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Published on: May 26, 2023
Development of a model for predicting major infection following pediatric heart surgery
Alfredo M Jauregui1, Paula V Urrunaga1, Juan A Gonzales1
1Facultad de Medicina, Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Perú.
Insights
This study developed a new risk model to predict major postoperative infection (MPI) in pediatric heart surgery patients, showing good performance. The Society of Thoracic Surgeons (STS) model was also validated for predicting infection risk.
Area of Science:
- Pediatric Cardiac Surgery
- Infectious Disease Epidemiology
- Risk Prediction Modeling
Background:
- Major postoperative infection (MPI) is a significant concern following pediatric heart surgery.
- Accurate risk stratification is crucial for optimizing patient management and outcomes.
- Existing models, such as the Society of Thoracic Surgeons (STS) score, require validation and potential improvement.
Purpose of the Study:
- To develop and validate a novel risk prediction model for major postoperative infection (MPI) in children undergoing heart surgery.
- To evaluate the performance of the Society of Thoracic Surgeons (STS) risk model in this specific pediatric population.
- To identify key predictive factors for MPI in pediatric cardiac surgery patients.
Main Methods:
- Retrospective analysis of 1,025 pediatric patients who underwent heart surgery with cardiopulmonary bypass (CPB) between 2000 and 2010.
- Development of a logistic regression model to predict MPI.
- Validation of the developed model and the STS model using statistical measures including c-statistic and Hosmer-Lemeshow test.
Main Results:
- A total of 5.8% of patients experienced MPI, with higher rates of hospital mortality, prolonged ventilation, and intensive care unit (ICU) stay.
- Key predictors for MPI included age, sex, weight, cyanotic heart disease, RACHS-1 score, functional class, prior hospitalization, and mechanical ventilation.
- The developed model demonstrated strong predictive performance (c-statistic 0.80), while the STS model showed moderate discrimination (c-statistic 0.78).
Conclusions:
- A clinically useful, well-calibrated risk prediction model was successfully developed for identifying children at high risk of MPI after CPB.
- The STS model was validated and found to have moderate predictive capability in this cohort.
- Preoperative risk assessment using validated models can aid in managing infection risk in pediatric cardiac surgery.
Objective:
The aim of this study was to develop a risk prediction model for major postoperative infection (MPI) after pediatric heart surgery and to validate the model of the Society of Thoracic Surgeons (STS).
Materials And Methods:
We analyzed a retrospective cohort of 1,025 children who underwent heart surgery with cardiopulmonary bypass (CPB) from 2000 to 2010. We used a logistic regression model, which was validated.
Results:
Of the 1,025 patients, 59 (5.8%) had at least one episode of MPI (4.8% had sepsis, 1% had mediastinitis, 0% had endocarditis). Hospital mortality (63% vs. 13%; p < 0.001), as well as duration of postoperative ventilation (301.6 vs. 34.3 hours; p < 0.001) and intensive care unit stay (20.9 vs. 5.1 days; p < 0.001) were higher in patients with MPI. The predictive factors found were age, sex, weight, cyanotic heart disease, RACHS-1 3-4, Ross-modified functional class IV, previous hospital stay, and previous history of mechanical ventilation. The proposed model had a c-statistic of 0.80 (95% CI: 0.74-0.86) and was considered as clinically useful. The STS model showed a c-statistic of 0.78 (95% CI: 0.71-0.84) and a Hosmer-Lemeshow of 18.2 (P = 0.020). A comparison between the two models was made using an accurate Fisher test.
Conclusion:
A model with good performance and calibration was developed to preoperatively identify children at high risk for severe infection after cardiac surgery with CPB. The STS model was also validated and was found to have a moderate discrimination performance.
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