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Epidemiology and treatment of Myasthenia Gravis (MG): A retrospective study in Clinical Practice Research Datalink (CPRD) and Hospital Episodes Statistics (HES)
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UCB Celltech
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Milada Mahic - Chief Investigator - UCB CelltechMilada Mahic - Corresponding Applicant - UCB CelltechNada Boudiaf - Collaborator - UCB BioSciencesInc.
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CPRD687
Myasthenia gravis (MG) is a neuromuscular disease with symptoms of muscular weakness and fatigability and is caused by a problem with the signals sent between the nerves and the muscles. It most commonly affects the muscles that control the eyes and eyelids, facial expressions, chewing, swallowing and speaking. But it can affect most parts of the body. In MG, the immune system damages the communication system between the nerves and muscles, making the muscles weak and easily tired. It's not clear why this happens, but it's been linked to issues with the thymus gland (a gland in the chest that's part of the immune system).
To understand the health care burden due to MG in children and adults, this retrospective cohort study aims to assess epidemiology, treatment patterns for the disease, as well as healthcare resource use (HCRU) in an outpatient and inpatient setting in the UK from 2010 to 2019. Study population will include children and adults with at least one diagnosis of MG in either the CPRD or HES during study period. Epidemiology of MG will be assessed in terms of incidence and prevalence in selected years, and treatment patterns during follow-up will be described among incident patients. Clinical burden of disease in incident patients will be expressed as incidence and event rates of MG clinical events (exacerbations, Myasthenic crisis, specific treatments). Linkage to ONS database will be used to assess time and cause of death. Co-morbidities and health economic burden of the disease in terms of healthcare resource utilization in the inpatient and outpatient settings will be reported as incidence and event rates in MG and randomly sampled matched non-MG cohort, and for this objective groups will be compared using conditional logistic regression model to obtain estimate after adjustment for baseline characteristics.
24/03/2021
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HES Accident and Emergency
HES Admitted Patient Care
HES Outpatient
ONS Death Registration Data
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