Dataset Information

Platelet Counts, Acute Kidney Injury, and Mortality after Coronary Artery Bypass Grafting Surgery.

ABSTRACT: BACKGROUND:Cardiac surgery requiring cardiopulmonary bypass is associated with platelet activation. Because platelets are increasingly recognized as important effectors of ischemia and end-organ inflammatory injury, the authors explored whether postoperative nadir platelet counts are associated with acute kidney injury (AKI) and mortality after coronary artery bypass grafting (CABG) surgery. METHODS:The authors evaluated 4,217 adult patients who underwent CABG surgery. Postoperative nadir platelet counts were defined as the lowest in-hospital values and were used as a continuous predictor of postoperative AKI and mortality. Nadir values in the lowest 10th percentile were also used as a categorical predictor. Multivariable logistic regression and Cox proportional hazard models examined the association between postoperative platelet counts, postoperative AKI, and mortality. RESULTS:The median postoperative nadir platelet count was 121 × 10/l. The incidence of postoperative AKI was 54%, including 9.5% (215 patients) and 3.4% (76 patients) who experienced stages II and III AKI, respectively. For every 30 × 10/l decrease in platelet counts, the risk for postoperative AKI increased by 14% (adjusted odds ratio, 1.14; 95% CI, 1.09 to 1.20; P < 0.0001). Patients with platelet counts in the lowest 10th percentile were three times more likely to progress to a higher severity of postoperative AKI (adjusted proportional odds ratio, 3.04; 95% CI, 2.26 to 4.07; P < 0.0001) and had associated increased risk for mortality immediately after surgery (adjusted hazard ratio, 5.46; 95% CI, 3.79 to 7.89; P < 0.0001). CONCLUSION:The authors found a significant association between postoperative nadir platelet counts and AKI and short-term mortality after CABG surgery.

SUBMITTER: Kertai MD

PROVIDER: S-EPMC5040517 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Platelet Counts, Acute Kidney Injury, and Mortality after Coronary Artery Bypass Grafting Surgery.

Kertai Miklos D MD Zhou Shan S Karhausen Jörn A JA Cooter Mary M Jooste Edmund E Li Yi-Ju YJ White William D WD Aronson Solomon S Podgoreanu Mihai V MV Gaca Jeffrey J Welsby Ian J IJ Levy Jerrold H JH Stafford-Smith Mark M Mathew Joseph P JP Fontes Manuel L ML

Anesthesiology 20160201 2

<h4>Background</h4>Cardiac surgery requiring cardiopulmonary bypass is associated with platelet activation. Because platelets are increasingly recognized as important effectors of ischemia and end-organ inflammatory injury, the authors explored whether postoperative nadir platelet counts are associated with acute kidney injury (AKI) and mortality after coronary artery bypass grafting (CABG) surgery.<h4>Methods</h4>The authors evaluated 4,217 adult patients who underwent CABG surgery. Postoperati ...[more]

PMID: 26599400

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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.

Project description:BackgroundCoronary artery bypass grafting (CABG) is associated with both cardiovascular disease (CVD) and non-CVD traits. In addition, women's prognosis after coronary events and revascularizations is worse than in men. As the course of CVD in women differs from that of men, we performed a phenome-wide analysis on the sex differences in CABG -related morbidity and mortality.Materials and methodsWe performed an untargeted analysis on the sex differences in predictors and outcomes of CABG. We studied a sample of 176,680 FinnGen participants, including 5,950 individuals who underwent CABG (4,988 men and 962 women) and were followed between 1998 and 2019. Over 1,100 different traits were analyzed for both sexes and the results were adjusted with age, smoking status and BMI. Cox proportional hazards models with sex-trait interactions were used to estimate the associations between (1) traits and incident CABG; and (2) CABG and incident traits.ResultsIn women, CABG was more strongly related to greater increases in risk of diseases such as hypertension, Alzheimer's, aortic aneurysms, gout, and chronic kidney disease compared to risk increases observed in men (all interaction p-values < 0.03). After CABG, men had 2.5-fold (p = 3.1E-15) and women 6.3-fold (p = 9.4E-08) greater risk of cardiac death compared to same-sex individuals who did not undergo CABG (p for interaction 8.2E-4). Moreover, the risk of death in women remained higher even 12 years after CABG, whereas the long-term risk of death in men was not increased, compared to same-sex individuals who did not undergo CABG.ConclusionThe adverse outcomes after CABG, both quantity and quality, also appear to differ between men and women. In women, CABG is related to greater long-term increases in risk of cardiac death and several other disease states than in men. Consideration should therefore be given to whether women receive adequate long-term post-operative therapy and follow-up as CABG is not associated with equally improved cardiovascular disease prognosis in women than in men.

Project description:Background and objectiveHeart failure (HF) is a global health issue, and coronary artery bypass graft (CABG) is one of the most effective surgical treatments for HF with coronary artery disease. Unfortunately, the incidence of postoperative acute kidney injury (AKI) is high in HF patients following CABG, and there are few tools to predict AKI after CABG surgery for such patients. The aim of this study is to establish a nomogram to predict the incidence of AKI after CABG in patients with impaired left ventricular ejection fraction (LVEF).MethodsFrom 2012 to 2017, Clinical information of 1208 consecutive patients who had LVEF< 50% and underwent isolated CABG was collected to establish a derivation cohort. A novel nomogram was developed using the logistic regression model to predict postoperative AKI among these patients. According to the same inclusion criteria and the same period, we extracted the data of patients from 6 other large cardiac centers in China (n = 540) from the China Heart Failure Surgery Registry (China-HFSR) database for external validation of the new model. The nomogram was compared with 3 other available models predicting renal failure after cardiac surgery in terms of calibration, discrimination and net benefit.ResultsIn the derivation cohort (n = 1208), 90 (7.45%) patients were diagnosed with postoperative AKI. The nomogram included 7 independent risk factors: female, increased preoperative creatinine(> 2 mg/dL), LVEF< 35%, previous myocardial infarction (MI), hypertension, cardiopulmonary bypass(CPB) used and perioperative blood transfusion. The area under the receiver operating characteristic curve (AUC) was 0.738, higher than the other 3 models. By comparing calibration curves and decision curve analyses (DCA) with other models, the novel nomogram showed better calibration and greater net benefit. Among the 540 patients in the validation cohort, 104 (19.3%) had postoperative AKI, and the novel nomogram performed better with respect to calibration, discrimination and net benefit.ConclusionsThe novel nomogram is a reliable model to predict postoperative AKI following isolated CABG for patients with impaired LVEF.

Dataset Information

Platelet Counts, Acute Kidney Injury, and Mortality after Coronary Artery Bypass Grafting Surgery.

Publications

Platelet Counts, Acute Kidney Injury, and Mortality after Coronary Artery Bypass Grafting Surgery.

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