Quantitative versus qualitative blood amount assessment as a predictor for shunt-dependent hydrocephalus following aneurysmal subarachnoid hemorrhage
Quantitative versus qualitative blood amount assessment as a predictor for shunt-dependent hydrocephalus following aneurysmal subarachnoid hemorrhage
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DOI：
10.3171/2018.7.jns18816
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发表时间：
2019-12-01
影响因子：
4.1
通讯作者：
Ensenat, Joaquim
中科院分区：
文献类型：
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作者：
Garcia, Sergio;Torne, Ramon;Ensenat, Joaquim
目的 缺乏可靠的工具来预测动脉瘤性蛛网膜下腔出血 (aSAH) 后分流依赖性脑积水 (SDHC) 的发展。出血量的定量测量是 SDHC 的良好预测指标，但在临床环境中可能不切实际。使用改进的 Fisher 量表 (mFisher) 和原始 Graeb 量表 (oGraeb) 等量表进行的定性评估更容易进行，但预测能力有限。其间，修改后的 Graeb 量表 (mGraeb) 保持了定性量表的简单性，但增加了对急性脑积水的评估，这可能会提高 SDHC 的预测能力。在这项研究中，作者调查了 mGraeb 的可能功能，并将其与之前验证的方法进行了比较。这项研究还旨在为 SDHC 预测定义定制的 mGraeb 截止点。 方法 作者对 2013 年 5 月至 2016 年 4 月期间入院诊断为 aSAH 的患者进行了回顾性分析。在 168 名患者中，78 名在应用预定义的排除标准后被纳入分析。进行单变量和多变量分析以评估所有 4 种方法（定量体积评估以及 mFisher、oGraeb 和 mGraeb 量表）的使用情况，以根据临床数据和初始 CT 扫描的血量评估来预测 SDHC 发生的可能性。 结果 mGraeb 量表被证明是 SDHC 最稳健的预测因子，曲线下面积 (AUC) 为 0.848（95% CI） 0.763-0.933）。根据AUC结果，mGraeb量表的表现显着优于oGraeb量表（chi(2) = 4.49；p = 0.034）和mFisher量表（chi(2) = 7.21；p = 0.007）。 mGraeb 和定量体积测量模型的 AUC 之间没有发现统计差异（chi(2) = 12.76；p = 0.23），但 mGraeb 被证明是最简单的模型，因为它显示出最低的 Akaike 信息标准 (66.4)、最低的贝叶斯信息标准 (71.2) 和最高的 R-Nagelkerke(2) 系数 (39.7%)。最初的 mGraeb 对评分为 12 分或以上的患者显示出超过 85% 的 SDHC 发展特异性。 结论 根据作者的数据，mGraeb 量表是与 SDHC 发展密切相关的最简单模型。由于针对 SDHC 预防的治疗的科学证据有限，我们建议 mGraeb 评分高于 12，以高度特异性地识别高风险患者。该 mGraeb 截止点也可能作为有用的预后工具，因为 aSAH 后 SDHC 患者的功能结果较差。
OBJECTIVE Reliable tools are lacking to predict shunt-dependent hydrocephalus (SDHC) development after aneurysmal subarachnoid hemorrhage (aSAH). Quantitative volumetric measurement of hemorrhagic blood is a good predictor of SDHC but might be impractical in the clinical setting. Qualitative assessment performed using scales such as the modified Fisher scale (mFisher) and the original Graeb scale (oGraeb) is easier to conduct but provides limited predictive power. In between, the modified Graeb scale (mGraeb) keeps the simplicity of the qualitative scales yet adds assessment of acute hydrocephalus, which might improve SDHC-predicting capabilities. In this study the authors investigated the likely capabilities of the mGraeb and compared them with previously validated methods. This research also aimed to define a tailored mGraeb cutoff point for SDHC prediction.METHODS The authors performed retrospective analysis of patients admitted to their institution with the diagnosis of aSAH between May 2013 and April 2016. Out of 168 patients, 78 were included for analysis after the application of predefined exclusion criteria. Univariate and multivariate analyses were conducted to evaluate the use of all 4 methods (quantitative volumetric assessment and the mFisher, oGraeb, and mGraeb scales) to predict the likelihood of SDHC development based on clinical data and blood amount assessment on initial CT scans.RESULTS The mGraeb scale was demonstrated to be the most robust predictor of SDHC, with an area under the curve (AUC) of 0.848 (95% CI 0.763-0.933). According to the AUC results, the performance of the mGraeb scale was significantly better than that of the oGraeb scale (chi(2) = 4.49; p = 0.034) and mFisher scale (chi(2) = 7.21; p = 0.007). No statistical difference was found between the AUCs of the mGraeb and the quantitative volumetric measurement models (chi(2) = 12.76; p = 0.23), but mGraeb proved to be the simplest model since it showed the lowest Akaike information criterion (66.4), the lowest Bayesian information criterion (71.2), and the highest R-Nagelkerke(2) coefficient (39.7%). The initial mGraeb showed more than 85% specificity for predicting the development of SDHC in patients presenting with a score of 12 or more points.CONCLUSIONS According to the authors' data, the mGraeb scale is the simplest model that correlates well with SDHC development. Due to limited scientific evidence of treatments aimed at SDHC prevention, we propose an mGraeb score higher than 12 to identify patients at risk with high specificity. This mGraeb cutoff point might also serve as a useful prognostic tool since patients with SDHC after aSAH have worse functional outcomes.