TY - JOUR
T1 - Predictors of perioperative complications in paediatric cranial vault reconstruction surgery
T2 - a multicentre observational study from the Pediatric Craniofacial Collaborative Group
AU - the Pediatric Craniofacial Collaborative Group
AU - Goobie, S. M.
AU - Zurakowski, D.
AU - Isaac, K. V.
AU - Taicher, B. M.
AU - Fernandez, P. G.
AU - Derderian, C. K.
AU - Hetmaniuk, M.
AU - Stricker, P. A.
AU - Abruzzese, Christopher
AU - Apuya, Jesus
AU - Beethe, Amy
AU - Benzon, Hubert
AU - Binstock, Wendy
AU - Brzenskim, Alyssa
AU - Budac, Stefan
AU - Busso, Veronica
AU - Chhabada, Surendrasingh
AU - Chiao, Franklin
AU - Cladis, Franklyn
AU - Claypool, Danielle
AU - Collins, Michael
AU - Dabek, Rachel
AU - Dalesio, Nicholas
AU - Falconl, Ricardo
AU - Fernandez, Allison
AU - Fernandez, Patrick
AU - Fiadjoe, John
AU - Gangadharan, Meera
AU - Gentry, Katherine
AU - Glover, Chris
AU - Gosman, Amanda
AU - Grap, Shannon
AU - Gries, Heike
AU - Griffin, Allison
AU - Haberkern, Charles
AU - Hajduk, John
AU - Hall, Rebecca
AU - Hansen, Jennifer
AU - Hetmaniuk, Mali
AU - Hsieh, Vincent
AU - Huang, Henry
AU - Ingelmo, Pablo
AU - Ivanova, Iskra
AU - Jain, Ranu
AU - Kars, Michelle
AU - Kowalczyk-Derderian, Courtney
AU - Kugler, Jane
AU - Labovsky, Kristen
AU - Lakheeram, Indrani
AU - Masel, Brian
N1 - Publisher Copyright:
© 2018 British Journal of Anaesthesia
PY - 2019/2
Y1 - 2019/2
N2 - Background: The 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. Methods: The 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. Results: A 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). Conclusions: The 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.
AB - Background: The 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. Methods: The 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. Results: A 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). Conclusions: The 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.
KW - craniosynostosis
KW - multivariable model
KW - paediatrics
KW - perioperative complications
KW - perioperative outcome
KW - predictive algorithm
KW - risk assessment
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U2 - 10.1016/j.bja.2018.10.061
DO - 10.1016/j.bja.2018.10.061
M3 - Article
C2 - 30686307
AN - SCOPUS:85058513227
SN - 0007-0912
VL - 122
SP - 215
EP - 223
JO - British Journal of Anaesthesia
JF - British Journal of Anaesthesia
IS - 2
ER -