Anderson Matheus Pereira da Silva , Victor Arthur Ohannesian , Luciano Falcão , Filipe Virgilio Ribeiro , Isabelle Rodrigues Menezes , Mariana Leticia de Bastos Maximiano , Mariana Lee Han , Lucas Silva Cabeça , Pedro Lucas Machado Magalhães , Gustavo Sousa Noleto , Maria Bernadete de Sousa Maia , Eryvelton de Souza Franco
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引用次数: 0
Abstract
Background
Epilepsy affects approximately 6.4 per 1000 individuals worldwide. Despite advances in antiseizure medications (ASMs), nearly one-third of patients remain refractory to treatment, meeting criteria for drug-resistant epilepsy (DRE). This meta-analysis evaluated the performance of machine learning (ML) models in predicting therapeutic response in DRE based on ILAE-defined outcomes.
Methods
A systematic review and meta-analysis were conducted in accordance with PRISMA-DTA guidelines. Studies applying ML algorithms to predict treatment response in DRE were included. Eligible designs comprised retrospective or prospective observational studies. Outcomes included seizure remission (ILAE Class 1), ≥ 50 % reduction in seizure frequency, treatment failure, and diagnostic accuracy metrics. A bivariate random-effects model was used to pool sensitivity, specificity, and diagnostic odds ratio (DOR). Heterogeneity was assessed (I², χ²), and subgroup and meta-regression analyses were performed. Likelihood ratios, area under the curve (AUC), and Bayesian post-test probabilities were estimated. Publication bias was evaluated with Deeks’ test.
Results
Eight studies (n = 1887) met inclusion criteria (Kappa=0.98). Pooled sensitivity and specificity were both 0.84 (95 % CI: 0.76–0.89 and 0.77–0.89, respectively), with an AUC of 0.91. DOR was 27; LR+ , 5.2; LR−, 0.19. Heterogeneity was high (χ²=22.7; p < 0.001). Sensitivity was lower in prospective studies, prognostic models, and ASMs users, and higher with long-term follow-up. Meta-regression identified model type, study design, ASM exposure, and sample adequacy as key moderators.
Conclusion
ML models demonstrate high diagnostic accuracy in predicting therapeutic response in DRE. Findings support their potential clinical utility, provided external validation and methodological standardisation.
期刊介绍:
Epilepsy Research provides for publication of high quality articles in both basic and clinical epilepsy research, with a special emphasis on translational research that ultimately relates to epilepsy as a human condition. The journal is intended to provide a forum for reporting the best and most rigorous epilepsy research from all disciplines ranging from biophysics and molecular biology to epidemiological and psychosocial research. As such the journal will publish original papers relevant to epilepsy from any scientific discipline and also studies of a multidisciplinary nature. Clinical and experimental research papers adopting fresh conceptual approaches to the study of epilepsy and its treatment are encouraged. The overriding criteria for publication are novelty, significant clinical or experimental relevance, and interest to a multidisciplinary audience in the broad arena of epilepsy. Review articles focused on any topic of epilepsy research will also be considered, but only if they present an exceptionally clear synthesis of current knowledge and future directions of a research area, based on a critical assessment of the available data or on hypotheses that are likely to stimulate more critical thinking and further advances in an area of epilepsy research.