Our client, a pharmaceutical company, wished to conduct an observational study in order to generate data to better understand the burden and evolution of a rare lysosomal disease. This disease is a rare disorder characterised by progressive neurological deterioration, for which no specific treatment has been approved. Drugs are being developed that target the glycosphingolipid metabolic pathway, which is implicated in many rare disorders.
The primary objective was to describe the natural history and burden of neurological and non-neurological manifestations in patients with this disease.
The client also wished to describe the treatment patterns and care consumption of patients, and to estimate the incidence and prevalence of this disease in the French general population.
One of the first questions to be asked concerned which data sources could meet these objectives and provide a maximum number of patients suffering from this rare disease. This is where our expert pharmaco-epidemiologist, a specialist in RWE studies, came in to help the client design their study.
In order to meet the abovementioned objectives, it was decided that the data obtained from the review of patients’ medical records would be combined with that from the French national health data system (SNDS) database.
The study was non-interventional and did not involve human subjects. It was conducted in accordance with good pharmaco-epidemiological practice as defined by the International Epidemiological Association, the ethical principles of the Declaration of Helsinki, and French legal and regulatory requirements.
Retrospective data was collected between 2009 and 2020 from patient records. No data allowing for the direct identification of patients was collected in this study. The study population consisted of all patients diagnosed with this rare disease in France. The pharmaco-epidemiologist, a specialist in the SNDS database, also defined the matching variables, i.e. the common non-identifying variables we would use to find our patients from the medical records in the SNDS database (dates of hospitalisation, dates of death, date and type of diagnostic test, etc). In collaboration with the data manager, he also determined the algorithm for identifying our population within the SNDS database (which codes to use for the disease of interest, co-morbidities, treatments used, etc.).
It was the medical writer’s turn to write a synopsis and a protocol detailing the project, in order to submit it to the relevant authorities to obtain authorisation to use data from the SNDS database and medical records. To do so, a precise format had to be adhered to, as well as tight deadlines to get through to the next committee.
Until now, little research had been published to describe the epidemiology, clinical and economic burden, and evolution of this disease. Additional research was therefore needed to better characterise this disease and understand any unmet needs.
In this situation, the collection of observational and retrospective data offers a unique opportunity, given the rarity and progressive nature of this disease. The laboratory is committed to communicating the results to the general public at the end of the project, in accordance with transparency obligations. The data will be used by the health authorities and the laboratory to better identify the type of patients to be included in clinical trials, and to target areas for future research.
