Creation of a Pediatric Sedation Risk Assessment Scoring System: A Novel Method to Stratify Risk

Abstract

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 β-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 95th, 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.