Objective: To recognize poorly compliant glaucoma patients using the Eye-Drop Fulfillment Questionnaire (EDSQ). Among 169 individuals who finished the EDSQ 113 got valid Travalert? data of whom 25 (22.1%) demonstrated low conformity. All 6 EDSQ dimensions were associated or indirectly with conformity directly. Two information exhibited low conformity ie individuals aged young than 77.5 years with a poor patient-physician relationship and self-declared poor patients and compliance aged GTx-024 older than 77.5 years with an unhealthy patient-physician relationship and self-declared good compliance. The 3rd profile showed high compliance ie patients aged younger than 77.5 years with a good patient-physician relationship and self-declared good compliance. Conclusion: Our results confirm a central role for the patient-physician relationship in the compliance process. Age self-declared compliance and patient satisfaction with the patient-physician relationship are all dimensions worth exploring before glaucoma medication is switched or proceeding to laser treatment or surgery. Keywords: glaucoma compliance risk factors patient satisfaction Introduction Glaucoma is the second leading cause of blindness globally.1 From 1991 to 1999 primary open-angle glaucoma prevalence increased from 4.6% to 13.8% among the elderly.2 Its treatment is aimed essentially at lowering intraocular pressure (IOP) by eye drop instillations reserving surgery or laser medical procedures for the most severe cases. Several classes of GTx-024 medicine are available ie prostaglandin analogs miotics beta-blockers alpha-adrenergic agonists and carbonic anhydrase inhibitors. Glaucoma treatment principles and options have been reported by the European Glaucoma Society.3 Successful treatment depends upon strict lifetime adherence to the instillation schedule. Thus higher adherence is usually associated with better IOP control on average 4 and a lower risk of eventual blindness.5 However patients perceive few symptoms in the early stages whereas eye drops (with potential side effects) are needed daily and may become a burden leading to poor treatment adherence.6 Adherence to treatment schedules has been examined by numerous studies in glaucoma using various methods. For example the medication possession ratio determining the mean proportion of days during a given period when patients possess medication was calculated from insurance claims or prescription databases 7 and from electronic devices capturing drop counts.10 Alternatively patients’ self-declared compliance was obtained from interviews11-13 or standardized questionnaires.8 14 Another difference between studies were noncompliance criteria eg patients who missed more than two doses per week18 or possessed insufficient drops for the specified period (medication possession ratio < 1).5 With this array of methodology across different drug classes Fshr and countries compliance rates varied from 59% to 77%.7 11 GTx-024 14 16 18 Even more informally imperfect conformity is reported among glaucoma sufferers consistently. To boost glaucoma care it is advisable to recognize sufferers who might not stick to treatment. Elements conducive to non-compliance have already been explored. For instance organic dosing regimens impact on conformity.19 22 However barriers cited by most research relate with patients’ perception and understanding of their illness and its own treatment.7 11 16 18 These factors prompted the introduction of an Eye-Drop Satisfaction Questionnaire (EDSQ) which asks sufferers to self-report their fulfillment and conformity with topical ophthalmic remedies.23 Replies to these relevant questions should be relevant to an exploration of non-compliance in glaucoma patients. A suitable way of examining such data is certainly GTx-024 a Bayesian network (BN) which facilitates the representation and manipulation of details. A BN is certainly a aimed acyclic graph representing interactions between factors (nodes in the graph) using a related group of conditional possibility dining tables that characterize regional dependencies between your various nodes. Therefore it provides a robust tool to review interdependencies between complicated processes such as for example patient.