A Predictive Model for Identifying Low Medication Adherence Among Patients with Cirrhosis

PURPOSE: This study aims to identify the novel risk predictors of low medication adherence of cirrhosis patients in a large cohort and construct an applicable predictive model to provide clinicians with a simple and precise personalized prediction tool. PATIENTS AND METHODS: Patients with cirrhosis were recruited from the inpatient populations at the Department of Infectious Diseases of Tangdu Hospital. Patients who did not meet the inclusion criteria were excluded. The primary outcome was medication adherence, which was analyzed by the medication possession ratio (MPR). Potential predictive factors, including demographics, the severity of cirrhosis, knowledge of disease and medical treatment, social support, self-care agency and pill burdens, were collected by questionnaires. Predictive factors were selected by univariable and multivariable logistic regression analysis. Then, a nomogram was constructed. The decision curve analysis (DCA), clinical application curve analysis, ROC curve analysis, Brier score and mean squared error (MSE) score were utilized to assess the performance of the model. In addition, the bootstrapping method was used for internal validation. RESULTS: Among the enrolled patients (460), most had good or moderate (344, 74.78%) medical adherence. The main risk factors for non-adherence include young age (≤50 years), low education level, low income, short duration of disease (<10 years), low Child-Plush class, poor knowledge of disease and medical treatment, poor social support, low self-care agency and high pill burden. The nomogram comprised these factors showed good calibration and good discrimination (AUC = 0.938, 95% CI = 0.918–0.956; Brier score = 0.14). In addition, the MSE value was 0.03, indicating no overfitting. CONCLUSION: This study identified predictive factors regarding low medication adherence among patients with cirrhosis, and a predictive nomogram was constructed. This model could help clinicians identify patients with a high risk of low medication adherence and intervention measures can be taken in time.