=9.494，P=0.302）。内部、外部验证显示，校准曲线实际值与预测值间的平均绝对误差分别为0.030和0.018，受试者工作特征曲线下面积分别为0.777和0.775。决策曲线分析显示，当预测风险阈值>0.01时模型提供显著临床净收益。 结论 年龄、合并感染、入住儿童重症监护病房、应用苯二氮?类药物、惊厥持续状态史、谵妄发作史与惊厥患儿镇静后谵妄发生密切相关；根据这些因素建立的惊厥患儿镇静后谵妄风险列线图预测模型具有较高的准确度、区分度和临床应用价值。"/>
Risk factors for delirium after sedation in children with convulsion and establishment of a nomogram model for predicting the risk of delirium
Abstract Objective To investigate the risk factors for delirium after sedation in children with convulsion, and to establish a nomogram model for predicting the risk of delirium. Methods A total of 373 children with convulsion who were hospitalized in the pediatric ward of the Second Affiliated Hospital of Air Force Medical University from August 2020 to January 2022 were prospectively enrolled. There were 245 children in the modeling group and 128 children in the validation group. A multivariate logistic regression analysis was used to identify independent predictive factors for delirium after sedation and establish a nomogram model for predicting the risk of this disorder based on these factors. The calibration curve, the receiver operating characteristic curve, and the decision curve analysis were used to evaluate the accuracy, discriminatory ability, and clinical application value of this model, respectively. Results The incidence of delirium after sedation was 22.3% (83/373) in the children with convulsion. The multivariate logistic regression analysis showed that age>5 years (OR=0.401, P<0.05) was a protective factor against delirium after sedation in these children, while presence of infection (OR=3.020, P<0.05), admission to the pediatric intensive care unit (OR=3.126, P<0.05), use of benzodiazepines (OR=5.219, P<0.05), history of status convulsion (OR=2.623, P<0.05), and history of delirium episodes (OR=3.119, P<0.05) were risk factors for delirium. The H-L deviation test of the nomogram prediction model showed a good degree of fit (χ2=9.494, P=0.302). Internal and external validation showed that the mean absolute errors between the actual and predicted values of the calibration curve were 0.030 and 0.018, respectively, and the areas under the receiver operating characteristic curve were 0.777 and 0.775, respectively. The decision curve analysis showed that the model provided significant net clinical benefit when the predicted risk threshold was >0.01. Conclusions Age, presence of infection, admission to the pediatric intensive care unit, use of benzodiazepines, history of status convulsion, and history of delirium episodes are closely associated with the development of delirium after sedation in children with convulsion. The nomogram model for predicting this disorder that is established based on these factors has relatively high accuracy, discriminatory ability, and clinical application value.
YU Xia,WANG Lei,GAO Ya et al. Risk factors for delirium after sedation in children with convulsion and establishment of a nomogram model for predicting the risk of delirium[J]. CJCP, 2022, 24(11): 1238-1245.
YU Xia,WANG Lei,GAO Ya et al. Risk factors for delirium after sedation in children with convulsion and establishment of a nomogram model for predicting the risk of delirium[J]. CJCP, 2022, 24(11): 1238-1245.
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