Predictors of Postoperative Acute Kidney Injury after Coronary Artery Bypass Graft Surgery

Prediction of Acute Kidney Injury Following Isolated Coronary Artery Bypass Grafting in Heart Failure Patients with Preserved Ejection Fraction Using Machine Leaning with a Novel Nomogram

Background

The incidence of postoperative acute kidney injury (AKI) is high due to insufficient perfusion in patients with heart failure. Heart failure patients with preserved ejection fraction (HFpEF) have strong heterogeneity, which can obtain more accurate results. There are few studies for predicting AKI after coronary artery bypass grafting (CABG) in HFpEF patients especially using machine learning methodology.

Methods

Patients were recruited in this study from 2018 to 2022. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. The machine learning methods adopted included logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gaussian naive bayes (GNB), and light gradient boosting machine (LGBM). We used the receiver operating characteristic curve (ROC) to evaluate the performance of these models. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were utilized to compare the prediction model.

Results

In our study, 417 (23.6%) patients developed AKI. Among the five models, random forest was the best predictor of AKI. The area under curve (AUC) value was 0.834 (95% confidence interval (CI) 0.80-0.86). The IDI and NRI was also better than the other models. Ejection fraction (EF), estimated glomerular filtration rate (eGFR), age, albumin (Alb), uric acid (UA), lactate dehydrogenase (LDH) were also significant risk factors in the random forest model.

Conclusions

EF, eGFR, age, Alb, UA, LDH are independent risk factors for AKI in HFpEF patients after CABG using the random forest model. EF, eGFR, and Alb positively correlated with age; UA and LDH had a negative correlation. The application of machine learning can better predict the occurrence of AKI after CABG and may help to improve the prognosis of HFpEF patients.

Amlodipine alleviates renal ischemia/reperfusion injury in rats through Nrf2/Sestrin2/PGC-1α/TFAM Pathway

BACKGROUND

Previously, observational studies showed that amlodipine can mitigate calcineurin inhibitor- and contrast-induced acute kidney injury (AKI). Herein, we aimed to measure the effect of amlodipine on renal ischemia/reperfusion (I/R) injury and find the underlying mechanisms.

MATERIALS AND METHODS

Bilateral renal I/R was induced by clamping the hilum of both kidneys for 30 min. The first dose of amlodipine 10 mg/kg was gavaged before anesthesia. The second dose of amlodipine was administered 24 h after the first dose. Forty-eight hours after I/R, rats were anesthetized, and their blood and tissue specimens were collected.

RESULTS

Amlodipine significantly decreased the elevated serum levels of creatinine and blood urea nitrogen (BUN) and mitigated tissue damage in hematoxylin & eosin (H&E) staining. Amlodipine strongly reduced the tissue levels of malondialdehyde (MDA), interleukin 1β (IL1β), and tumor necrosis factor α (TNF-α). Amlodipine enhanced antioxidant defense by upregulating nuclear factor erythroid 2-related factor 2 (Nrf2) and Sestrin2. Furthermore, amlodipine significantly improved mitochondrial biogenesis by promoting Sestrin2/peroxisome proliferator-activated receptor-gamma coactivator (PGC-1α)/mitochondrial transcription factor A (TFAM) pathway. It also enhanced autophagy and attenuated apoptosis, evidenced by increased LC3-II/LC3-I and bcl2/bax ratios after renal I/R.

CONCLUSION

These findings suggest that amlodipine protects against renal I/R through Nrf2/Sestrin2/PGC-1α/TFAM Pathway.

Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG.

METHODS

A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP).

RESULTS

The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area.

CONCLUSION

This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.

Investigating preoperative myoglobin level as predictive factor for acute kidney injury following cardiac surgery with cardiopulmonary bypass: a retrospective observational study

BACKGROUND

Early identification of patients at risk of AKI after cardiac surgery is of critical importance for optimizing perioperative management and improving outcomes. This study aimed to identify the association between preoperative myoglobin levels and postoperative acute kidney injury (AKI) in patients undergoing valve surgery or coronary artery bypass graft surgery (CABG) with cardiopulmonary bypass.

METHODS

This retrospective study included 293 patients aged over 17 years who underwent valve surgery or CABG with cardiopulmonary bypass. We excluded 87 patients as they met the exclusion criteria. Therefore, 206 patients were included in the final analysis. The patients... demographics as well as intraoperative and postoperative data were collected from electronic medical records. AKI was defined according to the Acute Kidney Injury Network classification system.

RESULTS

Of the 206 patients included in this study, 77 developed AKI. The patients who developed AKI were older, had a history of hypertension, underwent valve surgery with concomitant CABG, had lower preoperative hemoglobin levels, and experienced prolonged extracorporeal circulation (ECC) times. Multivariate logistic regression analysis revealed that preoperative myoglobin levels and ECC time were correlated with the development of AKI. A higher preoperative myoglobin level was an independent risk factor for the development of cardiac surgery-associated AKI.

CONCLUSIONS

Higher preoperative myoglobin levels may enable physicians to identify patients at risk of developing AKI and optimize management accordingly.

Incidence and peri-operative risk factors for development of acute kidney injury in patients after cardiac surgery: A prospective observational study

BACKGROUND

Patients admitted to intensive care unit (ICU) after cardiac surgery develop acute kidney injury (AKI) immediately post-operation. We hypothesized that AKI occurs mainly due to perioperative risk factors and may affect outcome.

AIM

To assess peri-operative risk factors for AKI post cardiac surgery and its relationship with clinical outcome.

METHODS

This was an observational single center, tertiary care setting study, which enrolled 206 consecutive patients, admitted to ICU after cardiac surgery. Patients were followed-up until ICU discharge or death, in order to determine the incidence of AKI, perioperative risk factors for AKI and its association with outcome. Univariate and multivariate logistic regression analysis was performed to assess predictor variables for AKI development.

RESULTS

After ICU admission, 55 patients (26.7%) developed AKI within 48 h. From the logistic regression analysis performed, high EuroScore II (OR: 1.18; 95%CI: 1.06-1.31, P = 0.003), white blood cells (WBC) pre-operatively (OR: 1.0; 95%CI: 1.0-1.0, P = 0.002) and history of chronic kidney disease (OR: 2.82; 95%CI: 1.195-6.65, P = 0.018) emerged as independent predictors of AKI among univariate predictors. AKI that developed AKI had longer duration of mechanical ventilation [1113 (777–2195) vs 714 (511–1020) min, P = 0.0001] and ICU length of stay [70 (28–129) vs 26 (21–51) h, P = 0.0001], higher rate of ICU-acquired weakness (16.4% vs 5.3%, P = 0.015), reintubation (10.9% vs 1.3%, P = 0.005), dialysis (7% vs 0%, P = 0.005), delirium (36.4% vs 23.8%, P = 0.001) and mortality (3.6% vs 0.7%, P = 0.046).

CONCLUSION

Patients present frequently with AKI after cardiac surgery. EuroScore II, WBC count and chronic kidney disease are independent predictors of AKI development. The occurrence of AKI is associated with poor outcome.

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

Type 2 diabetes mellitus and In-hospital Major Adverse Cardiac and Cerebrovascular Events (MACCEs) and postoperative complications among patients undergoing on-pump isolated coronary artery bypass surgery in Northeastern Iran

BACKGROUND

Diabetes Mellitus (DM) is a rapidly growing disorder worldwide, especially in the Middle East. A higher incidence of coronary artery diseases requiring coronary artery bypass graft (CABG) surgery has been reported in patients with diabetes. We assessed the association between type 2 diabetes mellitus (T2DM) and in-hospital major adverse cardiac and cerebrovascular events (MACCEs) and postoperative complications among patients who underwent on-pump isolated CABG.

METHODS

In this retrospective cohort study, we used the data registered for CABG patients from two heart centers in the Golestan province (North of Iran) between 2007 and 2016. The study population included 1956 patients divided into two groups: 1062 non-diabetic patients and 894 patients with diabetes (fasting plasma glucose ≥126 mg/dl or using antidiabetic medications). The study outcome was in-hospital MACCEs, a composite outcome of myocardial infarction (MI), stroke and cardiovascular death, and postoperative complications, including postoperative arrhythmia, acute atrial fibrillation (AF), major bleeding (defined as reoperation due to bleeding), and acute kidney injury (AKI).

RESULTS

During the 10-year study period, 1956 adult patients with a mean (SD) age of 59.0 (9.60) years were included. After adjustment for age, gender, ethnicity, obesity, opium consumption, and smoking, diabetes was a predictor of postoperative arrhythmia (AOR 1.30, 95% CI 1.08-1.57; P = 0.006). While it was not a predictor of in-hospital MACCEs (AOR 1.35, 95% CI 0.86, 2.11; P = 0.188), AF (AOR 0.85, 95% CI 0.60-1.19; P = 0.340), major bleeding (AOR 0.80, 95% CI 0.50, 1.30; P = 0.636) or AKI (AOR 1.29, 95% CI 0.42, 3.96; P 0.656) after CABG surgery.

CONCLUSION

Findings indicated that diabetes increased the risk of postoperative arrhythmia by 30%. However, we found similar in-hospital MACCEs, acute AF, major bleeding, and AKI following CABG surgery in both diabetic and non-diabetic patients.

Urine metabolites for preoperative prediction of acute kidney injury after coronary artery bypass graft surgery

OBJECTIVE

Acute kidney injury is a common complication after on-pump coronary artery bypass grafting. Prediction of acute kidney injury remains a challenge. Our study aims to identify a panel of urine metabolites for preoperative warning of acute kidney injury after on-pump coronary artery bypass grafting.

METHODS

A total of 159 patients undergoing isolated on-pump coronary artery bypass grafting were enrolled from July 7, 2017, to May 17, 2019. Preoperative urine samples were analyzed with the approach of liquid chromatography-mass spectrometry-based urine metabolomics. The study end point was the episode of acute kidney injury within 48 hours postoperatively. The predictive performance was determined by the area under the curve and calibration curve. The results were validated using bootstrap resampling.

RESULTS

The acute kidney injury (n = 55) and nonacute kidney injury (n = 104) groups showed significant different metabolic profiling. A total of 28 metabolites showed significant differences between the acute kidney injury and nonacute kidney injury groups. A metabolite panel of 5 metabolites (tyrosyl-gamma-glutamate, deoxycholic acid glycine conjugate, 5-acetylamino-6-amino-3-methyluracil, arginyl-arginine, and L-methionine) was discovered to have a good predicting performance (area under the curve, 0.89; 95% confidence interval, 0.82-0.93), which is higher than the clinical factor-based model (area under the curve, 0.63; 95% confidence interval, 0.53-0.72). Internal validation by bootstrap resampling showed an adjusted area under the curve of 0.88, and the calibration curve demonstrated good agreement between prediction and observation in the probability of postoperative acute kidney injury. Decision curve analysis revealed a superior net benefit of the metabolite model over the traditional clinical factor-based model.

CONCLUSIONS

We present 5 urine metabolites related to acute kidney injury after coronary artery bypass grafting. This metabolite model may serve as a preoperative warning of acute kidney injury after on-pump coronary artery bypass grafting.

Impact of Oxygen Delivery on the Development of Acute Kidney Injury in Patients Undergoing Valve Heart Surgery

One of the strongest risk factors for death in individuals undergoing cardiac surgery is Cardiac Surgery Associated-Acute Kidney Injury (CSA-AKI). Although the minimum kidney oxygen delivery index (DO₂i) during cardiopulmonary bypass (CPB) has been reported, the optimal threshold value has not yet been established. A prospective study was conducted from June 2012 to January 2016 to asses how DO₂i influences the pathogenesis of CSA-AKI, as well as its most favorable cut-off value. DO₂ levels were recorded at the beginning, middle, and end of the CPB. The association between DO₂i and CSA-AKI was investigated using multivariable logistic regression analysis. The optimal cut-off of DO₂i as a predictor of CSA-AKI was determined using Classification and Regression Tree (CART) analysis. A total of 782 consecutive patients were enrolled. Of these, 231 (29.5%) patients developed AKI. Optimal DO₂i thresholds of 303 mL/min/m² during the CPB and 295 mL/min/m² at the end of the intervention were identified, which increased the odds of CSA-AKI almost two-fold (Odds Ratio (OR), 1.90; 95% CI, 1.12–3.24) during the surgery and maintained that risk (OR 1.94; 95% CI, 1.15–3.29) until the end. Low DO₂i during cardiopulmonary bypass is a risk factor for CSA-AKI that cannot be ruled out. Continuous renal oxygen supply monitoring for adult patients could be a promising method for predicting AKI during CPB.

A COMPARISON OF AKIN, KDIGO AND RIFLE DEFINITIONS TO DIAGNOSE ACUTE KIDNEY INJURY AND PREDICT THE OUTCOMES AFTER CARDIAC SURGERY IN A SOUTH ASIAN COHORT

BACKGROUND

Acute kidney injury (AKI) after cardiac surgery is associated with an increased mortality and morbidity. Different definitions for AKI have been used such as Acute Kidney Injury Network (AKIN), Kidney Disease: Improving Global Outcomes (KDIGO) or risk, injury, failure, loss, end-stage kidney disease (RIFLE). Each of these definitions have their own benefits and limitations for predicting the degree of AKI and adverse outcomes following cardiac surgery. This study was aimed to compare the three AKI definitions to diagnose AKI and their predictive ability for mortality and morbidity after isolated coronary artery bypass surgery (CABG) in a South Asian cohort.

MATERIAL AND METHODS

A single center retrospective review was conducted on 1508 patients having undergone isolated CABG surgery during January 2015 to January 2019. AKI was assessed on three definitions and comparative receiver operating characteristics curves were built against the outcomes to assess discriminative power of each.

RESULTS

Mean age of participants was 59.43 (±1.12) years, predominantly males (82.6%). Patient with AKI were elder, more likely to be diabetic and hypertensive. AKI by any definition occurred in 58.7% (885/1508) patients. Frequency of AKI was 508 (33.7%), 517 (34.4%) and 871 (57.8%) on AKIN, KDIGO and RIFLE criteria, respectively. The proportion of patients with RIFLE- risk 619 (41%) was greater compared with AKIN stage 1 [342 (22.7%)], and KDIGO stage 1 [330 (21.9%)], while for stage 2 and 3 the proportions were comparable across the three systems. Area under the curve (AUC) for 30-day mortality for AKIN was [0.786, (95% CI: 0.764 to 0.806)], KDIGO: [0.796, (95% CI: 0.775 to 0.816)], and for RIFLE [0.844, (95% CI: 0.825 to 0.862)]. AUC for overall morbidity were in undesirable ranges (i.e. >0.5 to ≤ 0.7) for all three definitions.

CONCLUSION

In a South Asian cohort, performance of AKIN and KDIGO criteria was comparable to diagnose AKI, while RIFLE definition, though overestimated the incidence of AKI particularly stage 1, and had an excellent discriminatory power to predict mortality compared to other two definitions.

Preoperative use of furosemide may increase the incidence of acute kidney injury after coronary artery bypass grafting: a propensity score-matched study

OBJECTIVES

Furosemide is usually administered before the Coronary artery bypass grafting (CABG) to improve water-sodium retention. However, no final conclusions are available on the postoperative renal outcome of furosemide. We evaluated the effect of preoperative furosemide on acute kidney injury (AKI) after CABG.

METHODS

We recorded the use of furosemide 14 days before surgery in all patients who underwent CABG from 2016 to 2017. Patients were divided into furosemide (F) group and non-furosemide (NF) group according to preoperative use of furosemide. A 1:1 propensity score matching was performed. Multivariate analyses were conducted to determine risk factors for AKI after CABG.

RESULTS

Overall, 974 patients were included in the study, of which 82 cases were complicated with postoperative AKI. The incidence of AKI was significantly increased in F group than NF group (28.9% vs. 7.4%, p = 0.000). After adjusting for risk factors, the incidence of AKI in the F group was 5.34 times more than the NF group (95% confidence interval [CI] 2.45-11.64; p = 0.000). The incidence of AKI increased significantly when the cumulative dosage of furosemide exceeded 110 mg (odds ratio [OR] 6.23; 95% CI 2.07-18.74, p = 0.001) and 250 mg (OR 8.31; 95% CI 2.87-24.02, p = 0.000). After the propensity-matching group analysis, same results were obtained.

CONCLUSIONS

The incidence of AKI after CABG was related to the use of preoperative furosemide, and it increased exponentially with the increase of cumulative dose of furosemide. This provides guidance for the dose of preoperative furosemide.

Acute Kidney Injury following Cardiopulmonary Bypass: A Challenging Picture

Recent studies have recognized several risk factors for cardiopulmonary bypass- (CPB-) associated acute kidney injury (AKI). However, the lack of early biomarkers for AKI prevents practitioners from intervening in a timely manner. We reviewed the literature with the aim of improving our understanding of the risk factors for CPB-associated AKI, which may increase our ability to prevent or improve this condition. Some novel early biomarkers for AKI have been introduced. In particular, a combinational use of these biomarkers would be helpful to improve clinical outcomes. Furthermore, we discuss several interventions that are aimed at managing CPB-associated AKI, may increase the effect of renal replacement therapy (RRT), and may contribute to preventing CPB-associated AKI. Collectively, the conclusions of this paper are limited by the availability of clinical trial evidence and conflicting definitions of AKI. A guideline is urgently needed for CPB-associated AKI.

Positive Heparin/PF4 Antibodies and High Mortality Rate: a Retrospective Case-Series Analysis

Introduction

Heparin-induced thrombocytopenia (HIT) is a potentially lethal complication of unfractionated or low-molecular weight heparin therapy. We aimed to determine the incidence and mortality rate of patients with positive heparin/platelet factor 4 (PF4) antibodies, which is a rapid detection test of HIT.

Methods

Coronary artery bypass grafting and mitral and aortic valve surgeries were evaluated. Cardiopulmonary bypass was employed in all patients. The diagnosis of HIT was based on immunological assays. Postoperative complications, mortality rates, and the causes of death were specified in patients with positive heparin/PF4 antibodies.

Results

Postoperative thrombocytopenia was detected in 257 patients. Twenty of these patients undergoing open heart surgery were included in the final analysis. Antibodies against heparin/PF4 complex were positive in 20 patients. The mean body mass index was 28.8±2.3 kg/m², mean value of left ventricular ejection fraction was 48.3±6.7%, cardiopulmonary bypass time was 113.0±35.0 min, aortic cross-clamping time was 88.0±32.7 min, mean intensive care unit length of stay was 10.9±4.9 days, mean preoperative platelet count was 307.250±88528 platelets/microliter, and mean postoperative platelet count was 243.050±89.354 platelets/microliter. The mean duration of heparin exposure was 6.9±2.9 days. The mortality rate was 45% (nine patients) and 1.2% (three patients) in heparin/PF4 complex positive and negative patients, respectively.

Conclusion

Although the incidence of HIT was low in patients undergoing open heart surgery, an increased rate of early mortality was observed in patients with positive heparin/PF4 antibodies.

Near-Infrared Spectroscopy Monitoring in Cardiac and Noncardiac Surgery: Pairwise and Network Meta-Analyses

Goal-directed therapy based on brain-oxygen saturation (bSo2) is controversial and hotly debated. While meta-analyses of aggregated data have shown no clinical benefit for brain near-infrared spectroscopy (NIRS)-based interventions after cardiac surgery, no network meta-analyses involving both major cardiac and noncardiac procedures have yet been undertaken. Randomized controlled trials involving NIRS monitoring in both major cardiac and noncardiac surgery were included. Aggregate-level data summary estimates of critical outcomes (postoperative cognitive decline (POCD)/postoperative delirium (POD), acute kidney injury, cardiovascular events, bleeding/need for transfusion, and postoperative mortality) were obtained. NIRS was only associated with protection against POCD/POD in cardiac surgery patients (pooled odds ratio (OR)/95% confidence interval (CI)/I2/number of studies (n): 0.34/0.14-0.85/75%/7), although a favorable effect was observed in the analysis, including both cardiac and noncardiac procedures. However, the benefit of the use of NIRS monitoring was undetectable in Bayesian network meta-analysis, although maintaining bSo2 > 80% of the baseline appeared to have the most pronounced impact. Evidence was imprecise regarding acute kidney injury, cardiovascular events, bleeding/need for transfusion, and postoperative mortality. There is evidence that brain NIRS-based algorithms are effective in preventing POCD/POD in cardiac surgery, but not in major noncardiac surgery. However, the specific target bSo2 threshold has yet to be determined.

Predictive risk factors of acute kidney injury after on-pump coronary artery bypass grafting

Background

Acute kidney injury (AKI) is a common complication after coronary artery bypass grafting (CABG) and increases the risk of short and long-term morbidity and mortality. The aim of our study is to identify preoperative and intraoperative risk factors for development of AKI after primary isolated on-pump CABG.

Methods

In the retrospective study, 210 consecutive patients who underwent primary isolated on-pump CABG from January 2007 to March 2016 were included. The patients were divided into without AKI group (Group 1) and AKI group (Group 2) after operation. The s-Cr levels were recorded pre and postoperatively. The demographics, preoperative and postoperative data were collected from patient's medical profile and analyzed statistically.

Results

AKI developed in 40.5% of the patients (85 patients out of 210 patients). Age (Group 1; Group 2, 63.7±8.6; 67.2±8.2, P=0.004), body surface area (BSA) (Group 1; Group 2, 1.71±0.16; 1.64±0.16, P=0.003), body weight (Group 1; Group 2, 64.1±10.0; 60.7±10.2, P=0.017) were statistically significant for the development of AKI. However, preoperative hemoglobin, blood urea nitrogen (BUN), creatinine, estimated glomerular filtration rate (eGFR) and C-reactive protein (CRP) were not significant. As intraoperative factors, total pump time (TPT), aortic cross clamp time and transfusion were not significant. Female gender (OR 1.88; P=0.044), preoperative proteinuria (OR 2.711; P=0.011) and emergent operation (OR 2.641; P=0.035) were risk factors in univariate analysis. Preoperative proteinuria (OR 2.396; P=0.035) was only risk factor in multivariate analysis.

Conclusions

Preoperative proteinuria was an independent predictor of postoperative AKI in patients undergoing primary isolated on-pump CABG. The accurate risk prediction of AKI after surgery can help clinicians manage more effectively in high-risk patients.