Development and validation of a predictive model for nonadherence with once-daily glaucoma medications

To develop and validate a predictive model to estimate the probability of being nonadherent to topical glaucoma medications.

Prospective cohort study.

Patients being treated with once-daily prostaglandin eye drops.

A predictive model for nonadherence was developed from the Travatan Dosing Aid (TDA) study (n = 196) using stepwise logistic regression. The performance of the TDA-derived model was assessed using a separate cohort of subjects from the Automated Dosing Reminder Study (ADRS; n = 407). The assessment was based on regression coefficients, discrimination, and calibration. We also developed a scoring system from the TDA-derived model to simplify the estimation of risk for clinical use.

Usage of drops was monitored electronically for 3 months in both studies. Adherence was calculated as the percentage of days on which a dose was taken within 4 hours of the average dosing time for that patient. Nonadherence was defined as taking ≤ 75% prescribed doses within a window starting 2 weeks after the baseline visit until 2 weeks before the follow-up visit.

Six factors, including younger age, black race, worse general health status, shorter duration of glaucoma medication therapy, lower self-reported adherence, and admitting to not following doctors' orders, were associated with being nonadherent and were included in the predictive model. The coefficients for the TDA-derived and the ADRS-derived predictive models were similar. The risk scoring system developed from the TDA study had good discrimination (area under the receiver operating characteristic curve of 0.80) and calibration (Hosmer-Lemeshow goodness-of-fit test, P = 0.102) when applied to the ADRS population.

The TDA-derived predictive model for nonadherence performed well in an independent population. A risk scoring system was developed using demographic data and patient responses to 4 questions to provide an estimate of the probability of being nonadherent.