Couloures Kevin G, Anderson Michael P, Hill C L, Chen Allshine, Buckmaster Mark A
Division of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California, United States.
Department of Biostatistics, University of Oklahoma, Oklahoma City, Oklahoma, United States.
J Pediatr Intensive Care. 2022 May 17;13(2):201-208. doi: 10.1055/s-0042-1745831. eCollection 2024 Jun.
This study aimed to create a pediatric sedation scoring system independent of the American Society of Anesthesiology Physical Status (ASA-PS) classification that is predictive of adverse events, facilitates objective stratification, and resource allocation. Multivariable regression and machine learning algorithm analysis of 134,973 sedation encounters logged in to the Pediatric Sedation Research Consortium (PSRC) database between July 2007 and June 2011. Patient and procedure variables were correlated with adverse events with resultant -regression coefficients used to assign point values to each variable. Point values were then summed to create a risk assessment score. Validation of the model was performed with the 2011 to 2013 PSRC database followed by calculation of ROC curves and positive predictive values. Factors identified and resultant point values are as follows: 1 point: age ≤ 6 months, cardiac diagnosis, asthma, weight less than 5th percentile or greater than 95 , and computed tomography (CT) scan; 2 points: magnetic resonance cholangiopancreatography (MRCP) and weight greater than 99th percentile; 4 points: magnetic resonance imaging (MRI); 5 points: trisomy 21 and esophagogastroduodenoscopy (EGD); 7 points: cough at the time of examination; and 18 points: bronchoscopy. Sum of patient and procedural values produced total risk assessment scores. Total risk assessment score of 5 had a sensitivity of 82.69% and a specificity of 26.22%, while risk assessment score of 11 had a sensitivity of 12.70% but a specificity of 95.29%. Inclusion of ASA-PS value did not improve model sensitivity or specificity and was thus excluded. Higher risk assessment scores predicted increased likelihood of adverse events during sedation. The score can be used to triage patients independent of ASA-PS with site-specific cut-off values used to determine appropriate sedation resource allocation.
本研究旨在创建一个独立于美国麻醉医师协会身体状况（ASA-PS）分类的儿科镇静评分系统，该系统可预测不良事件、便于进行客观分层以及资源分配。对2007年7月至2011年6月期间登录儿科镇静研究联盟（PSRC）数据库的134973次镇静记录进行多变量回归和机器学习算法分析。将患者和手术变量与不良事件进行关联，所得回归系数用于为每个变量赋予分值。然后将分值相加得出风险评估分数。使用2011年至2013年的PSRC数据库对模型进行验证，随后计算ROC曲线和阳性预测值。确定的因素及所得分值如下：1分：年龄≤6个月、心脏诊断、哮喘、体重低于第5百分位数或高于第95百分位数以及计算机断层扫描（CT）；2分：磁共振胰胆管造影（MRCP）以及体重高于第99百分位数；4分：磁共振成像（MRI）；5分：21三体综合征和食管胃十二指肠镜检查（EGD）；7分：检查时咳嗽；18分：支气管镜检查。患者和手术分值之和得出总风险评估分数。总风险评估分数为5时，敏感性为82.69%，特异性为26.22%，而风险评估分数为11时，敏感性为12.70%，但特异性为95.29%。纳入ASA-PS值并未提高模型的敏感性或特异性，因此将其排除。较高的风险评估分数预示着镇静期间不良事件发生的可能性增加。该分数可用于对患者进行分诊，独立于ASA-PS，使用特定部位的临界值来确定适当的镇静资源分配。
