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Meta-analysis for outcomes of acute kidney injury after cardiac surgery.

ABSTRACT: This study aimed to investigate the outcomes of acute kidney injury (AKI) after cardiac surgery by the meta-analysis.Electronic databases PubMed and Embase were searched for relative studies from December 2008 to June 2015. For eligible studies, the R software was conducted to meta-analyze outcomes of AKI patients (AKI group) and none-AKI patients after cardiac surgery (NO AKI group). The chi-square-based Q test and I statistic were used for heterogeneity analysis. P??50% revealed significant heterogeneity among studies, and then a random effects model was used; otherwise a fixed effect model was performed. Egger's test was performed for publication bias assessment. Subgroup analysis was performed by stratifying AKI definitions and study type.Totally 17 studies with 9656 subjects (2331 in the AKI group and 7325 in the NO AKI group) were enrolled. Significantly higher renal replacement therapy (RRT) (OR=23.67, 95%CI: 12.58-44.55), mortality (OR = 6.27, 95%CI: 3.58-11.00), serum creatinine (SMD = 1.42, 95%CI: 1.01-1.83), and hospital length of stay (LOS) (SMD = 0.45, 95%CI: 0.02-0.88) were shown in the AKI group compared with patients in the NO AKI group. Subgroup analysis showed that results of only 3 subgroups were reversed indicating that the definition of AKI did not affect its outcomes. Publication bias was only found among studies involving mortality and serum creatinine, but the 2 outcomes were not reversed after correction.This meta-analysis confirmed the worse outcomes of AKI in patients after cardiac surgery, including higher RRT rates, mortality, and longer hospital LOS than those of NO AKI patients.

SUBMITTER: Shi Q

PROVIDER: S-EPMC5266033 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Meta-analysis for outcomes of acute kidney injury after cardiac surgery.

Shi Qiankun Q Hong Liang L Mu Xinwei X Zhang Cui C Chen Xin X

Medicine 20161201 49

This study aimed to investigate the outcomes of acute kidney injury (AKI) after cardiac surgery by the meta-analysis.Electronic databases PubMed and Embase were searched for relative studies from December 2008 to June 2015. For eligible studies, the R software was conducted to meta-analyze outcomes of AKI patients (AKI group) and none-AKI patients after cardiac surgery (NO AKI group). The chi-square-based Q test and I statistic were used for heterogeneity analysis. P < 0.1 or I > 50% revealed si ...[more]

PMID: 27930561

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Project description:BackgroundCardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication following cardiac surgery. Early prediction of CSA-AKI is of great significance for improving patients' prognoses. The aim of this study is to systematically evaluate the predictive performance of machine learning models for CSA-AKI.MethodsCochrane Library, PubMed, EMBASE, and Web of Science were searched from inception to 18 March 2022. Risk of bias assessment was performed using PROBAST. Rsoftware (version 4.1.1) was used to calculate the accuracy and C-index of CSA-AKI prediction. The importance of CSA-AKI prediction was defined according to the frequency of related factors in the models.ResultsThere were 38 eligible studies included, with a total of 255,943 patients and 60 machine learning models. The models mainly included Logistic Regression (n = 34), Neural Net (n = 6), Support Vector Machine (n = 4), Random Forest (n = 6), Extreme Gradient Boosting (n = 3), Decision Tree (n = 3), Gradient Boosted Machine (n = 1), COX regression (n = 1), κNeural Net (n = 1), and Naïve Bayes (n = 1), of which 51 models with intact recording in the training set and 17 in the validating set. Variables with the highest predicting frequency included Logistic Regression, Neural Net, Support Vector Machine, and Random Forest. The C-index and accuracy wer 0.76 (0.740, 0.780) and 0.72 (0.70, 0.73), respectively, in the training set, and 0.79 (0.75, 0.83) and 0.73 (0.71, 0.74), respectively, in the test set.ConclusionThe machine learning-based model is effective for the early prediction of CSA-AKI. More machine learning methods based on noninvasive or minimally invasive predictive indicators are needed to improve the predictive performance and make accurate predictions of CSA-AKI. Logistic regression remains currently the most commonly applied model in CSA-AKI prediction, although it is not the one with the best performance. There are other models that would be more effective, such as NNET and XGBoost.Systematic review registrationhttps://www.crd.york.ac.uk/; review registration ID: CRD42022345259.

Dataset Information

Meta-analysis for outcomes of acute kidney injury after cardiac surgery.

Publications

Meta-analysis for outcomes of acute kidney injury after cardiac surgery.

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