Bedside prediction rule for infections after pediatric cardiac surgery.

Area of Science:

• Pediatric Surgery
• Infectious Diseases
• Clinical Prediction Models

Background:

• Postoperative infections are a significant complication in pediatric cardiac surgery, affecting up to 30% of patients.
• Accurate risk stratification is crucial for implementing targeted preventive strategies.

Purpose of the Study:

• To develop a simple, bedside prediction rule for estimating the risk of postoperative infection in pediatric cardiac surgery patients.
• To identify key clinical variables available at 48 hours post-surgery that predict infection risk.

Main Methods:

• Retrospective analysis of 412 pediatric cardiac surgery procedures (April 2006 - May 2009).
• Infection defined by CDC criteria (2008).
• Multivariable logistic regression used to construct a prediction rule based on variables available at 48 hours post-surgery.

Main Results:

• 102 (25%) patients developed a postoperative infection.
• Predictive variables for infection: age < 6 months, pediatric intensive care unit (PICU) stay > 48 hours, and open sternum > 48 hours.
• A scoring system was developed, with infection risk ranging from 6.6% (score 0) to 57% (maximal score 6). Area under the ROC curve was 0.78.

Conclusions:

• A bedside prediction rule, applied 48 hours post-cardiac surgery, effectively identifies children at high or low risk of subsequent infection.
• This tool can aid clinicians in managing infection risk in this vulnerable population.