BACKGROUND: Adverse effects (AEs) of antiepileptic drugs (AEDs) are a major impediment to optimal dosing for seizure control. Better understanding of clinical properties of AEs is a prerequisite for systematic research of their neurobiological underpinnings. This study aimed to define specific patterns of AE occurrence and determine their clinical relevance based on their association with subjective health status. METHODS: Two hundred subjects with epilepsy completed validated self-report health assessments, including the Adverse Event Profile (AEP) and Quality of Life in Epilepsy Inventory (QOLIE)-89. Factor analysis was performed on the 19 AEP items to identify distinct classes of AEs. Correlations between AE class scores and QOLIE-89 scores were evaluated. Multivariate analysis was used to assess contributions of AE class scores to QOLIE-89 scores after controlling for depression and seizure frequency. Relationships between changes in AE class scores and changes in QOLIE-89 scores were also investigated in a subgroup of 62 subjects enrolled in a randomized trial. RESULTS: The mean number of AEs per subject was 6.5. AEs were segregated into five classes: Cognition/Coordination, Mood/Emotion, Sleep, Weight/Cephalgia, and Tegument/Mucosa. Higher scores in each AE class were associated with lower QOLIE-89 scores. Cognition/Coordination scores were the strongest predictor of QOLIE-89 scores. Improvements in Cognition/Coordination, Mood/Emotion, and Tegument/Mucosa scores were associated with improvements in QOLIE-89 scores. Improved Cognition/Coordination was the only predictor of improved QOLIE-89. CONCLUSION: Adverse effects (AEs) of antiepileptic drugs can be classified in five biologically plausible factors. When specific classes of AEs are identified and attempts are made to reduce them, quality of life is significantly improved.
BACKGROUND: Adverse effects (AEs) of antiepileptic drugs (AEDs) are a major impediment to optimal dosing for seizure control. Better understanding of clinical properties of AEs is a prerequisite for systematic research of their neurobiological underpinnings. This study aimed to define specific patterns of AE occurrence and determine their clinical relevance based on their association with subjective health status. METHODS: Two hundred subjects with epilepsy completed validated self-report health assessments, including the Adverse Event Profile (AEP) and Quality of Life in Epilepsy Inventory (QOLIE)-89. Factor analysis was performed on the 19 AEP items to identify distinct classes of AEs. Correlations between AE class scores and QOLIE-89 scores were evaluated. Multivariate analysis was used to assess contributions of AE class scores to QOLIE-89 scores after controlling for depression and seizure frequency. Relationships between changes in AE class scores and changes in QOLIE-89 scores were also investigated in a subgroup of 62 subjects enrolled in a randomized trial. RESULTS: The mean number of AEs per subject was 6.5. AEs were segregated into five classes: Cognition/Coordination, Mood/Emotion, Sleep, Weight/Cephalgia, and Tegument/Mucosa. Higher scores in each AE class were associated with lower QOLIE-89 scores. Cognition/Coordination scores were the strongest predictor of QOLIE-89 scores. Improvements in Cognition/Coordination, Mood/Emotion, and Tegument/Mucosa scores were associated with improvements in QOLIE-89 scores. Improved Cognition/Coordination was the only predictor of improved QOLIE-89. CONCLUSION: Adverse effects (AEs) of antiepileptic drugs can be classified in five biologically plausible factors. When specific classes of AEs are identified and attempts are made to reduce them, quality of life is significantly improved.
Authors: Asra Siddiqui; Reinhold Kerb; Michael E Weale; Ulrich Brinkmann; Alice Smith; David B Goldstein; Nicholas W Wood; Sanjay M Sisodiya Journal: N Engl J Med Date: 2003-04-10 Impact factor: 91.245
Authors: J Hara; C T Beuckmann; T Nambu; J T Willie; R M Chemelli; C M Sinton; F Sugiyama; K Yagami; K Goto; M Yanagisawa; T Sakurai Journal: Neuron Date: 2001-05 Impact factor: 17.173
Authors: Yun S Park; G Rees Cosgrove; Joseph R Madsen; Emad N Eskandar; Leigh R Hochberg; Sydney S Cash; Wilson Truccolo Journal: IEEE Trans Biomed Eng Date: 2019-06-06 Impact factor: 4.538
Authors: Toshihiro Nomura; Nicole A Hawkins; Jennifer A Kearney; Alfred L George; Anis Contractor Journal: J Physiol Date: 2019-05-20 Impact factor: 5.182
Authors: Frank G Gilliam; Kevin J Black; Jewell Carter; Kenneth E Freedland; Yvette I Sheline; Wei-Yann Tsai; Patrick J Lustman Journal: Ann Neurol Date: 2019-08-15 Impact factor: 10.422