Continuous medication monitoring: A clinical model to predict adherence to medications among chronic kidney disease patients

Background: An adherence model is required to optimise medication management among chronic kidney disease (CKD) patients, as current assessment methods overestimate the true adherence of CKD patients with complex regimens. An approach to assess adherence to individual medications is required to assist pharmacists in addressing non-adherence. Objective: To develop an adherence prediction model for CKD patients. Methods: This multi-centre, cross-sectional study was conducted in 10 tertiary hospitals in Malaysia using simple random sampling of CKD patients with >1 medication (sample size = 1012). A questionnaire-based collection of patient characteristics, adherence (defined as >80% consumption of each medication for the past one month), and knowledge of each medication (dose, frequency, indication, and administration) was performed. Continuous data were converted to categorical data, based on the median values, and then stratified and analysed. An adherence prediction model was developed through multiple logistic regression in the development group (n = 677) and validated on the remaining one-third of the sample (n = 335). Beta-coefficient values were then used to determine adherence scores (ranging from 0 to 7) based on the predictors identified, with lower scores indicating poorer medication adherence. Results: Most of the 1012 patients had poor medication adherence (n = 715, 70.6%) and half had good medication knowledge (n = 506, 50%). Multiple logistic regression analysis determined 4 significant predictors of adherence: <7 medications (constructed score = 2, p < 0.001), <3 co-morbidities (constructed score = 1, p = 0.015), absence of complementary/alternative medicine use (constructed score = 1, p = 0.003), and knowledge score >80% (constructed score = 3, p < 0.001). A higher total constructed score from the prediction model indicated a higher likelihood of adherence (odds ratio [OR]: 2.41; 95% confidence interval [CI]: 2.112-2.744; p < 0.001). The area under the receiver operating characteristic (ROC) curve of the developed model (n = 677) had good accuracy (ROC: 0.867, 95% CI: 0.840-0.896; p < 0.001). The validated model (n = 335) also had good accuracy (ROC: 0.812, 95% CI: 0.765-0.859; p < 0.001). There was no significant difference between the development and validation groups (p = 0.11, Z-value:1.62, standard error: 0.034). Conclusion: The score constructed from the medication adherence prediction model for CKD patients had good accuracy and could be useful for identifying patients with a higher risk of non-adherence, to ensure optimised adherence management.

Automated summary

The study aimed to develop an adherence prediction model for CKD patients by collecting patient data from 10 tertiary hospitals in Malaysia. Results showed that the model accurately identified high-risk non-adherent patients based on patient characteristics, medication adherence, and medication knowledge.