Can we predict surgical outcomes: A systematic review and critical appraisal of clinical prediction models in epilepsy surgery.

Abstract

Objective: Prediction models are increasingly being sought in epilepsy surgery to predict postoperative outcomes and support clinical decision-making. Studies summarizing the evidence in this area can provide insight into the type of surgical prediction models, their methodology, and their performance and inform areas for future research. Our aim was to address these knowledge gaps through a comprehensive systematic review of prediction models in epilepsy surgery.

Methods: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using four databases. Papers were included if they were primary research studies, human-based, studied adult or pediatric populations, studied people with epilepsy undergoing surgical management, and developed or validated a multivariable tool to predict epilepsy surgery outcomes. Data extraction was reviewed in triplicate, and the quality of evidence in each paper was assessed using the Prediction Model Risk of Bias Assessment Tool.

Results: The literature search yielded a total of 11 614 papers, with 42 papers and 113 prediction models included in the final analysis. The median area under the curve and accuracy for all models were .75 (interquartile range = .68-.83) and .76 (interquartile range = .69-.83), respectively. Overall, 54.0% of models underwent internal validation, and 20.4% underwent external validation. Models of cognitive-language outcomes seemed to perform better than those for other outcomes. Overall risk of bias was high in 81% of models, with weakest performance in outcomes and analyses, but trended toward improvement over time. Concerns for applicability were low in 89% of the models.

Significance: Prediction models in epilepsy surgery are rapidly proliferating, but most lack external validation, and many still exhibit a high risk of bias. Therefore, caution is needed when interpreting and applying these predictive tools. Evidence of improvement in methodological quality holds promise for enhancing patient care, if coupled with improved model performance.