医学
癫痫
重症监护医学
创伤性脑损伤
多样性(控制论)
风险分析(工程)
精神科
人工智能
计算机科学
作者
Yilun Chen,Stefanie P. Cappucci,Jennifer A. Kim
出处
期刊:Seminars in Neurology
[Georg Thieme Verlag KG]
日期:2024-04-15
标识
DOI:10.1055/s-0044-1785502
摘要
Abstract Posttraumatic epilepsy (PTE) is a complication of traumatic brain injury that can increase morbidity, but predicting which patients may develop PTE remains a challenge. Much work has been done to identify a variety of risk factors and biomarkers, or a combination thereof, for patients at highest risk of PTE. However, several issues have hampered progress toward fully adapted PTE models. Such issues include the need for models that are well-validated, cost-effective, and account for competing outcomes like death. Additionally, while an accurate PTE prediction model can provide quantitative prognostic information, how such information is communicated to inform shared decision-making and treatment strategies requires consideration of an individual patient's clinical trajectory and unique values, especially given the current absence of direct anti-epileptogenic treatments. Future work exploring approaches integrating individualized communication of prediction model results are needed.
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