小儿全麻苏醒期躁动风险评分的制定与验证：前瞻性观察研究
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Development and Validation of a Risk Scale for Emergence Agitation After General Anesthesia in Children: A Prospective Observational Study
背景与目的
苏醒期躁动（EA）是小儿全身麻醉后常见并发症。本研究包括两个部分：（1）通过回顾分析我们以前研究的数据，制定七氟醚麻醉小儿发生EA的预测模型（EA风险量表）（第1部分）和（2）在前瞻性观察性队列研究中确定EA风险量表的有效性（第2部分）。
方 法
第1部分研究中，收集我们以前研究的120名患者的数据，采用逻辑回归分析预测EA的发生率。使用Akaike信息准则逐步选择方法来确定预测因子的最佳组合。计算所选预测因子的β系数，并确定预测因子的分数。通过受试者工作特征（ROC）曲线评估EA风险量表的预测能力，以95％置信区间（CI）计算ROC曲线下面积（c-指数）。在第2部分研究中，使用另一组100例患者的数据（全身麻醉下进行小手术）确定EA风险量表的有效性。计算ROC曲线，c指数，最佳临界值，以及该点的灵敏度和特异度。此外，我们计算了灰度区域，其两点之间灵敏度和特异度分别达到90％。
结 果
在第1部分，多变量逻辑回归分析的最终模型包括以下4个预测因子：年龄（对数优势比[OR]，-0.38; 95％CI，-0.81-0.00），小儿麻醉行为评分（对数OR，0.65; 95％CI，-0.09-1.40），麻醉时间（对数OR，0.60; 95％CI，-0.18-1.19）和手术方式（斜视手术对数OR，2.53; 95％CI，1.30-3.75，扁桃体切除术对数OR，2.71; 95％CI，0.99-4.45）。 EA风险量表包括4个预测因子，1至23个点。在第二部分，EA的发生率为39％。第一部分c指数为0.84（95％CI，0.74-0.94），第二部分的c指数为0.81（95％CI，0.72-0.89）。 EA风险量表的最佳临界值为11（敏感度=87％，特异度=61％）。灰色区域为10至13点，包括了38％的患者。
结 论
我们制定并验证了七氟醚麻醉小儿的EA风险量表。在我们验证的人群中，该量表具有极好的预测性能（c-指数> 0.8）。 本EA风险量表可用于预测小儿的EA，并对那些高风险者采取预防措施。这些以计分为基础的预防措施应进行前瞻性研究，以评估此类措施的安全性和有效性。
原始文献摘要
Hino M, Mihara T, Miyazaki S, et al.Development and Validation of a Risk Scale for Emergence Agitation After General Anesthesia in Children: A Prospective Observational Study[J]. Anesth Analg, 2017 ,125(2):550-555. doi: 10.1213/ANE.0000000000002126.
PURPOSE:
Emergence agitation (EA) is a common complication in children after general anesthesia. The goal of this 2-phase study was (1) to develop a predictive model (EA risk scale) for the incidence of EA in children receiving sevoflurane anesthesia by performing a retrospective analysis of data from our previous study (phase 1) and (2) to determine the validity of the EA risk scale in a prospective observational cohort study (phase 2).
METHODS:
Using data collected from 120 patients in our previous study, logistic regression analysis was used to predict the incidence of EA in phase 1. The optimal combination of the predictors was determined by a stepwise selection procedure using Akaike information criterion. The β-coefficient for the selected predictors was calculated, and scores for predictors determined. The predictive ability of the EA risk scale was assessed by a receiver operating characteristic (ROC) curve, and the area under the ROC curve (c-index) was calculated with a 95% confidence interval (CI). In phase 2, the validity of the EA risk scale was confirmed using another data set of 100 patients (who underwent minor surgery under general anesthesia). The ROC curve, the c-index, the best cutoff point, and the sensitivity and specificity at the point were calculated. In addition, we calculated the gray zone, which ranges between the two points where sensitivity and specificity, respectively, become 90%.
RESULTS:
In phase 1, the final model of the multivariable logistic regression analysis included the following 4 predictors: age (logarithm odds ratios [OR], -0.38; 95% CI, -0.81 to 0.00), Pediatric Anesthesia Behavior score (logarithm OR, 0.65; 95% CI, -0.09 to 1.40), anesthesia time (logarithm OR, 0.60; 95% CI, -0.18 to 1.19), and operative procedure (logarithm OR, 2.53; 95% CI, 1.30-3.75 for strabismus surgery and logarithm OR, 2.71; 95% CI, 0.99-4.45 for tonsillectomy). The EA risk scale included these 4 predictors and ranged from 1 to 23 points. In phase 2, the incidence of EA was 39%. The c-index of phase 1 was 0.84 (95% CI, 0.74-0.94), and the c-index of phase 2 was 0.81 (95% CI, 0.72-0.89). The best cutoff point for the EA risk scale was 11 (sensitivity = 87% and specificity = 61%). The gray zone ranged from 10 to 13 points, and included 38% of patients.
CONCLUSION:
We developed and validated an EA risk scale for children receiving sevoflurane anesthesia. In our validation cohort, this scale has excellent predictive performance (c-index > 0.8). The EA risk scale could be used to predict EA in children and adopt a preventive strategy for those at high risk. This score-based preventive approach should be studied prospectively to assess the safety and efficacy of such a strategy.
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