From the Department of Anesthesiology, Walter Reed National Military Medical Center, Washington, DC.
The Center for Clinical & Translational Sciences, University of Illinois at Chicago.
Anesth Analg. 2020 Apr;130(4):967-974. doi: 10.1213/ANE.0000000000004383.
Ischemic optic neuropathy (ION) is a rare complication of anesthesia and surgery that causes vision loss in spine fusion. We sought to develop a predictive model based on known preoperative risk factors for perioperative ION to guide patient and physician preoperative decision-making.
In the National Inpatient Sample (NIS) for 1998-2012, discharges for posterior thoracic, lumbar, and sacral spine fusion were identified and classified by ION status. Variables were selected without weighting via variable clustering using Principal Component Analysis of Mixed Data (PCA-MIX). Hierarchical clustering with 4 clusters was performed, and the variable with largest squared loading in each cluster was chosen. By splitting our sample into a training and testing data set, we developed and internally validated a predictive model. The final model using variables known preoperatively was constructed to allow determination of relative and absolute risk of developing perioperative ION and was tested for calibration and discrimination.
The final predictive model based on hierarchical clustering contained 3 preoperative factors, age, male or female sex, and the presence of obstructive sleep apnea (OSA). The predictive model based on these factors had an area under the receiver operating characteristic curve (AUC) of 0.65 and good calibration. A score cutoff of >1 had 100% sensitivity, while score of 3 had 96.5% specificity. The highest estimated absolute risk (844.5/million) and relative risk of ION (46.40) was for a man, age 40-64 years, with OSA.
The predictive model could enable screening for patients at higher risk of ION to provide more accurate risk assessment and surgical and anesthetic planning for perioperative ION in spine fusion.
缺血性视神经病变(ION)是麻醉和手术的罕见并发症,可导致脊柱融合手术中的视力丧失。我们试图根据围手术期 ION 的已知术前危险因素开发一种预测模型,以指导患者和医生进行术前决策。
在 1998 年至 2012 年的全国住院患者样本(NIS)中,通过 ION 状态识别并分类了后胸椎、腰椎和骶骨脊柱融合的出院情况。使用混合数据主成分分析(PCA-MIX)的变量聚类法选择了无权重的变量。进行了 4 个聚类的层次聚类,每个聚类中选择具有最大平方载荷的变量。通过将我们的样本分为训练集和测试集,我们开发并内部验证了一种预测模型。使用术前已知变量构建最终模型,以确定发生围手术期 ION 的相对和绝对风险,并对其进行校准和区分能力的测试。
基于层次聚类的最终预测模型包含 3 个术前因素,即年龄、性别(男性或女性)和阻塞性睡眠呼吸暂停(OSA)的存在。基于这些因素的预测模型具有 0.65 的接收器操作特征曲线(ROC)下面积(AUC)和良好的校准。得分>1 的截断值具有 100%的敏感性,而得分 3 的截断值具有 96.5%的特异性。ION 的最高估计绝对风险(844.5/百万)和相对风险(46.40)是针对年龄在 40-64 岁之间、患有 OSA 的男性。
该预测模型可以对 ION 风险较高的患者进行筛选,以提供更准确的围手术期 ION 风险评估以及脊柱融合手术和麻醉计划。