Risk Factors for Prolonged Pleural Effusion Following Total Cavopulmonary Connection Surgery: 9 Years' Experience at Fuwai Hospital - Luo, Qipeng; Zhao, Wei; Su, Zhanhao; Liu, Yiwei; Jia, Yuan; Zhang, Liang; Wang, Hongbai; Li, Yinan; Wu, Xie; Li, Shoujun; Yan, Fuxia (Frontiers in Pediatrics, 2019)
ABSTRACT Background: Prolonged pleural effusion (PPE) contributes to adverse outcomes after total cavopulmonary connection (TCPC) completion. We aimed to identify risk factors for PPE following TCPC surgery. Methods: We studied a retrospective cohort of 525 who undergoing TCPC surgery from 2010 to 2019. We defined PPE as the duration of pleural effusion exceeding 14 days. Logistic regression was applied to identify risk factors for PPE and Cox regression was used to identify risk factors for predicting the duration of pleural effusion. The impacts of PPE on the short-term outcomes were evaluated. Results: The rate of PPE was 27.4% in our study and independent risk factors for PPE included: young age, no fenestration, low postoperative total protein, prolonged mechanical ventilation and chylothorax. These predictors were also achieved in the Cox regression for predicting the duration of pleural effusion. The applicability of the model was acceptable in different subgroups, which derived from the total cohort. Patients with PPE were associated with more renal replacement treatment, longer length of ICU and hospital stay, more hospitalization costs and a higher rate of in-hospital mortality. Conclusions: PPE in our study occurs at a relatively lower rate than that reported in previous studies and patients with PPE was associated with higher rate of in-hospital mortality when compared to patients without PPE. Young age, no fenestration, low postoperative total protein, prolonged mechanical ventilation and chylothorax were identified as independent risk factors to predict PPE. A preventive strategy that targets the identified risk factors to reduce the incidence of PPE following TCPC surgery could be beneficial for in-hospital outcomes, and the model needs further validation before its application.