Dataset Information

Improving the Prediction of Cardiac Surgery-Associated Acute Kidney Injury.

ABSTRACT:

Introduction

Acute 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.

Methods

To 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.

Results

The 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.

Discussion

Nadir intraoperative hematocrit is useful in improving discrimination of clinical risk scores for AKI, and may provide a target for intervention.

SUBMITTER: Crosina J

PROVIDER: S-EPMC5678656 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Improving the Prediction of Cardiac Surgery-Associated Acute Kidney Injury.

Crosina Jordan J Lerner Jordyn J Ho Julie J Tangri Navdeep N Komenda Paul P Hiebert Brett B Choi Nora N Arora Rakesh C RC Rigatto Claudio C

Kidney international reports 20161021 2

<h4>Introduction</h4>Acute 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.<h4>Methods</h4>To identify intraoperative variables that might improve the performance of a validated clinical risk score (Cl ...[more]

PMID: 29142955

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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.

Project description:PurposeTo establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers.Patients and methodsThis study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria.ResultsFive biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]).ConclusionOur study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Project description:Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) may be related to the increase of chronic kidney disease and mortality in critically ill patients, so developing potential biomarkers is particularly important for disease prediction and early diagnosis. The purpose of this study was to investigate the alteration profiles of plasma metabolome and the possible links with urinary metabolome in patients with CSA-AKI. Methods: Non-targeted metabolomics was performed on plasma samples collected from groups of patients with CSA-AKI at different time points, including Before_AKI (uninjured kidney), AKI_Day1 (injured kidney) and AKI_Day14 (recovered kidney). The differential plasma metabolites associated with CSA-AKI were screened out using multivariate and univariate statistical methods, and altered metabolic pathways were identified by examining the Kyoto Encyclopedia of Genes and Genomes database. After receiver operating characteristic analysis to obtain potential biomarkers, the differential plasma metabolites with Level 1 were used for correlation analysis and metabolomic interplay with urinary metabolome. Results: Nearly 1750 plasma metabolites were identified through bioinformatics methods at Annotation Levels 1-3. The plasma metabolome of injured kidney can be well separated from the plasma metabolomes of uninjured or recovered groups, but the plasma samples from the AKI_Day14 and Before_AKI groups cannot be distinguished using statistical analysis. Compared with the uninjured kidney group, the patients with CSA-AKI displayed dramatic changes in plasma metabolism, particularly for amino acid metabolism. As for the potential biomarkers with Level 1 identification in the plasma metabolome, carnitines and amino acid metabolites exhibited a significant positive correlation, while carnitines and organic acids in plasma also involved in the metabolomic interplay with urinary metabolome. Conclusions: Plasma metabolite disorder was observed in patients with CSA-AKI due to ischaemia and medical treatment, and the recovered patients’ systemic status were able to return to normal. This work provides data on plasma metabolite markers on CSA-AKI and possible link to urinary metabolome.

Dataset Information

Improving the Prediction of Cardiac Surgery-Associated Acute Kidney Injury.

Introduction

Methods

Results

Discussion

Publications

Improving the Prediction of Cardiac Surgery-Associated Acute Kidney Injury.

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