Dataset Information

Off-pump coronary artery bypass surgery and acute kidney injury: a meta-analysis of randomized controlled trials.

ABSTRACT:

Background and objectives

Off-pump coronary artery bypass grafting (CABG) has been advocated to cause less inflammation, morbidity, and mortality than the more traditional on-pump technique. This meta-analysis compares these two surgical techniques with respect to causing acute kidney injury (AKI).

Design, setting, participants, & measurements

This study searched for randomized controlled trials in MEDLINE and abstracts from the proceedings of scientific meetings through February 2010. Included were trials comparing off-pump to on-pump CABG that reported the incidence of AKI, as defined by a mixture of criteria including biochemical parameter/urine output/dialysis requirement. Mortality was evaluated among the studies that reported kidney-related outcomes. For primary and subgroup analyses, fixed-effect meta-analyses of odds ratios (OR) were performed.

Results

In 22 identified trials (4819 patients), the weighted incidence of AKI in the on-pump CABG group was 4.0% (95% confidence interval [CI] 1.8%, 8.5%), dialysis requirement 2.4% (95% CI 1.6%, 3.7%), and mortality 2.6% (95% CI 1.6%, 4.0%). By meta-analysis, off-pump CABG was associated with a 40% lower odds of postoperative AKI (OR 0.60; 95% CI 0.43, 0.84; P = 0.003) and a nonsignificant 33% lower odds for dialysis requirement (OR 0.67; 95% CI 0.40, 1.12; P = 0.12). Within the selected trials, off-pump CABG was not associated with a significant decrease in mortality.

Conclusions

Off-pump CABG may be associated with a lower incidence of postoperative AKI but may not affect dialysis requirement, a serious complication of cardiac surgery. However, the different definitions of AKI used in individual trials and methodological concerns preclude definitive conclusions.

SUBMITTER: Seabra VF

PROVIDER: S-EPMC2974371 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Off-pump coronary artery bypass surgery and acute kidney injury: a meta-analysis of randomized controlled trials.

Seabra Victor F VF Alobaidi Sami S Balk Ethan M EM Poon Alan H AH Jaber Bertrand L BL

Clinical journal of the American Society of Nephrology : CJASN 20100729 10

<h4>Background and objectives</h4>Off-pump coronary artery bypass grafting (CABG) has been advocated to cause less inflammation, morbidity, and mortality than the more traditional on-pump technique. This meta-analysis compares these two surgical techniques with respect to causing acute kidney injury (AKI).<h4>Design, setting, participants, & measurements</h4>This study searched for randomized controlled trials in MEDLINE and abstracts from the proceedings of scientific meetings through February ...[more]

PMID: 20671222

Similar Datasets

Project description:BackgroundThe 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.MethodsThe 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.ResultsAmong 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.ConclusionsA 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.

Dataset Information

Off-pump coronary artery bypass surgery and acute kidney injury: a meta-analysis of randomized controlled trials.

Background and objectives

Design, setting, participants, & measurements

Results

Conclusions

Publications

Off-pump coronary artery bypass surgery and acute kidney injury: a meta-analysis of randomized controlled trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets