A novel risk classification system for 30-day mortality in children undergoing surgery.

A simple, objective and accurate way of grouping children undergoing surgery into clinically relevant risk groups is needed. The purpose of this study, is to develop and validate a preoperative risk classification system for postsurgical 30-day mortality for children undergoing a wide variety of operations. The National Surgical Quality Improvement Project-Pediatric participant use file data for calendar years 2012-2014 was analyzed to determine preoperative variables most associated with death within 30 days of operation (D30). Risk groups were created using classification tree analysis based on these preoperative variables. The resulting risk groups were validated using 2015 data, and applied to neonates and higher risk CPT codes to determine validity in high-risk subpopulations. A five-level risk classification was found to be most accurate. The preoperative need for ventilation, oxygen support, inotropic support, sepsis, the need for emergent surgery and a do not resuscitate order defined non-overlapping groups with observed rates of D30 that vary from 0.075% (Very Low Risk) to 38.6% (Very High Risk). When CPT codes where death was never observed are eliminated or when the system is applied to neonates, the groupings remained predictive of death in an ordinal manner.

Identifying children at risk of death within 30 days of surgery at an NSQIP pediatric hospital.

BACKGROUND: Informed consent for operative procedures performed on children relies on the ability of the surgeon to estimate and describe accurately the risks and benefits of the planned operation to the parents. Understanding patient-specific risks is also an important prerequisite for surgeons and hospital administrators who wish to change hospital processes and improve patient safety. This study tests the feasibility of estimating the risk of death within 30 days of surgery using National Surgical Quality Improvement Program (NSQIP)-Pediatric data from a single children's hospital. STUDY DESIGN: Patient data submitted to NSQIP-Pediatric from our hospital were analyzed to identify variables predictive of death within 30 days of operation. A multiple logistic regression model was constructed using 3 years of data and validated using data submitted the following year. The model was then tested using the participant use file provided by NSQIP-Pediatric for 2012. RESULTS: The model identified 7 variables predictive of death: neonatal status, respiratory support, inotropic support, having a blood disorder, cerebrovascular injury, previous cardiac intervention, and the work relative value unit for the procedure. The resulting final model had a c statistic = 0.97. CONCLUSION: It is possible for a participating children's hospital to use NSQIP-Pediatric data to develop risk models for patient mortality occurring within 30 days of operation at their institution. The model presented may be generalizable to other institutions, but needs further testing and refining.