Rubinos Clio, Kwon Soon Bin, Megjhani Murad, Terilli Kalijah, Wong Brenda, Cespedes Lizbeth, Ford Jenna, Reyes Renz, Kirsch Hannah, Alkhachroum Ayham, Velazquez Angela, Roh David, Agarwal Sachin, Claassen Jan, Connolly E Sander, Park Soojin
Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Neurology, Columbia University, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA.
Neurocrit Care. 2022 Dec;37(3):670-677. doi: 10.1007/s12028-022-01538-8. Epub 2022 Jun 25.
Prolonged external ventricular drainage (EVD) in patients with subarachnoid hemorrhage (SAH) leads to morbidity, whereas early removal can have untoward effects related to recurrent hydrocephalus. A metric to help determine the optimal time for EVD removal or ventriculoperitoneal shunt (VPS) placement would be beneficial in preventing the prolonged, unnecessary use of EVD. This study aimed to identify whether dynamics of cerebrospinal fluid (CSF) biometrics can temporally predict VPS dependency after SAH.
This was a retrospective analysis of a prospective, single-center, observational study of patients with aneurysmal SAH who required EVD placement for hydrocephalus. Patients were divided into VPS-dependent (VPS+) and non-VPS dependent groups. We measured the bicaudate index (BCI) on all available computed tomography scans and calculated the change over time (ΔBCI). We analyzed the relationship of ΔBCI with CSF output by using Pearson's correlation. A k-nearest neighbor model of the relationship between ΔBCI and CSF output was computed to classify VPS.
Fifty-eight patients met inclusion criteria. CSF output was significantly higher in the VPS+ group in the 7 days post EVD placement. There was a negative correlation between delta BCI and CSF output in the VPS+ group (negative delta BCI means ventricles become smaller) and a positive correlation in the VPS- group starting from days four to six after EVD placement (p < 0.05). A weighted k-nearest neighbor model for classification had a sensitivity of 0.75, a specificity of 0.70, and an area under the receiver operating characteristic curve of 0.80.
The correlation of ΔBCI and CSF output is a reliable intraindividual biometric for VPS dependency after SAH as early as days four to six after EVD placement. Our machine learning model leverages this relationship between ΔBCI and cumulative CSF output to predict VPS dependency. Early knowledge of VPS dependency could be studied to reduce EVD duration in many centers (intensive care unit length of stay).
蛛网膜下腔出血（SAH）患者长期进行外部脑室引流（EVD）会导致发病，而早期拔除可能会产生与复发性脑积水相关的不良影响。一种有助于确定EVD拔除或脑室腹腔分流术（VPS）置入最佳时间的指标，将有利于防止EVD的长期、不必要使用。本研究旨在确定脑脊液（CSF）生物特征的动态变化是否能在时间上预测SAH后对VPS的依赖。
这是一项对因脑积水需要进行EVD置入的动脉瘤性SAH患者的前瞻性、单中心观察性研究的回顾性分析。患者被分为VPS依赖组（VPS+）和非VPS依赖组。我们在所有可用的计算机断层扫描上测量双尾状核指数（BCI），并计算其随时间的变化（ΔBCI）。我们使用Pearson相关性分析ΔBCI与脑脊液引流量的关系。计算ΔBCI与脑脊液引流量之间关系的k近邻模型以对VPS进行分类。
58例患者符合纳入标准。EVD置入后7天内，VPS+组的脑脊液引流量显著更高。VPS+组中，ΔBCI与脑脊液引流量呈负相关（ΔBCI为负意味着脑室变小），而在EVD置入后第4至6天开始，VPS-组中两者呈正相关（p<0.05）。用于分类的加权k近邻模型的灵敏度为0.75，特异性为0.70，受试者操作特征曲线下面积为0.80。
ΔBCI与脑脊液引流量的相关性是SAH后VPS依赖的可靠个体内生物特征，早在EVD置入后第4至6天即可体现。我们的机器学习模型利用了ΔBCI与累积脑脊液引流量之间的这种关系来预测VPS依赖。可以研究VPS依赖的早期信息，以减少许多中心（重症监护病房住院时间）的EVD持续时间。
