Acute Kidney Injury After Pediatric Cardiac Surgery

Association between postoperative ibuprofen exposure and acute kidney injury after pediatric cardiac surgery

BACKGROUND

Acute kidney injury (AKI) is a common complication after pediatric cardiac surgery and is associated with worse outcomes. Ibuprofen is widely used in the perioperative period and can affect kidney function in children. However, the association between ibuprofen exposure and AKI after pediatric cardiac surgery has not been determined yet.

METHODS

In this retrospective cohort study, children undergoing cardiac surgery with cardiopulmonary bypass were studied. Exposure was defined as given ibuprofen in the first 7 days after surgery. Postoperative AKI was diagnosed using the KDIGO criteria. A multivariable Cox regression model was used to assess the association between ibuprofen exposure and postoperative AKI by taking ibuprofen as a time-varying covariate.

RESULTS

Among 1,112 included children, 198 of them (17.8%) experienced AKI. In total, 396 children (35.6%) were exposed to ibuprofen. AKI occurred less frequently among children who were administered ibuprofen than among those who were not (46 of 396 [11.6%] vs. 152 of 716 [21.2%], p < 0.001). Using the Cox regression model accounting for time-varying exposures, ibuprofen treatment was not associated with AKI (adjusted HR, 0.99; 95% CI 0.70-1.39, p = 0.932). This insignificant association was consistent across the sensitivity and subgroup analyses.

CONCLUSIONS

Postoperative ibuprofen exposure in pediatric patients undergoing cardiac surgery was not associated with an increased risk of AKI.

Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Pediatric Patients Undergoing Cardiac Surgery

Acute kidney injury (AKI) is a common complication after cardiac surgery and associated with adverse outcomes. The purpose of this study is to construct a nomogram to predict the probability of postoperative AKI in pediatric patients undergoing cardiac surgery. We conducted a single-center retrospective cohort study of 1137 children having cardiac surgery under cardiopulmonary bypass. We randomly divided the included patients into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator regression model was used for feature selection. We constructed a multivariable logistic regression model to select predictors and develop a nomogram to predict AKI risk. Discrimination, calibration and clinical benefit of the final prediction model were evaluated in the development and validation cohorts. A simple nomogram was developed to predict risk of postoperative AKI using six predictors including age at operation, cyanosis, CPB duration longer than 120 min, cross-clamp time, baseline albumin and baseline creatinine levels. The area under the receiver operator characteristic curve of the nomogram was 0.739 (95% CI 0.693-0.786) and 0.755 (95% CI 0.694-0.816) for the development and validation cohort, respectively. The calibration curve showed a good correlation between predicted and observed risk of postoperative AKI. Decision curve analysis presented great clinical benefit of the nomogram. This novel nomogram for predicting AKI after pediatric cardiac surgery showed good discrimination, calibration and clinical practicability.

A Clinical Mathematical Model Estimating Postoperative Urine Output in Children Underwent Cardiopulmonary Bypass for Congenital Heart Surgery

Objectives Our objective was to build a proof of concept of the clinical mathematical model estimating postoperative urine output (UOP) utilizing preoperative, intraoperative, and immediate postoperative variables in children who underwent cardiopulmonary bypass (CPB) for congenital heart surgery.

Methods This was a single-center, retrospective cohort study in a university-affiliated children's hospital. Patients younger than 21 years old who underwent CPB for congenital heart surgery and were postoperatively admitted to West Virginia University Children's Hospital's pediatric intensive care unit (PICU) between September 1, 2007 and June 31, 2013 were included in the study. Body surface area, CPB duration, first measured hematocrit, serum pH, central venous pressure, and vasoactive-inotropic score in the PICU were used to build the mathematical model. A randomly selected 50% of the dataset was used to calculate model parameters. A cross-validation was used to assess model performance.

Results A total of 256 patients met the inclusion criteria. The model was able to achieve mean absolute error of 1.065 mL/kg/h (95% confidence interval (CI): 1.062–1.067 mL/kg/h), root mean squared error of 1.80 mL/kg/h (95% CI: 1.799–1.804 mL/kg/h), and R2 of 0.648 (95% CI: 0.646–0.650) in estimating UOP in the first 32 hours of postoperative period.

Conclusions The mathematical model utilizing preoperative, intraoperative, and immediate postoperative variables may be a potentially useful clinical tool in estimating UOP in the first 32 hours postoperative period.