episodes of infection in the MS cohort, and stratify on sex, age group (20-29 years, 30-39, 40-49, 50-59, and 60 and over), and incident/prevalent MS cases. We will perform a regression analysis on the incidence rate of infections during the year before and the years after recorded MS diagnosis for incident cases of MS during the study period. This will be compared to the incidence rates of infection in the two, matched control cohorts. We will also investigate whether associations exist between certain patient characteristics and risk of infection in MS patients. Data on covariates and outcomes are needed: the covariates are various patient characteristics assessed at the time of recorded diagnosis, including current and history of prescribed medicines, and the primary outcome is the incidence rate of episodes of infection per year of follow-up time. To find predictive factors of infection, multivariable analyses will be performed in a Poisson model with the candidate predictor variables as the independent variables and annual incidence rate of infection as the dependent variable. The effect size of each predictive variable will be calculated from its coefficient in the model.
Occurrence of infections of the five major types: urinary tract infection, infection of the pulmonary system, infection of the skin and subcutaneous tissue, sepsis, and infection of the gastrointestinal system.
Marloes Bazelier - Chief Investigator - Utrecht University
Melissa Leung - Corresponding Applicant - Utrecht University
- Collaborator -
Bernard Uitdehaag - Collaborator - VU Medical Centre
Hubert Leufkens - Collaborator - Utrecht University
Kyra Klijmeij - Collaborator - Utrecht University
Marloes Bazelier - Collaborator - Utrecht University
Patrick Souverein - Collaborator - Utrecht University