Establishment and validation of an individualized predictive model for medication compliance in epilepsy adult patients

CHEN Wenjing; XI Conglin; SONG Jing

doi:10.7619/jcmp.20232675

Journal of Clinical Medicine in Practice > 2023 > 27(22): 118-122. > DOI: 10.7619/jcmp.20232675

CHEN Wenjing, XI Conglin, SONG Jing. Establishment and validation of an individualized predictive model for medication compliance in epilepsy adult patients[J]. Journal of Clinical Medicine in Practice, 2023, 27(22): 118-122. DOI: 10.7619/jcmp.20232675

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Establishment and validation of an individualized predictive model for medication compliance in epilepsy adult patients

Department of Neurology, the Second People's Hospital of Huai'an of Jiangsu Province, Huai'an, Jiangsu, 223003

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Received Date: August 21, 2023
Revised Date: October 11, 2023
Available Online: December 05, 2023

Graphical Abstract

Abstract

Abstract

Objective
To establish an individualized prediction model for medication compliance in epilepsy adult patients and verify its predictive effect.
Methods
A total of 192 epilepsy patients 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 patients' 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 accuracy.
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.
- epilepsy,
- medication compliance,
- nomogram model,
- drug side effects,
- social support

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References (17)

References

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Establishment and validation of an individualized predictive model for medication compliance in epilepsy adult patients