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Risk Assessment Tool in Predicting the Therapeutic Outcomes of Antiseizure Medication in Adults with Epilepsy.
Rusli, Rose Aniza; Makmor Bakry, Mohd; Mohamed Shah, Noraida; Loo, Xin Ling; Hung, Stefanie Kar Yan.
Afiliação
  • Rusli RA; Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
  • Makmor Bakry M; Pharmacy Department, Hospital Shah Alam, Shah Alam, Selangor, Malaysia.
  • Mohamed Shah N; Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
  • Loo XL; Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
  • Hung SKY; Pharmacy Department, Hospital Tengku Ampuan Rahimah, Klang, Selangor, Malaysia.
Ther Clin Risk Manag ; 20: 529-541, 2024.
Article em En | MEDLINE | ID: mdl-39220771
ABSTRACT

Aim:

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 (OR1.87,95% CI1.10-3.17), generalized onset (OR7.42,95% CI2.95-18.66), focal onset seizure (OR8.24,95% CI2.98-22.77), non-adherence (OR3.55,95% CI1.52-8.27) and having ≥3 ASM (OR3.29,95% CI1.32-8.24). Younger age at epilepsy onset (≤40) (OR3.29,95% CI1.32-8.24) and neurological deficit (OR3.55,95% CI1.52-8.27) were significant predictors for SS. For AE, the positive predictors were age >35 (OR0.12,95% CI0.03-0.20), <13 years epilepsy duration (OR2.89,95% CI0.50-5.29) and changes in ASM regimen (OR2.93,95% 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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article