Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury.
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ABSTRACT:
SUBMITTER: Cui H
PROVIDER: S-EPMC8751958 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionAcute kidney injury (AKI) is a potentially fatal complication of cardiac surgery. The inability to predict cardiac surgery-associated AKI is a major barrier to prevention and early treatment. Current clinical risk models for the prediction of cardiac surgery-associated AKI are insufficient, particularly in patients with preexisting kidney dysfunction.MethodsTo identify intraoperative variables that might improve the performance of a validated clinical risk score (Cleveland Clinic Score, CCS) for the prediction of cardiac surgery-associated AKI, we conducted a prospective cohort study in 289 consecutive elective cardiac surgery patients at a tertiary care center. We compared the area under the receiver operator characteristic curve (AUC) of a base model including only the CCS with models containing additional selected intraoperative variables including mean arterial pressure, hematocrit, duration of procedure, blood transfusions, and fluid balance. AKI was defined by the Kidney Disease Improving Global Outcomes 2012 criteria.ResultsThe CCS alone gave an AUC of 0.72 (95% confidence interval, 0.62-0.82) for postoperative AKI. Nadir intraoperative hematocrit was the only variable that improved AUC for postoperative AKI when added to the CCS (AUC = 0.78; 95% confidence interval, 0.70-0.87; P = 0.002). In the subcohort of patients without preexisting chronic kidney disease (n = 214), where the CCS underperformed (AUC, 0.60 [0.43-0.76]), the improvement with the addition of nadir hematocrit was more marked (AUC, 0.74 [0.62-0.86]). Other variables did not improve discrimination.DiscussionNadir intraoperative hematocrit is useful in improving discrimination of clinical risk scores for AKI, and may provide a target for intervention.
| S-EPMC5678656 | biostudies-literature
Project description:Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.
| S-EPMC10133500 | biostudies-literature
Project description:BackgroundWe aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI).MethodsUsing 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset.ResultsThe incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI.ConclusionWe were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.
| S-EPMC9656700 | biostudies-literature
Project description:Growth-differentiation factor-15 (GDF-15) is an emerging humoral marker for risk stratification in cardiovascular disease. Cardiac-surgery-associated acute kidney injury (CSA-AKI), an important complication in patients undergoing cardiac surgery, is associated with poor prognosis. The present secondary analysis of an observational cohort study aimed to determine the role of GDF-15 in predicting CSA-AKI compared with the Cleveland-Clinic Acute Renal Failure (CC-ARF) score and a logistic regression model including variables associated with renal dysfunction.Preoperative plasma GDF-15 was determined in 1176 consecutive patients undergoing elective cardiac surgery. Patients with chronic kidney disease stage 5 were excluded. AKI was defined according to Kidney-Disease-Improving-Global-Outcomes (KDIGO) - creatinine criteria. The following variables were screened for association with development of postoperative AKI: age, gender, additive Euroscore, serum creatinine, duration of cardiopulmonary bypass, duration of surgery, type of surgery, total circulatory arrest, preoperative hemoglobin, preoperative oxygen-supplemented cerebral oxygen saturation, diabetes mellitus, hemofiltration during ECC, plasma GDF-15, high sensitivity troponin T (hsTNT), and N-terminal prohormone of B-type natriuretic peptide (NTproBNP).There were 258 patients (21.9 %) with AKI (AKI stage 1 (AKI-1), n?=?175 (14.9 %); AKI-2, n?=?6 (0.5 %); AKI-3, n?=?77 (6.5 %)). The incidence of AKI-1 and AKI-3 increased significantly from the lowest to the highest tertiles of GDF-15. In logistic regression, preoperative GDF-15, additive Euroscore, age, plasma creatinine, diabetes mellitus, and duration of cardiopulmonary bypass were independently associated with AKI. Inclusion of GDF-15 in a logistic regression model comprising these variables significantly increased the area under the curve (AUC 0.738 without and 0.750 with GDF-15 included) and the net reclassification ability to predict AKI. Comparably, in receiver operating characteristic analysis the predictive capacity of the CC-ARF score (AUC 0.628) was improved by adding GDF-15 (AUC 0.684) but this score also had lower predictability than the logistic regression model. In random forest analyses the predictive capacity of GDF-15 was especially pronounced in patients with normal plasma creatinine.This suggests that preoperative plasma GDF-15 independently predicts postoperative AKI in patients undergoing elective cardiac surgery and is particularly helpful for risk stratification in patients with normal creatinine.NCT01166360 on July 20, 2010.
| S-EPMC5055664 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI.ObjectivesThis study tries to predict postoperative AKI using interpretable machine learning models.MethodsFor this study, the information of 1435 patients was collected from multiple centers. The gathered data are in six categories: demographic characteristics and type of surgery, past medical history (PMH), drug history (DH), laboratory information, anesthesia and surgery information, and postoperative variables. Machine learning methods, including support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), random forest (RF), logistic regression, XGBoost, and AdaBoost, were used to predict postoperative AKI. Local interpretable model-agnostic explanations (LIME) and the Shapley methods were then leveraged to check the interpretability of models.ResultsComparing the area under the curves (AUCs) obtained for different machine learning models show that the RF and XGBoost methods with values of 0.81 and 0.80 best predict postoperative AKI. The interpretations obtained for the machine learning models show that creatinine (Cr), cardiopulmonary bypass time (CPB time), blood sugar (BS), and albumin (Alb) have the most significant impact on predictions.ConclusionsThe treatment team can be informed about the possibility of postoperative AKI before cardiac surgery using machine learning models such as RF and XGBoost and adjust the treatment procedure accordingly. Interpretability of predictions for each patient ensures the validity of obtained predictions.
| S-EPMC10016126 | biostudies-literature
Project description:ObjectiveTissue inhibitor of metalloproteinases 2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) are postoperative urinary biomarkers of renal stress and acute kidney injury (AKI). We conducted this study to test the hypothesis that intraoperative concentrations of urinary [TIMP-2]·[IGFBP7] are associated with postoperative AKI.MethodsWe measured urinary [TIMP-2]·[IGFBP7] at 8 perioperative timepoints in 400 patients who participated in a randomized controlled trial of atorvastatin for AKI in cardiac surgery. We compared [TIMP-2]·[IGFBP7] between subjects who did and did not develop KDIGO stage 2 or 3 AKI within 48 hours of surgery, adjusted for AKI risk factors.ResultsFourteen patients (3.5%) met the primary endpoint of stage 2 or 3 AKI within 48 hours of surgery, and an additional 77 patients (19.3%) developed stage 1 AKI. Patients who developed stage 2 or 3 AKI displayed bimodal elevations of [TIMP-2]·[IGFBP7], with a first elevation (median, 0.45 [ng/mL]2/1000) intraoperatively and a second elevation (1.45 [ng/mL]2/1000) 6 hours postoperatively. Patients who did not develop AKI did not have any elevations in [TIMP-2]·[IGFBP7]. Each 10-fold increase in intraoperative [TIMP-2]·[IGFBP7] was independently associated with a 290% increase in the odds of stage 2 or 3 AKI (P = .01), and each 10-fold increase in the 6 hours postoperative [TIMP-2]·[IGFBP7] was independently associated with a 650% increase in the odds of stage 2 or 3 AKI (P < .001). The maximum [TIMP-2]·[IGFBP7] between these 2 timepoints provided an area under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI], 0.73-0.90), 100% sensitivity, and 100% negative predictive value using the >0.3 cutoff to predict stage 2 or 3 AKI.ConclusionsIntraoperative elevations of [TIMP-2]·[IGFBP7] can predict moderate or severe AKI and could provide an opportunity to alter postoperative management to prevent kidney injury.
| S-EPMC6431272 | biostudies-literature
Project description:Acute kidney injury (AKI) after cardiac surgery is a common and underappreciated syndrome that is associated with poor shortand long-term outcomes. AKI after cardiac surgery may be epiphenomenon, a signal for adverse outcomes by virtue of other affected organ systems, and a consequence of multiple factors. Subtle increases in serum creatinine (SCr) postoperatively, once considered inconsequential, have been shown to reflect a kidney injury that likely occurred in the operating room during cardiopulmonary bypass (CPB) and more often in susceptible individuals. The postoperative elevation in SCr is a delayed signal reflecting the intraoperative injury. Preoperative checklists and the conduct of CPB represent opportunities for prevention of AKI. Newer definitions of AKI provide us with an opportunity to scrutinize perioperative processes of care and determine strategies to decrease the incidence of AKI subsequent to cardiac surgery. Recognizing and mitigating risk factors preoperatively and optimizing intraoperative practices may, in the aggregate, decrease the incidence of AKI. This review explores the pathophysiology of AKI and addresses the features of patients who are the most vulnerable to AKI. Preoperative strategies are discussed with particular attention to a readiness for surgery checklist. Intraoperative strategies include minimizing hemodilution and maximizing oxygen delivery with specific suggestions regarding fluid management and plasma preservation.
| S-EPMC4566816 | biostudies-other
Project description:To determine the changes in intra-renal gene expression in a novel large animal model of post Cardiopulmonary Bypass (CPB) acute kidney injury, we collected renal medulla samples obtained 24hours post intervention. The transcriptional profile of the mRNA in these samples was measured with gene array technology. Pigs were subjected to 2.5 hours of general anaesthesia or 2.5 hours of CPB under general anaesthesia. Renal medulla samples were collected 24 hours post intervention.
2014-09-29 | E-GEOD-44782 | biostudies-arrayexpress
Project description:IntroductionAcute lung injury (ALI) after cardiac surgery is associated with a high postoperative morbidity and mortality, but few predictors are known for the occurrence of the complication. This study evaluated whether elevated plasma levels of soluble receptor for advanced glycation end products (sRAGE) and S100A12 reflected impaired lung function in infants and young children after cardiac surgery necessitating cardiopulmonary bypass (CPB).MethodsConsecutive children younger than 3 years after cardiac surgery were prospectively enrolled and assigned to ALI and non-ALI groups, according to the American-European Consensus Criteria. Plasma concentrations of sRAGE and S100A12 were measured at baseline, before, and immediately after CPB, as well as 1 hour, 12 hours, and 24 hours after operation.ResultsFifty-eight patients were enrolled and 16 (27.6%) developed postoperative ALI. Plasma sRAGE and S100A12 levels increased immediately after CPB and remained significantly higher in the ALI group even 24 hour after operation (P < 0.01). In addition, a one-way MANOVA revealed that the overall sRAGE and S100A12 levels were higher in the ALI group than in the non-ALI group immediately after CPB (P < 0.001). The multivariate logistic regression analysis showed that the plasma sRAGE level immediately after CPB was an independent predictor for postoperative ALI (OR, 1.088; 95% CI, 1.011 to 1.171; P = 0.025). Increased sRAGE and S100A12 levels immediately after CPB were significantly correlated with a lower PaO2/FiO2 ratio (P < 0.01) and higher radiographic lung-injury score (P < 0.01), as well as longer mechanical ventilation time (sRAGEN: r = 0.405; P = 0.002; S100A12N: r = 0.322; P = 0.014), longer surgical intensive care unit stay (sRAGEN: r = 0.421; P = 0.001; S100A12N: r = 0.365; P = 0.005) and hospital stay (sRAGEN: r = 0.329; P = 0.012; S100A12N: r = 0.471; P = 0.001).ConclusionsElevated sRAGE and S100A12 levels correlate with impaired lung function, and sRAGE is a useful early biomarker of ALI in infants and young children undergoing cardiac surgery.
| S-EPMC3580637 | biostudies-literature
Project description:IntroductionAcute kidney injury (AKI) after cardiac surgery is independently associated with a prolonged hospital stay, increased cost of care, and increased post-operative mortality. Delayed elevation of serum creatinine (SCr) levels requires novel biomarkers to provide a prediction of AKI after cardiac surgery. Our objective was to find a novel blood biomarkers combination to construct a model for predicting AKI after cardiac surgery and risk stratification.MethodsThis was a case-control study. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to Gene Expression Omnibus (GEO) dataset GSE30718 to seek potential biomarkers associated with AKI. We measured biomarker levels in venous blood samples of 67 patients with AKI after cardiac surgery and 59 control patients in two cohorts. Clinical data were collected. We developed a multi-biomarker model for predicting cardiac-surgery-associated AKI and compared it with a traditional clinical-factor-based model.ResultsFrom bioinformatics analysis and previous articles, we found 6 potential plasma biomarkers for the prediction of AKI. Among them, 3 biomarkers, such as growth differentiation factor 15 (GDF15), soluble suppression of tumorigenicity 2 (ST2, IL1RL1), and soluble urokinase plasminogen activator receptor (uPAR) were found to have prediction ability for AKI (area under the curve [AUC] > 0.6) in patients undergoing cardiac surgery. They were then incorporated into a multi-biomarker model for predicting AKI (C-statistic: 0.84, Brier 0.15) which outperformed the traditional clinical-factor-based model (C-statistic: 0.73, Brier 0.16).ConclusionOur research validated a promising plasma multi-biomarker model for predicting AKI after cardiac surgery.
| S-EPMC8795513 | biostudies-literature