Development of an Ambulatory Clinical Pharmacy Prioritization Prediction Model for Patients With Diabetes

Background: Health care reform and the projected shortage of primary care physicians necessitate more efficient use of multidisciplinary collaboration. No studies, to date, have evaluated factors associated with treatment success among patients referred to a clinical pharmacist. Objective: To develop a prediction model using patient factors to assist in selecting patients with diabetes most likely to benefit from pharmacy interventions. Methods: A retrospective, nested case-control study was performed. A prediction model using multivariable logistic regression was developed, with model calibration and internal validation. Adult patients with diabetes who had a baseline hemoglobin A_1C (A1C) ≥9%, who were consulted for collaborative pharmacy care between July 2009 and July 2014 were included. Success (cases) was defined as a decrease in A1C by 2% or a value of A1C <8% 1 year after the initial visit. Failures were those who did not achieve the A1C goal or were lost to follow-up. Results: A total of 544 unique patients were included, with 243 (44.7%) classified as clinical successes and 301 as clinical failures. Independent factors associated with success included past medical history of cerebrovascular accident and higher baseline A1C, whereas use of short-acting insulin and higher number of classes of diabetic medications at baseline corresponded with failure. A prediction model for success using these factors had an optimism-adjusted C-statistics of 0.629, with good calibration. Conclusion: Referred patients with diabetes have baseline characteristics that are predictive of clinical success. A predictive model based on those factors performed well and might be utilized to improve pharmacist efficiency in the face of constrained resources.