Major infection after pediatric cardiac surgery: external validation of risk estimation model

Andrzej Kansy1, Jeffrey P Jacobs, Andrzej Pastuszko

  • 1The Children's Memorial Health Institute, Warsaw, Poland. ankansy@wp.pl

Insights

The Society of Thoracic Surgeons (STS) model accurately predicts major infection risk in congenital heart surgery patients. This validation confirms its utility as a preoperative risk stratification tool at the institutional level.

Area of Science:

  • Cardiology
  • Thoracic Surgery
  • Infectious Disease Epidemiology

Background:

  • A multivariable risk model for major infection in congenital heart surgery was previously developed using the STS Database.
  • This study aimed to validate the STS risk model and identify specific risk factors for major infection within a single institution over 16 years.

Purpose of the Study:

  • To externally validate the Society of Thoracic Surgeons (STS) multivariable risk estimation model for major infection in congenital heart surgery.
  • To assess the significance of identified STS risk factors in a local patient cohort.

Main Methods:

  • A multivariable model was created with major infection (septicemia, mediastinitis, endocarditis) as the primary outcome.
  • Patient data included Aristotle Basic Score and Risk Adjustment for Congenital Heart Surgery (RACHS-1) classifications.
  • The STS risk model's performance and the significance of its risk factors were evaluated.

Main Results:

  • Analysis of 6,314 patients revealed 197 (3.1%) major infections.
  • Preoperative risk factors significant for infection included young age, high/medium complexity, prior surgery, and preoperative ventilation (p<0.0001).
  • The model demonstrated good discrimination (c-index=0.808), with estimated infection risks from 0.32% to 11.58%.

Conclusions:

  • The STS risk model serves as a reliable preoperative risk stratification tool for major infection in congenital heart surgery.
  • External validation at the single institutional level confirms the model's applicability and accuracy.
Abstract