St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
Neurocrit Care. 2013 Feb;18(1):143-53. doi: 10.1007/s12028-012-9792-z.
Clinical prediction models can enhance clinical decision-making and research. However, available prediction models in aneurysmal subarachnoid hemorrhage (aSAH) are rarely used. We evaluated the methodological validity of SAH prediction models and the relevance of the main predictors to identify potentially reliable models and to guide future attempts at model development.
We searched the EMBASE, MEDLINE, and Web of Science databases from January 1995 to June 2012 to identify studies that reported clinical prediction models for mortality and functional outcome in aSAH. Validated methods were used to minimize bias.
Eleven studies were identified; 3 developed models from datasets of phase 3 clinical trials, the others from single hospital records. The median patient sample size was 340 (interquartile range 149-733). The main predictors used were age (n = 8), Fisher grade (n = 6), World Federation of Neurological Surgeons grade (n = 5), aneurysm size (n = 5), and Hunt and Hess grade (n = 3). Age was consistently dichotomized. Potential predictors were prescreened by univariate analysis in 36 % of studies. Only one study was penalized for model optimism. Details about model development were often insufficiently described and no published studies provided external validation.
While clinical prediction models for aSAH use a few simple predictors, there are substantial methodological problems with the models and none have had external validation. This precludes the use of existing models for clinical or research purposes. We recommend further studies to develop and validate reliable clinical prediction models for aSAH.
临床预测模型可以增强临床决策和研究。然而,在蛛网膜下腔出血(aSAH)中可用的预测模型很少被使用。我们评估了 aSAH 预测模型的方法学有效性和主要预测因素的相关性,以确定潜在可靠的模型,并指导未来的模型开发尝试。
我们从 1995 年 1 月至 2012 年 6 月在 EMBASE、MEDLINE 和 Web of Science 数据库中搜索了报道 aSAH 死亡率和功能结局的临床预测模型的研究。使用经过验证的方法来最小化偏倚。
确定了 11 项研究;其中 3 项从 3 期临床试验数据集开发了模型,其余的从单个医院记录中开发。患者样本量的中位数为 340(四分位距 149-733)。主要预测因素为年龄(n=8)、Fisher 分级(n=6)、世界神经外科医师联合会分级(n=5)、动脉瘤大小(n=5)和 Hunt 和 Hess 分级(n=3)。年龄始终被分为二项。36%的研究对潜在预测因素进行了单变量分析筛选。只有一项研究因模型乐观而受到惩罚。模型开发的详细信息通常描述不足,也没有发表的研究提供外部验证。
虽然 aSAH 的临床预测模型使用了一些简单的预测因素,但模型存在严重的方法学问题,并且没有经过外部验证。这使得现有的模型无法用于临床或研究目的。我们建议进一步研究以开发和验证可靠的 aSAH 临床预测模型。