Abstract
Abstract: :
Purpose: The clinical course of intraocular inflammation can be measured with many different parameters which are composed individually from patient to patient. In a long follow–up, this may be confusing, for example when correlating signs of inflammatory activity with different drug dosing regimens. Although controversial, various scores have been proposed which try to summarize clinical findings with a single numerical value. In order to circumvent the problems associated with a single score, we developed a computer–based system for documentation and evaluation of uveitis–related parameters. This allows one to visualize changes of intraocular inflammation with a standardized graphic. Methods: This software is based on Filemaker Pro 7. In this database, patient’s records including examination results, medication, and other individual data are included. Grading scales according to modified guidelines of the International Ocular Inflammation Society (IOIS) were used. The parameters of relevance are individually selected for each patient and transferred into a radar score. The number of axes in the radar score therefore can be different from patient to patient. Since every parameter used in the radar score has a different unit, all parameter scales have to be normalized to percentages. This normalization varies between 100%, symbolizing relative lack of pathology, and 0%, symbolizing its relative presence. Due to different individual characteristics of intraocular inflammation, the range of each parameter can be adjusted individually for each patient according to the clinical course and optimal graphical fit. Results: The Uveitis Patient Chart summarizes basic data from each patient, such as demographics, anatomical localization, and systemic association, along with the clinical course. In order to visualize follow–up, results of different examinations are summarized in one radar graph demonstrating changes of each selected parameter. Different colors represent different examination dates. The greater the area under the curve the better the clinical findings. Conclusions: This computer based system is easy to handle and offers visualized monitoring of the clinical course of uveitis patients. Furthermore, due to its abstract visualization of clinical findings it helps to improve a patient’s understanding of their disease and potential therapeutic success.
Keywords: clinical (human) or epidemiologic studies: systems/equipment/techniques • uveitis-clinical/animal model • autoimmune disease