Viviane G Nasr1, James A DiNardo, David Faraoni. 1. From the *Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts; and †Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
Abstract
BACKGROUND: Although there have been numerous attempts to predict perioperative mortality in adults, an objective model to predict mortality in children has not been developed. In this study, we aimed to develop a Pediatric Risk Assessment (PRAm) score to predict perioperative mortality in children undergoing noncardiac surgery. METHODS: We included all children recorded in the 2012 and 2013 American College of Surgeons National Surgical Quality Improvement Program Pediatric databases in a derivation cohort and those from the 2014 database in a validation cohort. The primary outcome was the incidence of in-hospital mortality. A total of 115,229 (63%) were included in the derivation cohort and 68,194 (37%) in the validation cohort. We used multivariable logistic regression to determine the predictors for mortality and designed the PRAm score. RESULTS: On the basis of the multivariable regression model, we created a simplified risk assessment tool (PRAm score) ranging from 0 to ≥9, including the presence of any comorbidities, factors of critical illness, age <12 months, the requirement for an urgent procedure, and the diagnosis of a neoplasm. The PRAm score showed an excellent discriminative ability with an apparent "optimistic" area under the receiver operating characteristic curve (AUC) of 0.950 (95% confidence interval [CI], 0.942-0.957) in the derivation cohort. In the validation cohort, we observed similar performances with an area under the "naive" receiver operating characteristic curve of 0.950 (95% CI, 0.938-0.961). The AUC was also calculated from a bootstrap procedure and then applied to the original derivation sample to estimate "optimism" for each bootstrap sample with an AUC of 0.943 (95% CI, 0.929-0.9956). The optimism in apparent performance was 0.007, corresponding to an optimism-corrected area of 0.943. Calibration was assessed graphically by plotting the observed outcome against the predicted mortality (Pearson correlation coefficient = 0.995, calibration in the large = 0.001 [P = .974], calibration slope = 0.927). CONCLUSIONS: In this study, we developed a simplified PRAm tool (PRAm score) as a predictor of perioperative mortality in children undergoing noncardiac surgery. The PRAm score has excellent accuracy. In patients assigned American Society of Anesthesiologists physical status classification ≥4, there is wide variability in objectively obtained PRAm scores.
BACKGROUND: Although there have been numerous attempts to predict perioperative mortality in adults, an objective model to predict mortality in children has not been developed. In this study, we aimed to develop a Pediatric Risk Assessment (PRAm) score to predict perioperative mortality in children undergoing noncardiac surgery. METHODS: We included all children recorded in the 2012 and 2013 American College of Surgeons National Surgical Quality Improvement Program Pediatric databases in a derivation cohort and those from the 2014 database in a validation cohort. The primary outcome was the incidence of in-hospital mortality. A total of 115,229 (63%) were included in the derivation cohort and 68,194 (37%) in the validation cohort. We used multivariable logistic regression to determine the predictors for mortality and designed the PRAm score. RESULTS: On the basis of the multivariable regression model, we created a simplified risk assessment tool (PRAm score) ranging from 0 to ≥9, including the presence of any comorbidities, factors of critical illness, age <12 months, the requirement for an urgent procedure, and the diagnosis of a neoplasm. The PRAm score showed an excellent discriminative ability with an apparent "optimistic" area under the receiver operating characteristic curve (AUC) of 0.950 (95% confidence interval [CI], 0.942-0.957) in the derivation cohort. In the validation cohort, we observed similar performances with an area under the "naive" receiver operating characteristic curve of 0.950 (95% CI, 0.938-0.961). The AUC was also calculated from a bootstrap procedure and then applied to the original derivation sample to estimate "optimism" for each bootstrap sample with an AUC of 0.943 (95% CI, 0.929-0.9956). The optimism in apparent performance was 0.007, corresponding to an optimism-corrected area of 0.943. Calibration was assessed graphically by plotting the observed outcome against the predicted mortality (Pearson correlation coefficient = 0.995, calibration in the large = 0.001 [P = .974], calibration slope = 0.927). CONCLUSIONS: In this study, we developed a simplified PRAm tool (PRAm score) as a predictor of perioperative mortality in children undergoing noncardiac surgery. The PRAm score has excellent accuracy. In patients assigned American Society of Anesthesiologists physical status classification ≥4, there is wide variability in objectively obtained PRAm scores.
Authors: Julie C Leonard; Cassandra D Josephson; James F Luther; Stephen R Wisniewski; Christine Allen; Fabrizio Chiusolo; Adrienne L Davis; Robert A Finkelstein; Julie C Fitzgerald; Barbara A Gaines; Susan M Goobie; Sheila J Hanson; Hilary A Hewes; Laurie H Johnson; Mark O McCollum; Jennifer A Muszynski; Alison B Nair; Robert B Rosenberg; Thomas M Rouse; Athina Sikavitsas; Marcy N Singleton; Marie E Steiner; Jeffrey S Upperman; Adam M Vogel; Hale Wills; Margaret K Winkler; Philip C Spinella Journal: Crit Care Med Date: 2021-11-01 Impact factor: 9.296
Authors: Rebecca Miller; Dmitry Tumin; Christopher McKee; Vidya T Raman; Joseph D Tobias; Jennifer N Cooper Journal: Laryngoscope Investig Otolaryngol Date: 2019-01-17