Xiong Han1, Bin Wang2, Shijun Yang3, Pan Zhao3, Mingmin Li3, Zongya Zhao4, Na Wang3, Huan Ma5, Yue Zhang2, Ting Zhao3, Yanan Chen3, Zhe Ren2, Yang Hong5, Qi Wang2. 1. Department of Neurology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, 450003, Henan Province, China. hanxiong7589@126.com. 2. Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, 450003, Henan Province, China. 3. Department of Neurology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, 450003, Henan Province, China. 4. School of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, China. 5. Department of Neurology, People's Hospital of Henan University, Zhengzhou, 450003, Henan, China.
Abstract
OBJECTIVE: Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. METHODS: One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. RESULTS: An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. CONCLUSION: We constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.
OBJECTIVE: Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. METHODS: One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. RESULTS: An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. CONCLUSION: We constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.
Authors: Kirsten M Fiest; Khara M Sauro; Samuel Wiebe; Scott B Patten; Churl-Su Kwon; Jonathan Dykeman; Tamara Pringsheim; Diane L Lorenzetti; Nathalie Jetté Journal: Neurology Date: 2016-12-16 Impact factor: 9.910
Authors: Merel Wassenaar; Frans S S Leijten; Toine C G Egberts; Karel G M Moons; Sabine G Uijl Journal: Epilepsy Res Date: 2013-07-20 Impact factor: 3.045
Authors: J Engel; S Wiebe; J French; M Sperling; P Williamson; D Spencer; R Gumnit; C Zahn; E Westbrook; B Enos Journal: Neurology Date: 2003-02-25 Impact factor: 9.910
Authors: Bin Wang; Xiong Han; Zongya Zhao; Na Wang; Pan Zhao; Mingmin Li; Yue Zhang; Ting Zhao; Yanan Chen; Zhe Ren; Yang Hong Journal: Front Med (Lausanne) Date: 2022-01-03