Centre for Medical Decision Making, Department of Public Health, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Lancet Neurol. 2010 May;9(5):543-54. doi: 10.1016/S1474-4422(10)70065-X.
Traumatic brain injury (TBI) is a heterogeneous condition that encompasses a broad spectrum of disorders. Outcome can be highly variable, particularly in more severely injured patients. Despite the association of many variables with outcome, prognostic predictions are notoriously difficult to make. Multivariable analysis has identified age, clinical severity, CT abnormalities, systemic insults (hypoxia and hypotension), and laboratory variables as relevant factors to include in models to predict outcome in individual patients. Advances in statistical modelling and the availability of large datasets have facilitated the development of prognostic models that have greater performance and generalisability. Two prediction models are currently available, both of which have been developed on large datasets with state-of-the-art methods, and offer new opportunities. We see great potential for their use in clinical practice, research, and policy making, as well as for assessment of the quality of health-care delivery. Continued development, refinement, and validation is advocated, together with assessment of the clinical impact of prediction models, including treatment response.
创伤性脑损伤(TBI)是一种异质性疾病,包含广泛的疾病谱。其结果可能高度可变,尤其是在更严重的损伤患者中。尽管许多变量与结果相关,但预后预测一直非常困难。多变量分析已经确定年龄、临床严重程度、CT 异常、全身损伤(缺氧和低血压)以及实验室变量是纳入模型以预测个体患者预后的相关因素。统计建模的进步和大型数据集的可用性促进了预后模型的发展,这些模型具有更好的性能和通用性。目前有两种预测模型,它们都是使用最先进的方法在大型数据集上开发的,提供了新的机会。我们认为它们在临床实践、研究和决策制定中,以及在评估医疗保健服务质量方面都具有很大的应用潜力。我们提倡继续开发、改进和验证这些模型,并评估预测模型的临床影响,包括治疗反应。