Risk Assessment Tool in Predicting the Therapeutic Outcomes of Antiseizure Medication in Adults with Epilepsy

• Main Authors: Rusli RA (Author), Makmor Bakry M (Author), Mohamed Shah N (Author), Loo XL (Author), Hung SKY (Author)
• Format: Book
• Published: Dove Medical Press, 2024-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1178-203X
• Keywords: antiseizure medication; seizure control; risk assessment; therapeutic outcomes; Therapeutics. Pharmacology; RM1-950
• Source: Therapeutics and Clinical Risk Management, Vol Volume 20, Pp 529-541 (2024)

Rose Aniza Rusli,1,2 Mohd Makmor Bakry,1 Noraida Mohamed Shah,1 Xin Ling Loo,3 Stefanie Kar Yan Hung4 1Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia; 2Pharmacy Department, Hospital Shah Alam, Shah Alam, Selangor, Malaysia; 3Pharmacy Department, Hospital Tengku Ampuan Rahimah, Klang, Selangor, Malaysia; 4Neuromedical Unit, Hospital Tengku Ampuan Rahimah, Klang, Selangor, MalaysiaCorrespondence: Mohd Makmor Bakry, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur, 50300, Malaysia, Tel [phone], Fax [phone], Email [e-mail]: Identifying a patient's risk for poor outcomes after starting antiseizure medication (ASM) therapy is crucial in managing epilepsy pharmacologically. To date, there is a lack of designated tools to assess such risks.Purpose: To develop and validate a risk assessment tool for the therapeutic outcomes of ASM therapy.Patients and Methods: A cross-sectional study was carried out in a hospital-based specialist clinic from September 2022 to August 2023. Data was analyzed from patients' medical records and face-to-face assessments. The seizure control domain was determined from the patients' medical records while seizure severity (SS) and adverse effects (AE) of ASM were assessed using the Seizure Severity Questionnaire and the Liverpool Adverse Event Profile respectively. The developed tool was devised from prediction models using logistic and linear regressions. Concurrent validity and interrater reliability methods were employed for validity assessments.Results: A total of 397 patients were included in the analysis. For seizure control, the identified predictors include ≥ 10 years' epilepsy duration (OR:1.87,95% CI:1.10- 3.17), generalized onset (OR:7.42,95% CI:2.95- 18.66), focal onset seizure (OR:8.24,95% CI:2.98- 22.77), non-adherence (OR:3.55,95% CI:1.52- 8.27) and having ≥ 3 ASM (OR:3.29,95% CI:1.32- 8.24). Younger age at epilepsy onset (≤ 40) (OR:3.29,95% CI:1.32- 8.24) and neurological deficit (OR:3.55,95% CI:1.52- 8.27) were significant predictors for SS. For AE, the positive predictors were age > 35 (OR:0.12,95% CI:0.03- 0.20), < 13 years epilepsy duration (OR:2.89,95% CI:0.50- 5.29) and changes in ASM regimen (OR:2.93,95% CI: 0.24- 5.62). The seizure control domain showed a good discriminatory ability with a c-index of 0.711. From the Bonferroni (ANOVA) analysis, only SS predicted scores generated a linear plot against the mean of the actual scores. The AE domain was omitted from the final tool because it did not meet the requirements for validity assessment.Conclusion: This newly developed tool (RAS-TO) is a promising tool that could help healthcare providers in determining optimal treatment strategies for adults with epilepsy.Keywords: antiseizure medication, seizure control, risk assessment, therapeutic outcomes