Development and validation of a nomogram model for medication non-adherence in patients with chronic kidney disease

Abstract

ObjectiveThe high prevalence of medication non-adherence in patients with chronic kidney disease places a tremendous burden on healthcare resources. The study was designed to develop and validate a nomogram model of medication non-adherence in patients with chronic kidney disease in China. MethodsA multicenter cross-sectional study was conducted. 1206 chronic kidney disease patients were consecutively enrolled from Be Resilient to Chronic Kidney Disease (registration number: ChiCTR2200062288) between September 2021 and October 2022 in four tertiary hospitals in China. The Chinese version of four-item Morisky Medication Adherence Scale was used to assess the medication adherence of the patients and associated factors consisted of socio-demographic information, self-designed medication knowledge questionnaire, the 10-item Connor-Davidson Resilience Scale, the Beliefs about Medicine questionnaire, the Acceptance Illness Scale, and the Family Adaptation Partnership Growth and Resolve Index. Least Absolute Shrinkage and Selection Operator regression was performed to select significant factors. Concordance index, Hosmer-Lemeshow test and decision curve analysis were estimated. ResultsThe prevalence of medication non-adherence was 63.8%. Area under the curves ranged from 0.72 to 0.96 in internal and external validation sets. The predicted probabilities of the model were consistent with those of the actual observations by Hosmer-Lemeshow test (all P > .05). The final model included educational level, occupational status, duration of chronic kidney disease, medication beliefs (perceptions of the need to take medications and concerns about adverse effects), and illness acceptance (adaptation and acceptance of the disease). ConclusionsThere is a high prevalence of medication non-adherence among Chinese patients with chronic kidney disease. A nomogram model based on five factors has been successfully developed and validated and could be incorporated into long-term medication management.