Treatment initiation decisions in newly diagnosed epilepsy–A longitudinal cohort study

Sharma, Sameer; Chen, Zhibin; Rychkova, Maria; Dunne, John W.; Lee, Judy; Kalilani, Linda; Lawn, Nicholas; Kwan, Patrick

doi:10.1111/epi.16439

Cited by 18 publications

(29 citation statements)

References 33 publications

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“…In our study, 51 out 55 (93%) patients with the diagnosis of epilepsy at the long‐term follow‐up were getting treatment with an ASM. This proportion of patients with newly diagnosed epilepsy getting ASM treatment is higher than what has been described in other high‐income countries 12–14 . We do not have a good explanation for this discrepancy but believe this is due to several factors 13 …”

Section: Discussioncontrasting

confidence: 58%

“…This proportion of patients with newly diagnosed epilepsy getting ASM treatment is higher than what has been described in other high-income countries. [12][13][14] We do not have a good explanation for this discrepancy but believe this is due to several factors. 13 The diagnostic accuracy of EEG after a first unprovoked seizure has been reported to be quite low and a recent review and meta-analysis of published studies suggested a sensitivity and specificity of routine EEG to be 17.3% and 94.7% respectively.…”

Section: Discussionmentioning

confidence: 94%

“…[12][13][14] We do not have a good explanation for this discrepancy but believe this is due to several factors. 13 The diagnostic accuracy of EEG after a first unprovoked seizure has been reported to be quite low and a recent review and meta-analysis of published studies suggested a sensitivity and specificity of routine EEG to be 17.3% and 94.7% respectively. 15,16 In our study, the sensitivity and specificity was 41.8% and 96.7%, respectively, at the 28 months follow-up.…”

Section: Discussionmentioning

confidence: 99%

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Long‐term outcome of 200 patients referred to a first seizure clinic

et al. 2020

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Patients presenting to the emergency department or their GP's office with a witnessed or unwitnessed first seizure is a frequent clinical challenge. There are estimates that 8% of the population will experience a seizure in their lifetimes 1 but if these events reflect true epileptic seizures is unclear. Although not every patient with a seizure will go on to develop epilepsy, the likelihood of recurrence

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Section: Discussioncontrasting

confidence: 58%

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

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Long‐term outcome of 200 patients referred to a first seizure clinic

et al. 2020

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“…While previous attempts at the same were done largely through surrogate markers of treatment response, 14, 15, 22 we have the advantage of being able to train our predictive model using relevant clinical information with known standard treatment outcome measures in two large cohorts of newly diagnosed and treated epilepsy patients. 3, 23…”

Section: Limitations and Discussionmentioning

confidence: 99%

Application of transformers for predicting epilepsy treatment response

Choong

Hakeem²,

Chen

et al. 2020

Preprint

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There is growing interest in machine learning based approaches to assist clinicians in treatment selection. In the treatment of epilepsy, a common neurological disorder that affects 70 million people worldwide, previous research has employed scoring methods generated from traditional machine learning methods based on pre-treatment patient characteristics to classify those with drug-resistant epilepsy (DRE). In this study, we used an attention-based approach in predicting the response to different antiseizure medications (ASMs) in individuals with newly diagnosed epilepsy. By applying a conventional transformer to model the patient’s response, we can use the predicted probability to determine the success rate of specific ASMs. Applying the transformer allowed the model to place attention on patient information and past treatments to model future drug responses. We trained a conventional transformer model based on one cohort of 1536 patients with newly diagnosed epilepsy, compared its performance with other trained models using RNN and LSTM, and applied it to a validation cohort of 736 patients. In the development cohort, the transformer model showed the highest accuracy (81%) and AUC (0.85), and maintained similar accuracy and AUC (74% and 0.79, respectively) in the validation cohort.

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“…The coronavirus disease 2019 (COVID-19) pandemic has affected everyone around the world, not least through pressure on healthcare systems and delivery of care. The initial wave and consequent lockdowns led to the cancellation of routine investigations and elective interventions, and many health care providers had to move to alternative models of care delivery [ 1 , 2 , 3 , 4 ]. The new disease started in China in late December 2019 and rapidly spread worldwide, becoming a global pandemic issue [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 Lockdown, during the Two Waves, on Drug Use and Emergency Department Access in People with Epilepsy: An Interrupted Time-Series Analysis

Antonazzo

Fornari

Maumus-Robert

et al. 2021

IJERPH

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Background: In 2020, during the COVID-19 pandemic, Italy implemented two national lockdowns aimed at reducing virus transmission. We assessed whether these lockdowns affected anti-seizure medication (ASM) use and epilepsy-related access to emergency departments (ED) in the general population. Methods: We performed a population-based study using the healthcare administrative database of Tuscany. We defined the weekly time series of prevalence and incidence of ASM, along with the incidence of epilepsy-related ED access from 1 January 2018 to 27 December 2020 in the general population. An interrupted time-series analysis was used to assess the effect of lockdowns on the observed outcomes. Results: Compared to pre-lockdown, we observed a relevant reduction of ASM incidence (0.65; 95% Confidence Intervals: 0.59–0.72) and ED access (0.72; 0.64–0.82), and a slight decrease of ASM prevalence (0.95; 0.94–0.96). During the post-lockdown the ASM incidence reported higher values compared to pre-lockdown, whereas ASM prevalence and ED access remained lower. Results also indicate a lower impact of the second lockdown for both ASM prevalence (0.97; 0.96–0.98) and incidence (0.89; 0.80–0.99). Conclusion: The lockdowns implemented during the COVID-19 outbreaks significantly affected ASM use and epilepsy-related ED access. The potential consequences of these phenomenon are still unknown, although an increased incidence of epilepsy-related symptoms after the first lockdown has been observed. These findings emphasize the need of ensuring continuous care of epileptic patients in stressful conditions such as the COVID-19 pandemic.

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Treatment initiation decisions in newly diagnosed epilepsy–A longitudinal cohort study

Cited by 18 publications

References 33 publications

Long‐term outcome of 200 patients referred to a first seizure clinic

Long‐term outcome of 200 patients referred to a first seizure clinic

Application of transformers for predicting epilepsy treatment response

Impact of COVID-19 Lockdown, during the Two Waves, on Drug Use and Emergency Department Access in People with Epilepsy: An Interrupted Time-Series Analysis

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