• S M Goobie, D Zurakowski, K V Isaac, B M Taicher, P G Fernandez, C K Derderian, M Hetmaniuk, P A Stricker, and Pediatric Craniofacial Collaborative Group.
• Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USA. Electronic address: susan.goobie@childrens.harvard.edu.
• Br J Anaesth. 2019 Feb 1; 122 (2): 215-223.

BackgroundThe current incidence of major complications in paediatric craniofacial surgery in North America has not been accurately defined. In this report, the Pediatric Craniofacial Collaborative Group evaluates the incidence and determines the independent predictors of major perioperative complications using a multicentre database.MethodsThe Pediatric Craniofacial Surgery Perioperative Registry was queried for subjects undergoing complex cranial vault reconstruction surgery over a 5-year period. Major perioperative complications were identified through a structured a priori consensus process. Logistic regression was applied to identify predictors of a major perioperative complication with bootstrapping to evaluate discrimination accuracy and provide internal validity of the multivariable model.ResultsA total of 1814 patients from 33 institutions in the US and Canada were analysed; 15% were reported to have a major perioperative complication. Multivariable predictors included ASA physical status 3 or 4 (P=0.005), craniofacial syndrome (P=0.008), antifibrinolytic administered (P=0.003), blood product transfusion >50 ml kg-1 (P<0.001), and surgery duration over 5 h (P<0.001). Bootstrapping indicated that the predictive algorithm had good internal validity and excellent discrimination and model performance. A perioperative complication was estimated to increase the hospital length of stay by an average of 3 days (P<0.001).ConclusionsThe predictive algorithm can be used as a prognostic tool to risk stratify patients and thereby potentially reduce morbidity and mortality. Craniofacial teams can utilise these predictors of complications to identify high-risk patients. Based on this information, further prospective quality improvement initiatives may decrease complications, and reduce morbidity and mortality.Copyright © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

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