2014-21: Development of a statistical Uveitis Assessment and Management Model to improve outcome in chronic uveitis patients
Uveitis is the third cause for (legal) blindness in the Western World, in particular in the working society (aged 20 to 65 years). Although the gold standard of evidence in medicine are randomised controlled trials (RCT), in severe visual threatening uveitis,
almost no significantly large RCTs can be found. The underlying systemic diseases and the individual comorbidities,
complications and response to therapy vary significantly, leading to an expert-opinions approach, and vaguely copying the
results of e.g. rheumatic arthritis trials in uveitis practice. Large biological antibody studies in Rheumatology have 2000+
patients, and meta-analysis in 20,000+ patients can be found, whereas in uveitis the largest RCTs have 200 patients with a
diverse mixed uveitis population.
Advantages in computer science and statistics enabled the development and practical application of various data analysis
domains, such as Bayesian statistics, machine learning and Big Data. These techniques may also be applicable to our
healthcare setting. Statistical modelling and analysis in chronic uveitis therefore has the potential to create a personalized care
management profile by combining individual and group-related data optimizing treatment decisions, lowering the risk of long
term complications and preserving vision.
OBJECTIVE:To develop and implement a statistical Uveitis Assessment and Management Model to improve long term outcome
and preservation of visual function, to prevent (ocular) complications and employment disability and to reduce healthcare costs.