Health-related quality of life in patients with poststroke epilepsy

Winter, Yaroslav; Daneshkhah, Naeimeh; Galland, Nikolaus; Kotulla, Isabel; Krüger, Anna; Groppa, Sergiu

doi:10.1016/j.yebeh.2017.12.037

Cited by 36 publications

(34 citation statements)

References 28 publications

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“…Quality of life (QOL) is a term that is loosely defined as well-being, looking at an individual's satisfaction with life, and includes physical functioning, mental health, and social roles [34,35]. Some studies used validated questionnaires to measure QOL [36][37][38] while others adapted measures for their studies [32,33], making comparisons difficult.…”

Section: Quality Of Lifementioning

confidence: 99%

“…It is unclear whether older people with new onset or chronic epilepsy have worse QOL as studies had conflicting results [32,33,39]. Developing epilepsy after stroke was found to be associated with worse QOL scores [38]. Depression, more frequent seizures and stigma were found to be associated with lower QOL scores in older people with epilepsy [28,29,40].…”

Section: Quality Of Lifementioning

confidence: 99%

“…Some studies specifically excluded people with a history of stroke, while others included them. Only two studies focussed on post-stoke epilepsy [38,46], which highlights the need for more stroke focussed research.…”

Section: Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Best care for older people with epilepsy: A scoping review

Wojewodka

McKinlay

Ridsdale

2021

Seizure

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Section: Quality Of Lifementioning

confidence: 99%

Section: Quality Of Lifementioning

confidence: 99%

See 1 more Smart Citation

Best care for older people with epilepsy: A scoping review

Wojewodka

McKinlay

Ridsdale

2021

Seizure

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“…They relate to a worse neurological outcome and higher mortality in the short term [2][3][4][5][6]. Long-term consequences are, amongst others, loss of independence, impaired self-confidence, and a higher risk of fractures after falling [7][8][9]. Prevention of seizures would therefore be worthwhile.…”

Section: Introductionmentioning

confidence: 99%

Treatment with Diazepam in Acute Stroke Prevents Poststroke Seizures: A Substudy of the EGASIS Trial

et al. 2021

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Objective: The frequency of seizures after stroke is high, with a severe impact on the quality of life. However, little is known about their prevention. Therefore, we investigated whether early administration of diazepam prevents the development of seizures in acute stroke patients. Methods: We performed a substudy of the EGASIS trial, a multicenter double-blind, randomized trial in which acute stroke patients were treated with diazepam or placebo for 3 days. Follow-up was after 2 weeks and 3 months. The occurrence of seizures was registered prospectively as one of the prespecified secondary outcomes. Results: 784 EGASIS patients were eligible for this substudy (389 treated with diazepam [49.6%] and 395 treated with placebo [50.4%]). Seizures were reported in 19 patients (2.4% of the total patient group). Seizures occurred less frequently in patients treated with diazepam (1.5 vs. 3.3% in the placebo group); however, this difference was only statistically significant in patients with a cortical anterior circulation infarction (0.9% in the diazepam group vs. 4.6% in the placebo group, incidence rate ratio 0.20, 95% CI: 0.05–0.78, p = 0.02, NNT = 27). Conclusion: We found that a 3-day treatment with diazepam after acute cortical anterior circulation stroke prevents the occurrence of seizures in the first 3 months following stroke.

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“…With the increasing number of aging population, the number of patients with post-stroke epilepsy (PSE) will continue to increase. PSE significantly reduced the quality of life of stroke patients, and increased the economic and psychological burden of the patients and their families [5]. Therefore, the prediction of patients with high risk of PSE is very important [2,6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of post-stroke epilepsy using machine learning method

Peng¹,

Yang²,

Wen³

et al. 2020

Preprint

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Background : Stroke is one of the most important causes of epilepsy and we aimed to find if it is possible to predict patients with high risk of developing post-stroke epilepsy (PSE) at the time of discharge using machine learning methods. Methods : Patients with stroke were enrolled and followed at least one year. Machine learning methods including support vector machine (SVM), random forest (RF) and logistic regression (LR) were used to learn the data. Results : A total of 2730 patients with cerebral infarction and 844 patients with cerebral hemorrhage were enrolled and the risk of PSE was 2.8% after cerebral infarction and 7.8% after cerebral hemorrhage in one year. Machine learning methods showed good performance in predicting PSE. The area under the receiver operating characteristic curve (AUC) for SVM and RF in predicting PSE after cerebral infarction was close to 1 and it was 0.92 for LR. When predicting PSE after cerebral hemorrhage, the performance of SVM was best with AUC being close to 1, followed by RF ( AUC = 0.99) and LR (AUC = 0.85) . Conclusion : Machine learning methods could be used to predict patients with high risk of developing PSE, which will help to stratify patients with high risk and start treatment earlier. Nevertheless, more work is needed before the application of thus intelligent predictive model in clinical practice.

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Health-related quality of life in patients with poststroke epilepsy

Cited by 36 publications

References 28 publications

Best care for older people with epilepsy: A scoping review

Best care for older people with epilepsy: A scoping review

Treatment with Diazepam in Acute Stroke Prevents Poststroke Seizures: A Substudy of the EGASIS Trial

Prediction of post-stroke epilepsy using machine learning method

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