Major infection after pediatric cardiac surgery: a risk estimation model

Area of Science:

• Pediatric cardiac surgery
• Infectious disease epidemiology
• Clinical risk prediction modeling

Background:

• Infection poses a significant threat to pediatric cardiac surgery patients, impacting morbidity and mortality.
• Developing accurate risk prediction models is crucial for managing post-operative complications.

Purpose of the Study:

• To develop and validate a predictive model for major infection risk in children undergoing cardiac surgery.
• To create a simple, accessible tool for bedside use.

Main Methods:

• Utilized the Society of Thoracic Surgeons Congenital Heart Surgery Database (30,078 children).
• Developed a multivariable logistic regression model, refined by backward selection.
• Created an integer scoring system for estimating infection risk (0.1% to 13.3%).

Main Results:

• Major infection occurred in 2.8% of patients, with higher mortality (22.2%) and prolonged hospital stays.
• Key predictors included young age, complex procedures, prior surgery, extended preoperative stay, ventilator support, and genetic abnormalities.
• The final model demonstrated good discrimination (c-index = 0.79).

Conclusions:

• A straightforward bedside tool was developed to identify pediatric cardiac surgery patients at high risk of major infection.
• This tool can guide targeted interventions and patient selection for clinical trials to mitigate infection risks.