BACKGROUND: Attempts to predict who is at risk of future nonadherence have largely focused on predictions at the time of therapy initiation; however, these users are only a small proportion of all patients on therapy at any point in time. Methods to predict nonadherence for established medication users, which have not been previously described in the literature, would be helpful to guide efforts to enhance the use of evidence-based therapies. OBJECTIVE: To test approaches for adherence prediction among prevalent statin users, namely the use of short-term filling behavior, investigator-specified predictors from medical and pharmacy administrative claims, and the empirical selection of potential predictors using the high-dimensional propensity score variable selection algorithm. METHODS: Medical and prescription claims data from a large national health insurer were used to create a cohort of patients who filled statin medication prescriptions in January 2012. We defined 6 groups of adherence predictors and estimated 10 main models to predict medication adherence in the full cohort. The same was done for the population stratified based on the days supply of the index statin prescription (≤ 30 days vs. > 30 days). RESULTS: The study cohort consisted of 93,777 individuals, 58.4% of which were adherent to statins during follow-up. The use of 3 pre-index adherence predictors alone achieved a c-statistic of 0.70. Investigator-specified and empirically selected pharmacy, medical, and demographic variables did substantially worse (0.57-0.60). The use of 3 indicators of post-index adherence achieved a higher c-statistic than the best-performing model using pre-index information (0.74 vs. 0.72). The addition of 3 pre-index adherence predictors further improved discrimination (0.78). CONCLUSIONS: This analysis demonstrated the ability to predict adherence among medication users using filling behavior before and immediately after an index prescription fill. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank, Brennan, and Matlin were employees and shareholders at CVS Health at the time of this manuscript preparation; they report no financial interests in products or services that are related to the subject of the manuscript. Franklin has received consulting fees from Aetion. Chourdry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, and MediSafe. Spettell is an employee of, and shareholder in, Aetna. The other authors have nothing to disclose. Krumme, Choudhry, Tong, and Franklin contributed to the study design, interpretation of results, and manuscript drafting. Tong prepared and analyzed the data. Isaman, Spettell, Shrank, Brennan, and Matlin provided interpretation of results and critical manuscript revisions.
BACKGROUND: Attempts to predict who is at risk of future nonadherence have largely focused on predictions at the time of therapy initiation; however, these users are only a small proportion of all patients on therapy at any point in time. Methods to predict nonadherence for established medication users, which have not been previously described in the literature, would be helpful to guide efforts to enhance the use of evidence-based therapies. OBJECTIVE: To test approaches for adherence prediction among prevalent statin users, namely the use of short-term filling behavior, investigator-specified predictors from medical and pharmacy administrative claims, and the empirical selection of potential predictors using the high-dimensional propensity score variable selection algorithm. METHODS: Medical and prescription claims data from a large national health insurer were used to create a cohort of patients who filled statin medication prescriptions in January 2012. We defined 6 groups of adherence predictors and estimated 10 main models to predict medication adherence in the full cohort. The same was done for the population stratified based on the days supply of the index statin prescription (≤ 30 days vs. > 30 days). RESULTS: The study cohort consisted of 93,777 individuals, 58.4% of which were adherent to statins during follow-up. The use of 3 pre-index adherence predictors alone achieved a c-statistic of 0.70. Investigator-specified and empirically selected pharmacy, medical, and demographic variables did substantially worse (0.57-0.60). The use of 3 indicators of post-index adherence achieved a higher c-statistic than the best-performing model using pre-index information (0.74 vs. 0.72). The addition of 3 pre-index adherence predictors further improved discrimination (0.78). CONCLUSIONS: This analysis demonstrated the ability to predict adherence among medication users using filling behavior before and immediately after an index prescription fill. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank, Brennan, and Matlin were employees and shareholders at CVS Health at the time of this manuscript preparation; they report no financial interests in products or services that are related to the subject of the manuscript. Franklin has received consulting fees from Aetion. Chourdry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, and MediSafe. Spettell is an employee of, and shareholder in, Aetna. The other authors have nothing to disclose. Krumme, Choudhry, Tong, and Franklin contributed to the study design, interpretation of results, and manuscript drafting. Tong prepared and analyzed the data. Isaman, Spettell, Shrank, Brennan, and Matlin provided interpretation of results and critical manuscript revisions.
Authors: Greta A Bushnell; M Alan Brookhart; Bradley N Gaynes; Scott N Compton; Stacie B Dusetzina; Til Stürmer Journal: Med Care Date: 2018-06 Impact factor: 2.983
Authors: Deepak Voora; Jordan Baye; Adam McDermaid; Smitha Narayana Gowda; Russell A Wilke; Anna Nicole Myrmoe; Catherine Hajek; Eric A Larson Journal: Clin Pharmacol Ther Date: 2022-02-05 Impact factor: 6.903