Maai Hino1, Takahiro Mihara, Saeko Miyazaki, Toshiyuki Hijikata, Takaaki Miwa, Takahisa Goto, Koui Ka. 1. From the *Department of Anesthesiology, Kanagawa Children's Medical Center, Yokohama, Japan; †Department of Anesthesiology, Itabashi Chuo Medical Center, Tokyo, Japan; and ‡Department of Anesthesiology and Critical Care Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Abstract
BACKGROUND: 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. CONCLUSIONS: 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.
BACKGROUND: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. CONCLUSIONS: 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.