The risk factors analysis and establishment of an early warning model for healthcare-associated infections after pediatric cardiac surgery

Abstract

The aim of this study was to identify the main risk factors for health-care-associated infections (HAIs) following cardiac surgery and to establish an effective early warning model for HAIs to enable intervention in an earlier stage.In total, 2227 patients, including 222 patients with postoperative diagnosis of HAIs and 2005 patients with no-HAIs, were continuously enrolled in Beijing Anzhen Hospital, Beijing, China. Propensity score matching was used and 222 matched pairs were created. The risk factors were analyzed with the methods of univariate and multivariate logistic regression. The receiver operating characteristic (ROC) curve was used to test the accuracy of the HAIs early warning model.After propensity score matching, operation time, clamping time, intubation time, urinary catheter time, central venous catheter time, ≥3 blood transfusions, re-endotracheal intubation, length of hospital stay, and length of intensive care unit stay, still showed significant differences between the 2 groups. After logistic model analysis, the independent risk factors for HAIs were medium to high complexity, intubation time, urinary catheter time, and central venous catheter time. The ROC showed the area under curve was 0.985 (confidence interval: 0.975–0.996). When the probability was 0.529, the model had the highest prediction rate, the corresponding sensitivity was 0.946, and the specificity was 0.968.According to the results, the early warning model containing medium to high complexity, intubation time, urinary catheter time, and central venous catheter time enables more accurate predictions and can be used to guide early intervention after pediatric cardiac surgery.