Factors predictive of poor behavioral compliance during inhaled induction in children

BACKGROUND: Preoperative identification of children at risk of emotional distress and poor behavioral compliance during inhaled induction of anesthesia allows targeted interventions to reduce distress, thereby enhancing the quality of the anesthetic experience. We sought to identify patient, procedural, and health care system factors predictive of poor behavioral compliance during induction. METHODS: We studied 861 developmentally appropriate children ages 1-13 yr, The American Society of Anesthesiologists physical status I to III, presenting for inhaled induction of anesthesia. All inductions were performed in an induction room with parent(s) present. Behavioral compliance was assessed using the Induction Compliance Checklist (ICC), an observational scale consisting of 10 behaviors scored as the number of behaviors observed during induction; ICC 4 was considered poor behavioral compliance. A multivariable ordinal logistic regression model for behavioral compliance was generated and the performance of the multivariable model was evaluated by the c statistic. RESULTS: Twenty-one percent of children exhibited poor behavioral compliance on induction. Factors increasing the odds of poor behavioral compliance were younger age (<4 yr, P < 0.0001), shorter preoperative preparation time (P = 0.004), and high anxiety levels in the preoperative clinic (modified-Yale preoperative anxiety scale >40; P = 0.016). Previous anesthesia experience increased the odds in school-age children (P = 0.046); this effect was ameliorated in children attending the preoperative tour (P = 0.018). The model using these factors demonstrated moderate discrimination between children with poor compliance and those with perfect compliance (ICC = 0) (c statistic = 0.75). CONCLUSIONS: Factors predictive of poor behavioral compliance were age, previous anesthesia, preoperative tour attendance, preoperative preparation time and anxiety levels in the preoperative clinic. These factors, bundled into a predictive algorithm, may help identify children who could benefit from behavioral or pharmacological interventions and avoid use of interventions to those at low risk.