Dataset Information

Late epileptic seizures following cerebral venous thrombosis: a systematic review and meta-analysis.

ABSTRACT:

Background

Identifying late epileptic seizures (LS) following cerebral venous thrombosis (CVT) can be useful for prognosis and management. We systematically reviewed the literature to identify risk factors for LS due to CVT.

Methods

We systematically searched PubMed, Scholar, and Scopus databases (May 2021) to identify studies reporting data on prevalence and risk factors for CVT-LS. The methodological quality was assessed with the Ottawa-Newcastle Scale. The risk of developing CVT-LS was summarized in meta-analyses and expressed as odds ratio (OR) and corresponding 95% confidence intervals (CIs) using random-effects models.

Results

Out of the 332 records retrieved, four studies were eventually included with a total of 1309 patients with CVT and 142 (11%) with CVT-LS. The most relevant predictors of CVT-LS were symptomatic seizures (OR 5.66, 95% CI 3.83-8.35), stupor/coma (OR 6.81, 95% CI 1.18-39.20), focal neurologic signs (OR 6.81, 95% CI 1.18-39.2), hemorrhagic component (OR 3.52, 95% CI 2.45-5.06), and superior sagittal sinus involvement (OR 1.52, 95% CI 1.04-2.21).

Conclusion

There are several risk factors for CVT-LS that should be considered in clinical practice. Further high-quality studies are warranted to develop predictive models for individualized risk stratification and prediction of CVT-LS.

SUBMITTER: Gasparini S

PROVIDER: S-EPMC9153875 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Late epileptic seizures following cerebral venous thrombosis: a systematic review and meta-analysis.

Gasparini Sara S Neri Sabrina S Brigo Francesco F Cianci Vittoria V Mammì Anna A Pascarella Angelo A Manzo Lucia L Benna Paolo P Striano Pasquale P Martino Tommaso T Romoli Michele M Muccioli Lorenzo L Nilo Annacarmen A Valente Mariarosaria M Cagnetti Claudia C Labate Angelo A Gambardella Antonio A Pisani Francesco F Casciato Sara S Di Gennaro Giancarlo G Belcastro Vincenzo V Aguglia Umberto U Ferlazzo Edoardo E

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 20220531 9

<h4>Background</h4>Identifying late epileptic seizures (LS) following cerebral venous thrombosis (CVT) can be useful for prognosis and management. We systematically reviewed the literature to identify risk factors for LS due to CVT.<h4>Methods</h4>We systematically searched PubMed, Scholar, and Scopus databases (May 2021) to identify studies reporting data on prevalence and risk factors for CVT-LS. The methodological quality was assessed with the Ottawa-Newcastle Scale. The risk of developing CV ...[more]

PMID: 35639217

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Project description:BackgroundReal-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning (ML) has attracted substantial attention from researchers for diagnosing and treating neurological diseases, leading to its application for detecting pediatric epileptic seizures. However, systematic evidence substantiating its feasibility remains limited.ObjectiveThis systematic review aimed to consolidate the existing evidence regarding the effectiveness of ML in monitoring pediatric epileptic seizures with an effort to provide an evidence-based foundation for the development and enhancement of intelligent tools in the future.MethodsWe conducted a systematic search of the PubMed, Cochrane, Embase, and Web of Science databases for original studies focused on the detection of pediatric epileptic seizures using ML, with a cutoff date of August 27, 2023. The risk of bias in eligible studies was assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2). Meta-analyses were performed to evaluate the C-index and the diagnostic 4-grid table, using a bivariate mixed-effects model for the latter. We also examined publication bias for the C-index by using funnel plots and the Egger test.ResultsThis systematic review included 28 original studies, with 15 studies on ML and 13 on deep learning (DL). All these models were based on electroencephalography data of children. The pooled C-index, sensitivity, specificity, and accuracy of ML in the training set were 0.76 (95% CI 0.69-0.82), 0.77 (95% CI 0.73-0.80), 0.74 (95% CI 0.70-0.77), and 0.75 (95% CI 0.72-0.77), respectively. In the validation set, the pooled C-index, sensitivity, specificity, and accuracy of ML were 0.73 (95% CI 0.67-0.79), 0.88 (95% CI 0.83-0.91), 0.83 (95% CI 0.71-0.90), and 0.78 (95% CI 0.73-0.82), respectively. Meanwhile, the pooled C-index of DL in the validation set was 0.91 (95% CI 0.88-0.94), with sensitivity, specificity, and accuracy being 0.89 (95% CI 0.85-0.91), 0.91 (95% CI 0.88-0.93), and 0.89 (95% CI 0.86-0.92), respectively.ConclusionsOur systematic review demonstrates promising accuracy of artificial intelligence methods in epilepsy detection. DL appears to offer higher detection accuracy than ML. These findings support the development of DL-based early-warning tools in future research.Trial registrationPROSPERO CRD42023467260; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023467260.

Dataset Information

Late epileptic seizures following cerebral venous thrombosis: a systematic review and meta-analysis.

Background

Methods

Results

Conclusion

Publications

Late epileptic seizures following cerebral venous thrombosis: a systematic review and meta-analysis.

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