Major infection after pediatric cardiac surgery: a risk estimation model

Abstract

Background: In pediatric cardiac surgery, infection is a leading cause of morbidity and mortality. We created a model to predict risk of major infection in this population.

Methods: Using the Society of Thoracic Surgeons Congenital Heart Surgery Database, we created a multivariable model in which the primary outcome was major infection (septicemia, mediastinitis, or endocarditis). Candidate-independent variables included demographic characteristics, comorbid conditions, preoperative factors, and cardiac surgical procedures. We created a reduced model by backward selection and then created an integer scoring system using a scaling factor with scores corresponding to percent risk of infection.

Results: Of 30,078 children from 48 centers, 2.8% had major infection (2.6% septicemia, 0.3% mediastinitis, and 0.09% endocarditis). Mortality and postoperative length of stay were greater in those with major infection (mortality, 22.2% versus 3.0%; length of stay >21 days, 69.9% versus 10.7%). Young age, high complexity, previous cardiothoracic operation, preoperative length of stay more than 1 day, preoperative ventilator support, and presence of a genetic abnormality were associated with major infection after backward selection (p < 0.001). Estimated infection risk ranged from less than 0.1% to 13.3%; the model discrimination was good (c index, 0.79).

Conclusions: We created a simple bedside tool to identify children at high risk for major infection after cardiac surgery. These patients may be targeted for interventions to reduce the risk of infection and for inclusion in future clinical trials.

Figure 1

Panel A: Bedside tool model of predicted risk in relation to risk score. Solid line represents model estimate. Dotted line represents 95% confidence interval. X axis denotes risk score and Y axis denotes estimated infection risk. Panel B: Distribution of study population by risk score category. X axis denotes risk score and Y axis represents total number of patients.

Figure 2

Observed vs. predicted risk of infection during internal calibration of bedside model. Squares correspond to observed risk for each unique value of the risk score. Solid lines correspond to 95% confidence intervals. Goodness of fit chi-square = 17.8; p = 0.66. X axis denotes predicted infection risk and Y axis shows observed infection risk.