1.Establishment and validation of an individualized predictive model for medication compliance in epilepsy adult patients
Wenjing CHEN ; Conglin XI ; Jing SONG
Journal of Clinical Medicine in Practice 2023;27(22):118-122
Objective To establish an individualized prediction model for medication compli-ance in epilepsy adult patients and verify its predictive effect.Methods A total of 192 epilepsy pa-tients were selected as study objects,the Morisky Medication Adherence Scale(MMAS)was applied to assess patients'medication compliance,and patients were divided into compliance group(MMAS score of 6 to 8 points)and control group(MMAS score<6 points)based on their compliance status.The general information,disease information,medication information,and social support of patients were analyzed.Multiple Logistic regression analysis was applied to analyze the influencing factors of pa-tients'medication compliance,the rms program package in R software was applied to establish a column chart model for predicting medication adherence,and the area(AUC)under the receiver operating characteristic(ROC)curve and the calibration curve were used to test its discrimination and accuracy.Results Among the 192 patients,119(61.98%)had good medication adherence and 73(38.02%)had poor medication adherence.The main influencing factors of medication compliance in epilepsy patients included educational background,antiepileptic drug medication type,side effects of drugs,frequency of medication knowledge education,and social support(P<0.05);a column chart model for medication compliance in epilepsy patients was constructed based on five predictive indicators,the results showed that the AUC was 0.830(95%CI,0.772 to 0.888),and the calibration curve and ideal curve trends were similar,the goodness of fit Hosmer-Lemeshow test showed that thechi-square value was 9.970 and P value was 0.267,indicating that the model had high discrimination and accu-racy.Conclusion The nomogram established based on educational background,administration of antiepileptic drug type,side effects of drugs,frequency of medication knowledge education,and social support has a good predictive value in medication compliance in epilepsy patients.
2.Establishment and validation of an individualized predictive model for medication compliance in epilepsy adult patients
Wenjing CHEN ; Conglin XI ; Jing SONG
Journal of Clinical Medicine in Practice 2023;27(22):118-122
Objective To establish an individualized prediction model for medication compli-ance in epilepsy adult patients and verify its predictive effect.Methods A total of 192 epilepsy pa-tients were selected as study objects,the Morisky Medication Adherence Scale(MMAS)was applied to assess patients'medication compliance,and patients were divided into compliance group(MMAS score of 6 to 8 points)and control group(MMAS score<6 points)based on their compliance status.The general information,disease information,medication information,and social support of patients were analyzed.Multiple Logistic regression analysis was applied to analyze the influencing factors of pa-tients'medication compliance,the rms program package in R software was applied to establish a column chart model for predicting medication adherence,and the area(AUC)under the receiver operating characteristic(ROC)curve and the calibration curve were used to test its discrimination and accuracy.Results Among the 192 patients,119(61.98%)had good medication adherence and 73(38.02%)had poor medication adherence.The main influencing factors of medication compliance in epilepsy patients included educational background,antiepileptic drug medication type,side effects of drugs,frequency of medication knowledge education,and social support(P<0.05);a column chart model for medication compliance in epilepsy patients was constructed based on five predictive indicators,the results showed that the AUC was 0.830(95%CI,0.772 to 0.888),and the calibration curve and ideal curve trends were similar,the goodness of fit Hosmer-Lemeshow test showed that thechi-square value was 9.970 and P value was 0.267,indicating that the model had high discrimination and accu-racy.Conclusion The nomogram established based on educational background,administration of antiepileptic drug type,side effects of drugs,frequency of medication knowledge education,and social support has a good predictive value in medication compliance in epilepsy patients.

Result Analysis
Print
Save
E-mail