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Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China
Chunnian Ren1,2,3, Chun Wu1,2,3, Zhengxia Pan1,2,3
1Department of Cardiothoracic Surgery, Children's Hospital of Chongqing Medical University, No.136, Zhongshan 2nd Road, Yuzhong Dis, Chongqing, 400014, P.R. China.
Insights
A new risk prediction model identifies children at high risk for pulmonary infection after congenital heart disease (CHD) surgery. This tool aids early screening and targeted interventions to reduce complications and improve outcomes.
Area of Science:
- Pediatric Cardiology
- Thoracic Surgery
- Infectious Disease Epidemiology
Background:
- Pulmonary infection post-congenital heart disease (CHD) surgery significantly increases CICU stay, costs, and mortality.
- Early identification of high-risk patients is crucial for timely intervention.
- A validated risk prediction model for post-CHD surgery pulmonary infection is lacking.
Purpose of the Study:
- To develop and validate the first risk prediction model for pulmonary infection following CHD surgery.
- To identify independent risk factors for pulmonary infection in pediatric cardiac surgery patients.
- To enable early screening and targeted interventions for high-risk individuals.
Main Methods:
- Development cohort: CHD surgery patients (Oct 2012-Dec 2017).
- Validation cohort: CHD surgery patients (Dec 2017-Oct 2018).
- Univariable/multivariable logistic regression to identify risk factors; nomogram model construction; ROC curve (AUC) and Hosmer-Lemeshow tests for evaluation.
Main Results:
- Six independent risk factors identified: age, weight, preoperative hospital stay, RACHS-1 score, cardiopulmonary bypass time, and intraoperative blood transfusion.
- Developed nomogram model demonstrated high accuracy and reliability.
- AUC values of 0.900 (development) and 0.908 (validation); calibration tests showed good consistency between predicted and actual probabilities (P=0.999 and P=0.452).
Conclusions:
- Established an individualized prediction model for pulmonary infection risk after pediatric CHD surgery.
- The model accurately identifies patients at high risk, facilitating targeted interventions.
- Early identification and intervention can mitigate the risks associated with post-surgical pulmonary infections.
Objectives:
The occurrence of pulmonary infection after congenital heart disease (CHD) surgery can lead to significant increases in intensive care in cardiac intensive care unit (CICU) retention time, medical expenses, and risk of death risk. We hypothesized that patients with a high risk of pulmonary infection could be screened out as early after surgery. Hence, we developed and validated the first risk prediction model to verify our hypothesis.
Methods:
Patients who underwent CHD surgery from October 2012 to December 2017 in the Children's Hospital of Chongqing Medical University were included in the development group, while patients who underwent CHD surgery from December 2017 to October 2018 were included in the validation group. The independent risk factors associated with pulmonary infection following CHD surgery were screened using univariable and multivariable logistic regression analyses. The corresponding nomogram prediction model was constructed according to the regression coefficients. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC) (AUC), and model calibration was conducted with the Hosmer-Lemeshow test.
Results:
The univariate and multivariate logistic regression analyses identified the following six independent risk factors of pulmonary infection after cardiac surgery: age, weight, preoperative hospital stay, risk-adjusted classification for congenital heart surgery (RACHS)-1 score, cardiopulmonary bypass time and intraoperative blood transfusion. We established an individualized prediction model of pulmonary infection following cardiopulmonary bypass surgery for CHD in children. The model displayed accuracy and reliability and was evaluated by discrimination and calibration analyses. The AUCs for the development and validation groups were 0.900 and 0.908, respectively, and the P-values of the calibration tests were 0.999 and 0.452 respectively. Therefore, the predicted probability of the model was consistent with the actual probability.
Conclusions:
Identified the independent risk factors of pulmonary infection after cardiopulmonary bypass surgery. An individualized prediction model was developed to evaluate the pulmonary infection of patients after surgery. For high-risk patients, after surgery, targeted interventions can reduce the risk of pulmonary infection.
