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Development of acute kidney injury following pediatric cardiac surgery.

ABSTRACT: Acute kidney injury (AKI) in the pediatric population is a relatively common phenomenon. Specifically, AKI has been found in increasing numbers within the pediatric population following cardiac surgery, with up to 43% of pediatric patients developing AKI post-cardiac surgery. However, recent advances have allowed for the identification of risk factors. These can be divided into preoperative, intraoperative, and postoperative factors. Although the majority of pediatric patients developing AKI after cardiac surgery completely recover, this condition is associated with worse outcomes. These include fluid overload and increased mortality and result in longer hospital and intensive care unit stays. Detecting the presence of AKI has advanced; use of relatively novel biomarkers, including neutrophil gelatinase associated lipocalin, has shown promise in detecting more subtle changes in kidney function when compared to conventional methods. While a single, superior treatment has not been elucidated yet, novel functions of medications, including fenoldopam, theophylline and aminophylline, have been shown to have better outcomes for these patients. With the recent advances in identification of risk factors, outcomes, diagnosis, and management, the medical community can further explain the complexities of AKI in the pediatric population post-cardiac surgery.

SUBMITTER: Sharma A

PROVIDER: S-EPMC7530361 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Development of acute kidney injury following pediatric cardiac surgery.

Sharma Aditya A Chakraborty Ronith R Sharma Katyayini K Sethi Sidharth K SK Raina Rupesh R

Kidney research and clinical practice 20200901 3

Acute kidney injury (AKI) in the pediatric population is a relatively common phenomenon. Specifically, AKI has been found in increasing numbers within the pediatric population following cardiac surgery, with up to 43% of pediatric patients developing AKI post-cardiac surgery. However, recent advances have allowed for the identification of risk factors. These can be divided into preoperative, intraoperative, and postoperative factors. Although the majority of pediatric patients developing AKI aft ...[more]

PMID: 32773391

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Project description:BackgroundCardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI.MethodsA total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model.ResultsDevelopment of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772-0.898), whereas the AUC (0.843, 95% CI 0.778-0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model.ConclusionsIn this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

Project description:BackgroundCardiac surgery-associated acute kidney injury (AKI) is associated with increased morbidity and mortality. We examined the utility of combining biomarkers of kidney function loss (serum cystatin C) and kidney tubular damage (urine neutrophil gelatinase-associated lipocalin [NGAL] and Kidney Injury Molecule-1 [KIM-1]) for the prediction of post-cardiac surgery AKI.MethodsSingle-center prospective cohort study of 106 adults undergoing coronary artery bypass grafting and/or valve surgery with cardiopulmonary bypass (CPB). Primary outcome was postoperative in-hospital AKI defined by serum creatinine (SCr)-Kidney Disease: Improving Global Outcomes criteria. Biomarkers were measured preoperatively, 6 hours after CPB and on postoperative days (PODs) 1 to 4.ResultsA total of 23 subjects (21.7%) developed AKI. After adjusting for preoperative left ventricular ejection fraction, body mass index >30 kg/m2, and estimated glomerular filtration rate (eGFR) <60 ml/min per 1.73 m2, the combination of peak serum cystatin C and peak urine KIM-1/creatinine (Cr) (6 hours post-CPB to POD 1) above optimal cutoff significantly associated with postoperative AKI (odds ratio [OR]: 5.32; 95% confidence interval [CI]: 1.31-21.67; P = 0.020). This biomarker combination significantly improved the performance of the clinical model for the prediction of postoperative AKI (area under the curve [AUC]: 0.77, 95% CI: 0.65-0.90 for the clinical model alone versus 0.83, 95% CI: 0.73-0.93 for the clinical model with the addition of biomarker data, P = 0.049).ConclusionsCombining biomarkers of postoperative kidney function loss and postoperative kidney tubular damage significantly improved prediction of in-hospital AKI following cardiac surgery. Future large, multicenter studies are warranted to assess whether panels of biomarkers reflecting distinct pathobiology can be used to guide interventions and improve short- and long-term outcomes in patients undergoing cardiac surgery.

Project description:OBJECTIVES:To understand the effect of tight glycemic control on cardiac surgery-associated acute kidney injury. DESIGN:Secondary analysis of data from the Safe Pediatric Euglycemia after Cardiac Surgery trial of tight glycemic control versus standard care. SETTING:Pediatric cardiac ICUs at University of Michigan, C.S. Mott Children's Hospital, and Boston Children's Hospital. PATIENTS:Children 0-36 months old undergoing congenital cardiac surgery. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Cardiac surgery-associated acute kidney injury was assigned using the Acute Kidney Injury Network criteria with the modification that a greater than 0.1?mg/dL increase in serum creatinine was required to assign cardiac surgery-associated acute kidney injury. We explored associations between cardiac surgery-associated acute kidney injury and tight glycemic control and clinical outcomes. Of 799 patients studied, cardiac surgery-associated acute kidney injury occurred in 289 patients (36%), most of whom had stage II or III disease (72%). Cardiac surgery-associated acute kidney injury rates were similar between treatment groups (36% vs 36%; p = 0.99). Multivariable modeling showed that patients with cardiac surgery-associated acute kidney injury were younger (p = 0.002), underwent more complex surgery (p = 0.005), and had longer cardiopulmonary bypass times (p = 0.002). Cardiac surgery-associated acute kidney injury was associated with longer mechanical ventilation and ICU and hospital stays and increased mortality. Patients at University of Michigan had higher rates of cardiac surgery-associated acute kidney injury compared with Boston Children's Hospital patients (66% vs 15%; p < 0.001), but University of Michigan patients with cardiac surgery-associated acute kidney injury had shorter time to extubation and ICU and hospital stays compared with Boston Children's Hospital patients. CONCLUSIONS:Tight glycemic control did not reduce the cardiac surgery-associated acute kidney injury rate in this trial cohort. We observed significant differences in cardiac surgery-associated acute kidney injury rates between the two study sites, and there was a differential effect of cardiac surgery-associated acute kidney injury on clinical outcomes by site. These findings warrant further investigation to identify causal variation in perioperative practices that affect cardiac surgery-associated acute kidney injury epidemiology.

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Development of acute kidney injury following pediatric cardiac surgery.

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Development of acute kidney injury following pediatric cardiac surgery.

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