RESUMO
Background: The mortality rate of coronary artery disease ranks first in developed countries, and coronary revascularization therapy is an important cornerstone of its treatment. The postoperative pulmonary complications (PPCs) in patients receiving one-stop hybrid coronary revascularization (HCR) aggravate the dysfunction of multiple organs such as the heart and lungs, therefore increasing mortality. However, the risk factors are still unclear. The objective of this study was to explore the risk factors of PPCs after HCR surgery. Methods: In this study, the perioperative data of 311 patients undergoing HCR surgery were reviewed. All patients were divided into two groups according to whether the PPCs occurred. The baseline information and surgery-related indicators in preoperative laboratory examination, intraoperative fluid management, and anesthesia management were compared between the two groups. Results: Advanced age [odds ratio (OR): 1.065, 95% confidence interval (CI): 1.030-1.101, P<0.001], high body mass index (BMI; OR: 1.113, 95% CI: 1.011-1.225, P=0.02), history of percutaneous coronary intervention (PCI) surgery (OR: 2.831, 95% CI: 1.388-5.775, P=0.004), one-lung volume ventilation (OR: 3.804, 95% CI: 1.923-7.526, P<0.001), inhalation of high concentration oxygen (OR: 3.666, 95% CI: 1.719-7.815, P=0.001), the application of positive end-expiratory pressure (PEEP; OR: 2.567, 95% CI: 1.338-4.926, P=0.005), and long one-lung ventilation time (OR: 1.015, 95% CI: 1.006-1.023, P=0.001) may be risk factors for postoperative PPCs in patients undergoing one-stop coronary revascularization surgery. Using the above seven factors to jointly predict the risk of PPCs in patients undergoing one-stop coronary revascularization surgery, the receiver operating characteristic (ROC) curve showed an area under the curve (AUC) =0.873, 95% CI: 0.835-0.911, sensitivity: 84.81%, and specificity: 75.82%; the predictive model was shown to be effective. Conclusions: Patients undergoing HCR surgery with advanced age, high BMI, a history of PCI surgery, one-lung volume ventilation, inhalation of high concentration oxygen, use of PEEP, and prolonged single lung ventilation are more prone to PPCs.
RESUMO
Background: The cardiac surgery-associated acute kidney injury (CSA-AKI) occurs in up to 1 out of 3 patients. Off-pump coronary artery bypass grafting (OPCABG) is one of the major cardiac surgeries leading to CSA-AKI. Early identification and timely intervention are of clinical significance for CSA-AKI. In this study, we aimed to establish a prediction model of off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) after surgery based on machine learning methods. Methods: The preoperative and intraoperative data of 1,041 patients who underwent OPCABG in Chest Hospital, Tianjin University from June 1, 2021 to April 30, 2023 were retrospectively collected. The definition of OPCABG-AKI was based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The baseline data and intraoperative time series data were included in the dataset, which were preprocessed separately. A total of eight machine learning models were constructed based on the baseline data: logistic regression (LR), gradient-boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). The intraoperative time series data were extracted using a long short-term memory (LSTM) deep learning model. The baseline data and intraoperative features were then integrated through transfer learning and fused into each of the eight machine learning models for training. Based on the calculation of accuracy and area under the curve (AUC) of the prediction model, the best model was selected to establish the final OPCABG-AKI risk prediction model. The importance of features was calculated and ranked by DT model, to identify the main risk factors. Results: Among 701 patients included in the study, 73 patients (10.4%) developed OPCABG-AKI. The GBDT model was shown to have the best predictions, both based on baseline data only (AUC =0.739, accuracy: 0.943) as well as based on baseline and intraoperative datasets (AUC =0.861, accuracy: 0.936). The ranking of importance of features of the GBDT model showed that use of insulin aspart was the most important predictor of OPCABG-AKI, followed by use of acarbose, spironolactone, alfentanil, dezocine, levosimendan, clindamycin, history of myocardial infarction, and gender. Conclusions: A GBDT-based model showed excellent performance for the prediction of OPCABG-AKI. The fusion of preoperative and intraoperative data can improve the accuracy of predicting OPCABG-AKI.