PRO: American College of Surgeons National Surgical Quality Improvement Program Risk Calculators Should Be Preferred Over the Revised Cardiac Risk Index for Perioperative Risk Stratification

2018 ◽  
Vol 32 (5) ◽  
pp. 2417-2419 ◽  
Author(s):  
Somnath Bose ◽  
Abraham Sonny
Keyword(s):  
Quality Improvement ◽  
Risk Stratification ◽  
Risk Index ◽  
Cardiac Risk ◽  
Perioperative Risk ◽  
Quality Improvement Program ◽  
Improvement Program ◽  
Surgical Quality ◽  
Cardiac Risk Index ◽  
American College Of Surgeons

Pediatric Risk Stratification Is Improved by Integrating Both Patient Comorbidities and Intrinsic Surgical Risk

2019 ◽  
Vol 130 (6) ◽  
pp. 971-980 ◽  
Author(s):  
Viviane G. Nasr ◽  
Steven J. Staffa ◽  
David Zurakowski ◽  
James A. DiNardo ◽  
David Faraoni
Keyword(s):  
Quality Improvement ◽  
Risk Stratification ◽  
Surgical Procedures ◽  
External Validation ◽  
Quality Improvement Program ◽  
Current Procedural Terminology ◽  
Improvement Program ◽  
Surgical Quality ◽  
Surgical Risk ◽  
American College Of Surgeons

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New 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.

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