Viviane G Nasr1, Steven J Staffa, David Zurakowski, James A DiNardo, David Faraoni. 1. From the Division of Cardiac Anesthesia (V.G.N., J.A.D.) Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts (S.J.S., D.Z.) the Division of Cardiac Anesthesia, Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, University of Toronto, Toronto, Ontario Canada (D.F).
Abstract
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: Risk stratification models to predict perioperative mortality in pediatric surgical populations are based on patient comorbidities, but do not take into consideration the intrinsic risk of the surgical procedures. WHAT THIS ARTICLE TELLS US THAT IS NEW: Surgical procedures identified by specialty are not independent risk factors for perioperative mortality in pediatric patients. However, in multivariable predictive algorithms, the interaction of patient comorbidities with the intrinsic risk of the surgical procedure strongly predicts 30-day mortality. BACKGROUND: Recently developed risk stratification models for perioperative mortality incorporate patient comorbidities as predictors but fail to consider the intrinsic risk of surgical procedures. In this study, the authors used the American College of Surgeons National Surgical Quality Improvement Program Pediatric database to demonstrate the relationship between the intrinsic surgical risk and 30-day mortality and develop and validate an accessible risk stratification model that includes the surgical procedures in addition to the patient comorbidities and physical status. METHODS: A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program Pediatric database was performed. The incidence of 30-day mortality was the primary outcome. Surgical Current Procedural Terminology codes with at least 25 occurrences were included. Multivariable logistic regression model was used to determine the predictors for mortality including patient comorbidities and intrinsic surgical risk. An internal validation using bootstrap resampling, and an external validation of the model were performed. RESULTS: The authors analyzed 367,065 surgical cases encompassing 659 unique Current Procedural Terminology codes with an incidence of overall 30-day mortality of 0.34%. Intrinsic risk of surgical procedures represented by Current Procedural Terminology risk quartiles instead of broad categorization was significantly associated with 30-day mortality (P < 0.001). Predicted risk of 30-day mortality ranges from 0% with no comorbidities to 4.7% when all comorbidities are present among low-risk surgical procedures and from 0.07 to 46.7% among high-risk surgical procedures. Using an external validation cohort of 110,474 observations, the multivariable predictive risk model displayed good calibration and excellent discrimination with area under curve (c-index) equals 0.95 (95% CI, 0.94 to 0.96; P < 0.001). CONCLUSIONS: Understanding and accurately estimating perioperative risk by accounting for the intrinsic risk of surgical procedures and patient comorbidities will lead to a more comprehensive discussion between patients, families, and providers and could potentially be used to conduct cost analysis and allocate resources.
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: Risk stratification models to predict perioperative mortality in pediatric surgical populations are based on patient comorbidities, but do not take into consideration the intrinsic risk of the surgical procedures. WHAT THIS ARTICLE TELLS US THAT IS NEW: Surgical procedures identified by specialty are not independent risk factors for perioperative mortality in pediatric patients. However, in multivariable predictive algorithms, the interaction of patient comorbidities with the intrinsic risk of the surgical procedure strongly predicts 30-day mortality. BACKGROUND: Recently developed risk stratification models for perioperative mortality incorporate patient comorbidities as predictors but fail to consider the intrinsic risk of surgical procedures. In this study, the authors used the American College of Surgeons National Surgical Quality Improvement Program Pediatric database to demonstrate the relationship between the intrinsic surgical risk and 30-day mortality and develop and validate an accessible risk stratification model that includes the surgical procedures in addition to the patient comorbidities and physical status. METHODS: A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program Pediatric database was performed. The incidence of 30-day mortality was the primary outcome. Surgical Current Procedural Terminology codes with at least 25 occurrences were included. Multivariable logistic regression model was used to determine the predictors for mortality including patient comorbidities and intrinsic surgical risk. An internal validation using bootstrap resampling, and an external validation of the model were performed. RESULTS: The authors analyzed 367,065 surgical cases encompassing 659 unique Current Procedural Terminology codes with an incidence of overall 30-day mortality of 0.34%. Intrinsic risk of surgical procedures represented by Current Procedural Terminology risk quartiles instead of broad categorization was significantly associated with 30-day mortality (P < 0.001). Predicted risk of 30-day mortality ranges from 0% with no comorbidities to 4.7% when all comorbidities are present among low-risk surgical procedures and from 0.07 to 46.7% among high-risk surgical procedures. Using an external validation cohort of 110,474 observations, the multivariable predictive risk model displayed good calibration and excellent discrimination with area under curve (c-index) equals 0.95 (95% CI, 0.94 to 0.96; P < 0.001). CONCLUSIONS: Understanding and accurately estimating perioperative risk by accounting for the intrinsic risk of surgical procedures and patient comorbidities will lead to a more comprehensive discussion between patients, families, and providers and could potentially be used to conduct cost analysis and allocate resources.
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: Viviane G Nasr; Urbano L França; Meena Nathan; James A DiNardo; David Faraoni; Michael L McManus Journal: J Am Heart Assoc Date: 2022-07-20 Impact factor: 6.106
Authors: Ahmed Mounir A Metwally; Ahmed Haroun M Mahmoud; Abdulrahman A Alwakeel; Abdulrahman M Aljamous; Rayan S Aldusari; Faisal Hijji; Fouzia A Al Tuwaijri; Saad Eldin M Hassan; Mohamed Ebid; Abdullah A Alghamdi Journal: J Saudi Heart Assoc Date: 2022-07-22