Development of a multivariable model to predict medication non-adherence risk factor for patients with acute coronary syndrome

Objective The aim of this study was to develop a risk prediction model for non-adherence to prescribed medication based on self-reported risk factors in patients with the acute coronary syndrome (ACS). MethodsThis is a prospective follow-up cohort study of 210 patients with ACS at a tertiary hospital in Al Ain city in the United Arab Emirates. Patients with ACS in the electronic registry who were discharged from the hospital but continued to attend outpatient clinics and were prescribed evidence-based medications were identified and interviewed. Univariate and multivariate logistic regression models were constructed and used as appropriate. SPSS V24 was used for data analysis. Key findings A final predictive model of eight variables was developed for ACS medication nonadherence.The significant predicted risk factors identified in the final model with their odds ratios (ORs) and confidence intervals (CIs) were as follows: poor knowledge of prescribed medications (OR = 1.81; CI = 1.032–3.34; P = 0.010), five or more prescribed medicines (OR = 4.97; CI = 1.98–2.49; P = 0.007), more than twice daily dosing regimen (OR = 2.21; CI = 1.04–4.67; P = 0.039), unpleasant side-effects (OR = 2.97; CI = 1.98–2.49; P = 0.007), patients believed that side-effects were the cause of health problems (OR = 4.28; CI = 1.78–10.39; P = 0.001), patients undertaking regular exercise (OR = 2.14; CI = 1.06–4.32; P = 0.035), and comorbid diabetes (OR = 1.97; CI = 1.00–3.87; P = 0.049). Conclusion This study indicates poor knowledge, polypharmacy and comorbidity as risk factors associated with medication non-adherence among patients with ACS. Identification of predictors of non-adherence and strategies has the potential to reduce non-adherence dramatically. © The Author(s) 2021. Published by Oxford University Press on behalf of the Royal Pharmaceutical Society. All rights reserved.