Cardiac surgery-associated acute kidney injury: risk factors analysis and comparison of prediction models

Uric Acid to Albumin Ratio: A Predictive Marker for Acute Kidney Injury in Isolated Tricuspid Valve Surgery

Background

The plasma uric acid/albumin ratio (UAR) has emerged as a novel inflammatory biomarker for predicting the development of acute kidney injury (AKI) following percutaneous coronary intervention. However, the potential of the UAR to serve as a predictive marker for AKI in patients undergoing isolated tricuspid valve (TV) surgery remains unknown. This study aimed to explore the association between the UAR and AKI and to assess whether the UAR can predict AKI in these patients.

Methods

We conducted a retrospective analysis of patients who underwent isolated TV surgery between January 2018 and June 2019. The patients were divided into three groups based on the tertiles of the UAR. We utilized multivariate logistic regression and restricted cubic spline analysis to examine the association between the UAR and AKI. Additionally, we used the receiver operating characteristic (ROC) curve analysis to assess the predictive accuracy of the UAR for AKI.

Results

A total of 224 patients were enrolled in this study, of whom 41 developed AKI. The incidence of AKI across the three UAR tertiles was 3.8%, 22.2%, and 29.7%, with a significant difference between the group (p < 0.001). In the multivariate analysis, UAR ≥8.5 was associated with a 7-fold increased risk of AKI (odds ratio (OR): 7.73, 95% confidence interval (CI): 1.61-37.14), while a UAR ≥10.8 was a linked to a 9-fold increased risk (OR: 9.34, 95% CI: 1.96-44.60). The restricted cubic spline model showed a linear association between the UAR and AKI development. The area under the curve (AUC) value for the UAR was 0.713 (95% CI: 0.633-0.793; p < 0.001) with a cutoff value of 8.89.

Conclusions

An increased UAR was significantly associated with a higher risk of AKI in patients undergoing isolated TV surgery; however, while the UAR could serve as a marker to predict AKI, it was not superior to uric acid alone.

Perioperative Acute Kidney Injury: Diagnosis, Prediction, Prevention, and Treatment

In this review, the authors define acute kidney injury in the perioperative setting, describe the epidemiologic burden, discuss procedure-specific risk factors, detail principles of management, and highlight areas of ongoing controversy and research.

High-Density Lipoprotein Anti-Inflammatory Capacity and Acute Kidney Injury After Cardiac and Vascular Surgery: A Prospective Observational Study

OBJECTIVES

Acute kidney injury (AKI) predicts death after cardiac and vascular surgery. Higher preoperative high-density lipoprotein (HDL) concentrations are associated with less postoperative AKI. In animals, HDL's anti-inflammatory capacity to suppress endothelial cell adhesion molecule expression reduces kidney damage due to ischemia and hemorrhagic shock. The objective of this study is to evaluate the statistical relationship between HDL anti-inflammatory capacity and AKI after major cardiac and vascular surgery.

DESIGN

Prospective observational study.

SETTING

Quaternary medical center.

PATIENTS

One hundred adults with chronic kidney disease on long-term statin therapy undergoing major elective cardiac and vascular surgery.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Apolipoprotein B-depleted serum collected at anesthetic induction was incubated with tumor necrosis factor alpha stimulated human endothelial cells. Reverse transcriptase-polymerase chain reaction was used to measure intercellular adhesion molecule-1 (ICAM-1) messenger RNA. Enzyme-linked immunosorbent assay assays were used to measure apolipoprotein A-I and postoperative soluble ICAM-1 concentrations in patient plasma. HDL concentration did not correlate with HDL ICAM-1 suppression capacity (Spearman R = 0.05; p = 0.64). Twelve patients (12%) were found to have dysfunctional, pro-inflammatory HDL. Patients with pro-inflammatory HDL had a higher rate of postoperative AKI than patients with anti-inflammatory HDL ( p = 0.046). After adjustment for AKI risk factors, a higher preoperative HDL capacity to suppress endothelial ICAM-1 was independently associated with lower odds of AKI (odds ratio, 0.88; 95% CI, 0.80-0.98; p = 0.016). The association between HDL anti-inflammatory capacity and postoperative AKI was independent of HDL concentration ( p = 0.018). Further, a higher long-term statin dose was associated with higher HDL capacity to suppress endothelial ICAM-1 ( p = 0.045).

CONCLUSIONS

Patients with chronic kidney disease undergoing cardiac and vascular surgery who have dysfunctional, pro-inflammatory HDL have a higher risk of postoperative AKI compared with patients with anti-inflammatory HDL. Conversely, a higher HDL anti-inflammatory capacity is associated with a lower risk of postoperative AKI, independent of HDL concentration. Higher long-term statin dose is associated with higher HDL anti-inflammatory capacity.

Features selection in a predictive model for cardiac surgery-associated acute kidney injury

BACKGROUND

Cardiac surgery-associated acute kidney injury (CSA-AKI) is related to increased morbidity and mortality. However, limited studies have explored the influence of different feature selection (FS) methods on the predictive performance of CSA-AKI. Therefore, we aimed to compare the impact of different FS methods for CSA-AKI.

METHODS

CSA-AKI is defined according to the kidney disease: Improving Global Outcomes (KDIGO) criteria. Both traditional logistic regression and machine learning methods were used to select the potential risk factors for CSA-AKI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. In addition, the importance matrix plot by random forest was used to rank the features' importance.

RESULTS

A total of 1977 patients undergoing cardiac surgery at Fuwai hospital from December 2018 to April 2021 were enrolled. The incidence of CSA-AKI during the first postoperative week was 27.8%. We concluded that different enrolled numbers of features impact the final selected feature number. The more you input, the more likely its output with all FS methods. In terms of performance, all selected features by various FS methods demonstrated excellent AUCs. Meanwhile, the embedded method demonstrated the highest accuracy compared with the LR method, while the filter method showed the lowest accuracy. Furthermore, NT-proBNP was found to be strongly associated with AKI. Our results confirmed some features that previous studies have reported and found some novel clinical parameters.

CONCLUSIONS

In our study, FS was as suitable as LR for predicting CSA-AKI. For FS, the embedded method demonstrated better efficacy than the other methods. Furthermore, NT-proBNP was confirmed to be strongly associated with AKI.

AKI development is an independent predictor of mortality in infective endocarditis

INTRODUCTION

Infective endocarditis presents a 25% mortality. Acute kidney injury (AKI) develops in up to 70% of the cases. The aim of this study is to evaluate the predictive value of AKI in mortality due to endocarditis and to assess its associated factors.

METHODS

Unicentric and retrospective study including all patients with in-hospital diagnosis of endocarditis between 2015 and 2021. Epidemiological data and comorbidities were collected at baseline. During admission, renal function parameters, infection-related variables and mortality were collected. Using adjusted multivariate models, LRA predictive value was determined.

RESULTS

One hundred and thirty-four patients (63% males, age 72±15 years) were included. Of them 94 (70%) developed AKI (50% AKIN-1, 29% AKIN-2 and 21% AKIN-3). Factors associated to AKI were age (p=0.03), hypertension (p=0.005), previous chronic kidney disease (p=0.001), heart failure (p=0.006), peripheral vascular disease (p=0.022) and glomerular filtration rate (GFR) at baseline (p<0.001). GFR at baseline was the only factor independently associated to AKI (OR 0.94, p=0.001). In-hospital deaths were registered in 46 (34%) patients. Of them, 45 (98%) patients had developed AKI. AKI was independently associated to mortality through diverse multivariate models. GFR loss (OR 1.054, p<0.001) and GFR at baseline (0.963, p=0.012) also predicted mortality during admission.

CONCLUSIONS

AKI development and its severity (GFR loss and AKIN severity) impacts in in-hospital mortality due to infective endocarditis.

A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study

BACKGROUND

Acute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.

OBJECTIVE

The primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.

METHODS

We developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.

RESULTS

The developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.

CONCLUSIONS

NAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

MicroRNA-192-5p downregulates Fat Mass and Obesity-associated Protein to aggravate renal ischemia/reperfusion injury

Acute kidney injury (AKI) is a common disorder without effective therapy yet. Renal ischemia/reperfusion (I/R) injury is a common cause of AKI. MicroRNA miR-192-5p has been previously reported to be upregulated in AKI models. However, its functional role in renal I/R injury is not fully understood. This study aimed to investigate the effects and the underlying mechanism of miR-192-5p in renal I/R progression. Hypoxia/reoxygenation (H/R)-induced cell injury model in HK-2 cells and I/R-induced renal injury model in mice were established in this study. Cell counting kit-8 assay was performed to determine cell viability. Quantitative real-time PCR and western blot analysis were performed to detect gene expressions. Hematoxylin-eosin and periodic acid-Schiff staining were performed to observe the histopathological changes. Enzyme-linked immunosorbent assay was performed to detect the kidney markers' expression. In vivo and in vitro results showed that miR-192-5p was up-regulated in the I/R-induced mice model and H/R-induced cell model, and miR-192-5p overexpression exacerbated I/R-induced renal damage. Then, the downstream target of miR-192-5p was analyzed by combining the differentially expressed mRNAs and the predicted genes and confirmed using a dual-luciferase reporter assay. It was found that miR-192-5p was found to regulate fat mass and obesity-associated (FTO) protein expression by directly targeting the 3' untranslated region of FTO mRNA. Moreover, in vivo and in vitro studies unveiled that FTO overexpression alleviated renal I/R injury and promoted HK-2 cell viability via stimulating autophagy flux. In conclusion, miR-192-5p aggravated I/R-induced renal injury by blocking autophagy flux via down-regulating FTO.

Acute Kidney Injury after Cardiac Surgery: Prediction, Prevention, and Management

Acute kidney injury (AKI) is a common complication in cardiac surgery patients, with a reported incidence of 20 to 30%. The development of AKI is associated with worse short- and long-term mortality, and longer hospital length of stay. The pathogenesis of cardiac surgery-associated AKI is poorly understood but likely involves an interplay between preoperative comorbidities and perioperative stressors. AKI is commonly diagnosed by using increases in serum creatinine or decreased urine output and staged using a standardized definition such as the Kidney Disease Improving Global Outcomes classification. Novel biomarkers under investigation may provide earlier detection and better prediction of AKI, enabling mitigating therapies early in the perioperative period. Recent clinical trials of cardiac surgery patients have demonstrated the benefit of goal-directed oxygen delivery, avoidance of hyperthermic perfusion and specific fluid and medication strategies. This review article highlights both advances and limitations regarding the prevention, prediction, and treatment of cardiac surgery-associated AKI.

Central vs. Peripheral Cannulation During Reoperations: A Propensity Score Matching Analysis

INTRODUCTION

The aim of this study is to compare the postoperative outcomes and early mortality of peripheral and central cannulation techniques in cardiac reoperations using propensity score matching analysis.

METHODS

In this retrospective cohort, patients who underwent cardiac reoperations with median resternotomy were analyzed in terms of propensity score matching. Between November 2010 and September 2020, 257 patients underwent cardiac reoperations via central (Group 1) or peripheral (Group 2) cannulation. A 1:1 propensity score matching was performed to balance the influence of potential confounding factors to compare postoperative data and mortality rate.

RESULTS

There were no significant differences when comparing the matched groups regarding early mortality (P=0.51), major cardiac injury (P=0.99), prolonged ventilation (P=0.16), and postoperative stroke (P=0.99). The development of acute renal failure (P=0.02) was statistically less frequent in Group 1.

CONCLUSIONS

Performing cardiopulmonary bypass via peripheral cannulation increases acute renal failure in cardiac reoperations. In contrast, peripheral or central cannulation have similar early mortality rate in cardiac reoperations.

Combining Machine Learning and Urine Oximetry: Towards an Intraoperative AKI Risk Prediction Algorithm

Acute kidney injury (AKI) affects up to 50% of cardiac surgery patients. The definition of AKI is based on changes in serum creatinine relative to a baseline measurement or a decrease in urine output. These monitoring methods lead to a delayed diagnosis. Monitoring the partial pressure of oxygen in urine (PuO2) may provide a method to assess the patient's AKI risk status dynamically. This study aimed to assess the predictive capability of two machine learning algorithms for AKI in cardiac surgery patients. One algorithm incorporated a feature derived from PuO2 monitoring, while the other algorithm solely relied on preoperative risk factors. The hypothesis was that the model incorporating PuO2 information would exhibit a higher area under the receiver operator characteristic curve (AUROC). An automated forward variable selection method was used to identify the best preoperative features. The AUROC for individual features derived from the PuO2 monitor was used to pick the single best PuO2-based feature. The AUROC for the preoperative plus PuO2 model vs. the preoperative-only model was 0.78 vs. 0.66 (p-value < 0.01). In summary, a model that includes an intraoperative PuO2 feature better predicts AKI than one that only includes preoperative patient data.

Increased numbers of pre-operative circulating monocytes predict risk of developing cardiac surgery-associated acute kidney injury in conditions requiring cardio pulmonary bypass

BACKGROUND

Evaluating patients' risk for acute kidney injury (AKI) is crucial for positive outcomes following cardiac surgery. Our aims were first to select candidate risk factors from pre- or intra-operative real-world parameters collected from routine medical care and then evaluate potential associations between those parameters and risk of onset of post-operative cardiac surgery-associated AKI (CSA-AKI).

METHOD

We conducted two cohort studies in Japan. The first was a single-center prospective cohort study (n = 145) to assess potential association between 115 clinical parameters collected from routine medical care and CSA-AKI (≥ Stage1) risk in the population of patients undergoing cardiac surgery involving cardiopulmonary bypass (CPB). To select candidate risk factors, we employed random forest analysis and applied survival analyses to evaluate association strength. In a second retrospective cohort study, we targeted patients undergoing cardiac surgery with CPB (n = 619) and evaluated potential positive associations between CSA-AKI incidence and risk factors suggested by the first cohort study.

RESULTS

Variable selection analysis revealed that parameters in clinical categories such as circulating inflammatory cells, CPB-related parameters, ventilation, or aging were potential CSA-AKI risk factors. Survival analyses revealed that increased counts of pre-operative circulating monocytes and neutrophils were associated with CSA-AKI incidence. Finally, in the second cohort study, we found that increased pre-operative circulating monocyte counts were associated with increased CSA-AKI incidence.

CONCLUSIONS

Circulating monocyte counts in the pre-operative state are associated with increased risk of CSA-AKI development. This finding may be useful in stratifying patients for risk of developing CSA-AKI in routine clinical practice.

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.

The prognostic value of elevated neutrophil-lymphocyte ratio for cardiac surgery-associated acute kidney injury: A systematic review and meta-analysis

BACKGROUND

Patients undergoing cardiac surgery are at significant risk of developing postoperative acute kidney injury (AKI). Neutrophil-lymphocyte ratio (NLR) is a widely available inflammatory biomarker which may be of prognostic value in this setting.

METHODS

We conducted a systematic review and meta-analysis of studies reporting associations between perioperative NLR with postoperative AKI. We searched Medline, Embase and the Cochrane Library, without language restriction, from inception to May 2022 for relevant studies. We meta-analysed the reported odds ratios (ORs) with 95% confidence intervals (CIs) for both elevated preoperative and postoperative NLR with risk of postoperative AKI and need for renal replacement therapy (RRT). We conducted a meta-regression to explore inter-study statistical heterogeneity.

RESULTS

Twelve studies involving 10,724 participants undergoing cardiac surgery were included, with eight studies being deemed at high risk of bias using PROBAST modelling. We found statistically significant associations between elevated preoperative NLR and postoperative AKI (OR 1.45, 95% CI 1.18-1.77), as well as postoperative need for RRT (OR 2.37, 95% CI 1.50-3.72). Postoperative NLR measurements were not of prognostic significance.

CONCLUSIONS

Elevated preoperative NLR is a reliable inflammatory biomarker for predicting AKI following cardiac surgery.

Perioperative Management of the Patient at High-Risk for Cardiac Surgery-Associated Acute Kidney Injury

Acute kidney injury (AKI) is one of the most common major complications of cardiac surgery, and is associated with increased morbidity and mortality. Cardiac surgery-associated AKI has a complex, multifactorial etiology, including numerous factors such as primary cardiac dysfunction, hemodynamic derangements of cardiac surgery and cardiopulmonary bypass, and the possibility of a large volume of blood transfusion. There are no truly effective pharmacologic therapies for the management of AKI, and, therefore, anesthesiologists, intensivists, and cardiac surgeons must remain vigilant and attempt to minimize the risk of developing renal dysfunction. This narrative review describes the current state of the scientific literature concerning the specific aspects of cardiac surgery-associated AKI, and presents it in a chronological fashion to aid the perioperative clinician in their approach to this high-risk patient group. The evidence was considered for risk prediction models, preoperative optimization, and the intraoperative and postoperative management of cardiac surgery patients to improve renal outcomes.

Risk Factors for Postoperative Acute Kidney Injury in Patients Undergoing Redo Cardiac Surgery Using Cardiopulmonary Bypass

Objective: This paper aimed to investigate the incidence and risk factors of postoperative acute kidney injury (AKI) in adult patients undergoing redo cardiac surgery with cardiopulmonary bypass (CPB), and explore the impact of AKI on early outcomes. Methods: A total of 116 patients undergoing redo cardiac surgery with CPB between November 2017 and May 2021 were included. Patients were divided into two groups, AKI group and non-AKI group, according to the Kidney Disease Improving Global Outcomes criteria. Perioperative variables were retrospectively collected and analyzed. Risk factors for the development of AKI were investigated by univariate and multiple logistic regression models. Clinical outcomes were also compared between the groups. Results: Postoperative AKI occurred in 63 patients (54.3%), among whom renal replacement therapy was required in 12 patients (19.0%). The mechanical ventilation time (AKI: 43.00 (19.00, 72.00) hours; non-AKI: 18.00 (15.00, 20.00) hours; p < 0.001), ICU length of stay (AKI: 4.00 (2.00, 6.00) days; non-AKI: 3.00 (2.00, 4.00) days; p = 0.010), hospital length of stay since operation (AKI: 12.00 (8.00, 18.00) days; non-AKI: 9.00 (7.00, 12.50) days; p = 0.024), dialysis (AKI: 12.00 (19.05%); non-AKI: 0 (0%); p = 0.001), reintubation (AKI: 7.00 (11.11%); non-AKI: 0 (0%); p = 0.035), and hospital mortality (AKI: 8.00 (12.70%); non-AKI: 0 (0%); p = 0.020) were all higher in the AKI group than in the non-AKI group. Multivariate analysis revealed that high aspartate aminotransferase (OR, 1.028, 95% CI, 1.003 to 1.053, p = 0.025), coronary angiogram within 2 weeks before surgery (OR, 3.209, 95% CI, 1.307 to 7.878, p = 0.011) and CPB time (OR, 1.012, 95% CI, 1.005 to 1.019, p = 0.001) were independent risk factors for postoperative AKI. Conclusions: High aspartate aminotransferase, coronary angiogram within 2 weeks before surgery and CPB time seem to be associated with an increased incidence of postoperative AKI in patients with redo cardiac surgery.

Risk factors for acute kidney injury following coronary artery bypass graft surgery in a Chinese population and development of a prediction model

BACKGROUND

Acute kidney injury (AKI) after coronary artery bypass graft (CABG) surgery is associated with significant morbidity and mortality. This retrospective study aimed to establish a risk score for postoperative AKI in a Chinese population.

METHODS

A total of 1138 patients undergoing CABG were collected from September 2018 to May 2020 and divided into a derivation and validation cohort. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Multivariable logistic regression analysis was used to determine the independent predictors of AKI, and the predictive ability of the model was determined using a receiver operating characteristic (ROC) curve.

RESULTS

The incidence of cardiac surgery–associated acute kidney injury (CSA-AKI) was 24.17%, and 0.53% of AKI patients required dialysis (AKI-D). Among the derivation cohort, multivariable logistic regression showed that age ≥ 70 years, body mass index (BMI) ≥ 25 kg/m², estimated glomerular filtration rate (eGFR) ≤ 60 mL/min per 1.73 m², ejection fraction (EF) ≤ 45%, use of statins, red blood cell transfusion, use of adrenaline, intra-aortic balloon pump (IABP) implantation, postoperative low cardiac output syndrome (LCOS) and reoperation for bleeding were independent predictors. The predictive model was scored from 0 to 32 points with three risk categories. The AKI frequencies were as follows: 0−8 points (15.9%), 9−17points (36.5%) and ≥ 18 points (90.4%). The area under of the ROC curve was 0.730 (95% CI: 0.691−0.768) in the derivation cohort. The predictive index had good discrimination in the validation cohort, with an area under the curve of 0.735 (95% CI: 0.655−0.815). The model was well calibrated according to the Hosmer-Lemeshow test (P = 0.372).

CONCLUSION

The performance of the prediction model was valid and accurate in predicting KDIGO-AKI after CABG surgery in Chinese patients, and could improve the early prognosis and clinical interventions.

Improved creatinine-based early detection of acute kidney injury after cardiac surgery

 

OBJECTIVES

This study aims to improve early detection of cardiac surgery-associated acute kidney injury (CSA-AKI) compared to classical clinical scores.

METHODS

Data from 7633 patients who underwent cardiac surgery between 2008 and 2018 in our institution were analysed. CSA-AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Cleveland Clinical Score served as the reference with an area under the curve (AUC) 0.65 in our cohort. Based on that, stepwise logistic regression modelling was performed on the training data set including creatinine (Cr), estimated glomerular filtration rate (eGFR) levels and deltas (ΔCr, ΔeGFR) at different time points and clinical parameters as preoperative haemoglobin, intraoperative packed red blood cells (units) and cardiopulmonary bypass time (min) to predict CSA-AKI in the early postoperative course. The AUC was determined on the validation data set for each model respectively.

RESULTS

Incidence of CSA-AKI in the early postoperative course was 22.4% (n = 1712). The 30-day mortality was 12.5% in the CSA-AKI group (n = 214) and in the no-CSA-AKI group 0.9% (n = 53) (P < 0.001). Logistic regression models based on Cr and its delta gained an AUC of 0.69; 'Model eGFRCKD-EPI' an AUC of 0.73. Finally, 'Model DynaLab' including dynamic laboratory parameters and clinical parameters as haemoglobin, packed red blood cells and cardiopulmonary bypass time improved AUC to 0.84.

CONCLUSIONS

Model DynaLab' improves early detection of CSA-AKI within 12 h after surgery. This simple Cr-based framework poses a fundament for further endeavours towards reduction of CSA-AKI incidence and severity.

Predicting acute kidney injury following nonemergent cardiac surgery: A preoperative scorecard

OBJECTIVE

To determine the predictors of postoperative acute kidney injury (AKI) following nonemergent cardiac surgery among patients with variable preoperative estimated glomerular filtration rate (eGFR) levels.

METHODS

A retrospective study of patients who underwent elective or in-hospital cardiac surgical procedures was performed between January 2006 and November 2015. The procedures included isolated coronary artery bypass grafting (CABG), isolated aortic valve replacement (AVR), or combined CABG and AVR. The primary outcome AKI (any stage) following nonemergent cardiac surgery utilizing the 2012 Kidney Disease-Improving Global Outcomes (KDIGO) criteria. Patients were categorized based on the following renal outcomes: mild AKI, severe AKI (KDIGO stage 2 or 3), and postoperative dialysis. Patients with G5 preoperative kidney function (including dialysis patients) were excluded.

RESULTS

A total of 6675 patients were included in our study. The mean age was 66.8 years (SD ± 10.4), with 76.3% being males. A total of 4487 patients had normal or mildly decreased eGFR (G1 or G2) preoperatively (67.2%), while 1960 patients were in the G3 category (29.4%). Only 228 patients (3.4%) had G4 renal function. A total of 1453 (21.7%) patients experienced postoperative AKI. The need for postoperative dialysis occurred in 3.2% of the AKI subgroup. In-hospital mortality was higher among the AKI subgroup (7.2% vs. 0.5%; p < .0001). In an adjusted model, a lower preoperative eGFR category was the strongest predictor of AKI. A practical scorecard for the preoperative estimation of severe AKI for nonemergent cardiac procedures incorporating these parameters was developed.

CONCLUSIONS

Preoperative eGFR is the strongest predictor of postoperative AKI in individuals undergoing nonemergent cardiac surgery. A practical scorecard incorporating preoperative predictors of AKI may allow informed decision-making and predict AKI following nonemergent cardiac surgery.

Acute Kidney Injury after Heart Valve Surgery in Elderly Patients: any Risk Factors to Modify?

Introduction

Postoperative acute kidney injury contributes to longer hospital stays and increased costs related to cardiac surgery in the elderly. We analyse the influence of the patient’s age on risk factors for acute kidney injury after cardiac valve surgery.

Methods

We evaluated the prevalence and risk factors for acute kidney injury in 939 consecutive patients undergoing valve surgery, between 2013 and 2018.

Results

The prevalence of acute kidney injury was 19.5%. Hypertension (P=0.017); RR (95% CI): 1.74 (1.10-3.48), age ≥70 years (P=0.006); RR (95% CI): 1.79 (1.17-2.72), preoperative haematocrit <33% (P=0.009); RR (95% CI): 2.04 (1.19-3.48), glomerular filtration rate <60 ml/min/1.73 m² (P<0.0001); RR (95%) CI: 2.36 (1.54-3.62) and cardiac catheterization <8 days before surgery (P=0.021); RR (95% CI): 2.15 (1.12-4.11) were identified as independent risk factors. In patients older than 70 years, with no kidney disease diagnosed preoperatively, glomerular filtration rate <70 ml/min/1.73 m², male gender, cardiopulmonary bypass time, preoperative haematocrit <36% and preoperative therapy with angiotensin-converting enzyme inhibitors were risk factors for acute kidney injury after valve surgery.

Conclusions

In elderly patients, postoperative acute kidney injury develops with higher values of preoperative glomerular filtration rate than those observed in a younger population. Preoperative correction of anaemia, discontinuation of angiotensin-converting enzyme inhibitors and surgical techniques reducing cardiopulmonary bypass time would be considered to reduce the prevalence of renal failure.

Risk assessment of acute kidney injury following cardiopulmonary bypass

BACKGROUND

Acute kidney injury (AKI) is a frequent and serious complication of cardiac surgery, associated with a high incidence of morbidity and mortality. Although the RIFLE criteria serve as a prominent tool to identify patients at high risk of AKI, an optimized diagnosis model in clinical practice is desired.

METHODS

Based on the SOP-criteria, 365 patients (10%) developed AKI following surgery and were subjected to RRT. In contrast, the incidence of AKI, defined according to the RIFLE criteria, was only 7% (n = 251 patients). Prominent risk factors identified by SOP were patients' sex, valve and combined valve and bypass surgery, deep hypothermia, use of intra-aortic balloon pump (IABP) and previous coronary interventions. Ischemia, reperfusion, blood loss and surgery time also served as significant risk factors for patient evaluated by SOP.

RESULTS

Risk assessment by RIFLE differed in as much as most patients with normothermia and those receiving only cardiovascular bypass developed AKI. However, patients' sex and valve surgery did not serve as a risk factor.

CONCLUSION

Evaluation of patients by the RIFLE versus SOP criteria yielded different results with more AKI patients detected by SOP. Based on the present data, it is concluded that patients may not prone to AKI when surgery and ischemia time will be kept short, when blood loss is mitigated to a minimum and when surgery is performed under non-hypothermic conditions.

Defining an Intraoperative Hypotension Threshold in Association with De Novo Renal Replacement Therapy after Cardiac Surgery

BACKGROUND

Acute kidney injury (AKI) is a frequent and deadly complication after cardiac surgery. In the absence of effective therapies, a focus on risk factor identification and modification has been the mainstay of management. The authors sought to determine the impact of intraoperative hypotension on de novo postoperative renal replacement therapy in patients undergoing cardiac surgery, hypothesizing that prolonged periods of hypotension during and after cardiopulmonary bypass (CPB) were associated with an increased risk of renal replacement therapy.

METHODS

Included in this single-center retrospective cohort study were adult patients who underwent cardiac surgery requiring CPB between November 2009 and April 2015. Excluded were patients who were dialysis dependent, underwent thoracic aorta or off-pump procedures, or died before receiving renal replacement therapy. Degrees of hypotension were defined by mean arterial pressure (MAP) as less than 55, 55 to 64, and 65 to 74 mmHg before, during, and after CPB. The primary outcome was de novo renal replacement therapy.

RESULTS

Of 6,523 patient records, 336 (5.2%) required new postoperative renal replacement therapy. Each 10-min epoch of MAP less than 55 mmHg post-CPB was associated with an adjusted odds ratio of 1.13 (95% CI, 1.05 to 1.23; P = 0.002), and each 10-min epoch of MAP between 55 and 64 mmHg post-CPB was associated with an adjusted odds ratio of 1.12 (95% CI, 1.06 to 1.18; P = 0.0001) for renal replacement therapy. The authors did not observe an association between hypotension before and during CPB with renal replacement therapy.

CONCLUSIONS

MAP less than 65 mmHg for 10 min or more post-CPB is associated with an increased risk of de novo postoperative renal replacement therapy. The association between intraoperative hypotension and AKI was weaker in comparison to factors such as renal insufficiency, heart failure, obesity, anemia, complex or emergent surgery, and new-onset postoperative atrial fibrillation. Nonetheless, post-CPB hypotension is a potentially easier modifiable risk factor that warrants further investigation.

Survival and renal recovery after acute kidney injury requiring dialysis outside of intensive care units

BACKGROUND

The incidence of acute kidney injury requiring dialysis (AKI-D) is increasing globally and it is usually associated to chronic kidney disease (CKD) and high mortality. Literature is lacking in short- and intermediate-term data on recovery of renal function after acute kidney injury (AKI).

OBJECTIVES

The objective was to evaluate the overall survival and renal recovery after an episode of AKI requiring dialysis out of intensive care units (ICUs).

MATERIALS AND METHODS

Retrospective study including patients admitted in two nephrology units along a period of 2 years. Patients admitted to ICUs and renal transplant patients were excluded. Baseline renal function, mortality and glomerular filtration rate (GFR) improvement were evaluated.

RESULTS

151 consecutive adult patients with AKI requiring renal replacement therapy (RRT) were included. Mean age was 70.5 ± 15.2 years, 60.3% were males. Median baseline creatinine (bCr) and baseline GFR (bGFR) were 1.4 mg/dL and 46 mL/min/1.73 m², respectively. After 1 year of follow-up, we completed the monitoring of 94 patients: 64.9% had died, 10.6% were alive on dialysis and 24.5% were alive without need for RRT. Patients with bGFR > 60 mL/min/1.73 m² prior to AKI episode had a slower but sustained GFR improvement through the follow-up in comparison with patients with bGFR < 60 mL/min/1.73 m² whose recovery was incomplete.

CONCLUSIONS

Patients with AKI requiring RRT have high short- and intermediate-term mortality and some require maintenance dialysis. Patients with GFR > 60 mL/min/1.73 m² prior to AKI had a renal recovery closer to the basal renal function than in patients with a previously diminished GFR.

Creatinine elevations from baseline at the time of cardiac surgery are associated with postoperative complications

OBJECTIVES

Baseline kidney function is a key predictor of postoperative morbidity and mortality. Whether an increased creatinine at the time of surgery, compared with the lowest creatinine in the 3 months before surgery, is associated with poor outcomes has not been evaluated. We examined whether creatinine elevations from "baseline" were associated with adverse postoperative outcomes.

METHODS

A total of 1486 patients who underwent cardiac surgery at the University of Colorado Hospital between January 2011 and May 2016 met inclusion criteria. "Change in creatinine from baseline" was defined as the difference between the immediate presurgical creatinine value and the lowest creatinine value within 3 months preceding surgery. Outcomes evaluated were in-hospital mortality, postoperative infection, postoperative stroke, development of stage 3 acute kidney injury, intensive care unit length of stay, and hospital length of stay. Outcomes were adjusted using a balancing score to account for differences in patient characteristics.

RESULTS

There were significant increases in the odds of postoperative infection (odds ratio, 1.17; confidence interval, 1.02-1.34; per 0.1 mg/dL increase in creatinine), stage 3 acute kidney injury (odds ratio, 1.44; confidence interval; 1.18-1.75), intensive care unit length of stay (odds ratio, 1.13; confidence interval, 1.01-1.26), and hospital length of stay (odds ratio, 1.09; confidence interval, 1.05-1.13). There was a significant increase in mortality in the unadjusted analysis, although not after adjustment using a balancing score. There was no association with postoperative stroke.

CONCLUSIONS

Elevations in creatinine at the time of surgery above the "baseline" level are associated with increased postoperative morbidity. Baseline creatinine should be established before surgery, and small changes in creatinine should trigger heightened vigilance in the postoperative period.

Acute kidney injury prediction models: current concepts and future strategies

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a critical condition associated with poor patient outcomes. We aimed to review the current concepts and future strategies regarding AKI risk prediction models.

RECENT FINDINGS

Recent studies have shown that AKI occurs frequently in patients with common risk factors and certain medical conditions. Prediction models for AKI risk have been reported in medical fields such as critical care medicine, surgery, nephrotoxic agent exposure, and others. However, practical, generalizable, externally validated, and robust AKI prediction models remain relatively rare. Further efforts to develop AKI prediction models based on comprehensive clinical data, artificial intelligence, improved delivery of care, and novel biomarkers may help improve patient outcomes through precise AKI risk prediction.

SUMMARY

This brief review provides insights for current concepts for AKI prediction model development. In addition, by overviewing the recent AKI prediction models in various medical fields, future strategies to construct advanced AKI prediction models are suggested.

MicroRNA-27a-3p aggravates renal ischemia/reperfusion injury by promoting oxidative stress via targeting growth factor receptor-bound protein 2

Renal ischemia-reperfusion (RI/R) injury with high morbidity and mortality is one common clinical disease. Development of drug targets to treat the disorder is critical important. MiR-27a-3p plays important roles in regulating oxidative stress. However, its effects on RI/R injury have not been reported. In this paper, hypoxia/reoxygenation (H/R) models on NRK-52E and HK-2 cells, and RI/R model in C57BL/6 mice were established. The results showed that H/R in vitro decreased cell viability and increased ROS levels in cells, and RI/R caused renal injury and oxidative damage in mice. The expression levels of miR-27a-3p were up-regulated based on real-time PCR and FISH assays in model groups compared with control groups, which directly targeted Grb2 based on dual luciferase reporter assay and co-transfaction test. In addition, miR-27a- 3p markedly reduced Grb2 expression to down-regulate the expression levels of p-PI3K, p-AKT, Nrf2, HO-1, and up-regulate Keap1 expression in model groups. MiR-27a-3p mimics in vitro enhanced H/R-caused oxidative stress via increasing ROS levels and decreasing Grb2 expression to down-regulate PI3K-AKT signal. In contrary, miR-27a-3p inhibitor in vitro significantly reduced H/R-caused oxidative damage via decreasing ROS levels and increasing Grb2 expression to up-regulate PI3K-AKT signal. In vivo, miR-27a- 3p agomir exacerbated RI/R-caused renal damage by decreasing SOD level and increasing Cr, BUN, MDA levels via suppressing Grb2 expression to down-regulate PI3K- AKT signal. However, miR-27a -3p antagomir alleviated RI/R-caused oxidative damage via increasing Grb2 expression to up-regulate PI3k-AKT signal. Grb2siRNA in mice further enhanced RI/R-caused renal injury by increasing Cr, BUN, MDA levels and decreasing SOD level via inhibiting the expression levels of Grb2, Nrf2, HO-1, and increasing Keap1 expression. Our data showed that miR-27a-3p aggravated RI/R injury by promoting oxidative stress via targeting Grb2, which should be considered as one new drug target to treat RI/R injury.

Risk Factors for Acute Kidney Injury in Hospitalized Non-Critically Ill Patients: A Population-Based Study

OBJECTIVE

To develop and validate an acute kidney injury (AKI) risk prediction model for hospitalized non-critically ill patients.

PATIENTS AND METHODS

We retrospectively identified all Olmsted County, Minnesota, residents admitted to non-intensive care unit (ICU) wards at Mayo Clinic Hospital, Rochester, Minnesota, in 2013 and 2014. The cohort was divided into development and validation sets by year. The primary outcome was hospital-acquired AKI defined by Kidney Disease: Improving Global Outcomes criteria. Cox regression was used to analyze mortality data. Comorbid risk factors for AKI were identified, and a multivariable model was developed and validated.

RESULTS

The development and validation cohorts included 3816 and 3232 adults, respectively. Approximately 10% of patients in both cohorts had AKI, and patients with AKI had an increased risk of death (hazard ratio, 3.62; 95% CI, 2.97-4.43; P<.001). Significant univariate determinants of AKI were preexisting kidney disease, diabetes mellitus, hypertension, heart failure, vascular disease, coagulopathy, pulmonary disease, coronary artery disease, cancer, obesity, liver disease, and weight loss (all P<.05). The final multivariable model included increased baseline serum creatinine value, admission to a medical service, pulmonary disease, diabetes mellitus, kidney disease, cancer, hypertension, and vascular disease. The area under the receiver operating characteristic curves for the development and validation cohorts were 0.71 (95% CI, 0.69-0.75) and 0.75 (95% CI, 0.72-0.78), respectively.

CONCLUSION

Hospital-acquired AKI is common in non-ICU inpatients and is associated with worse outcomes. Patient data at admission can be used to identify increased risk; such patients may benefit from more intensive monitoring and earlier intervention and testing with emerging biomarkers.

Risk estimation model for acute kidney injury defined by KDIGO classification after heart valve replacement surgery

OBJECTIVES

Risk prediction for postoperative acute kidney injury (AKI) has a great clinical value to achieve early prevention strategies for AKI after cardiac surgery. We aimed to identify the patients at risk of postoperative AKI and to create patient risk group for AKI using a simple risk estimation model in patients undergoing heart valve replacement surgery.

METHODS

Between May 2008 and February 2018, 219 consecutive patients undergoing heart valve replacement surgery with or without concomitant coronary artery bypass grafting (CAGB) were included in the study. To define postoperative AKI and its severity stages, KDIGO classification which is the latest uniform classification for determining and staging of AKI was used.

RESULTS

The AKI incidence was 38.8%, and Class I was the dominant stage (43.5%). Postoperative AKI development was associated with a serious of postoperative adverse events, early, and long-term mortality. Furthermore, the incidence of poor outcomes increased with the degree of AKI severity. The presence of older age, chronic obstructive pulmonary disease, NYHA class III-IV, diabetes, concomitant CABG, and longer cardiopulmonary bypass duration was found to be an independent predictor for AKI, and each factor was scored according to the integer value of their odds ratio, based on risk estimation model. Patient risk groups from mild to severe for AKI development were created. The patients at severe risk group exhibited a significantly higher rate of adverse events, early, and long-term mortality as well as lower long-term survival rates.

CONCLUSIONS

The risk estimation model is a useful tool to identify the patients at risk and to create patient risk groups for postoperative AKI defined by KDIGO after heart valve replacement surgery.

Preventing acute kidney injury and improving outcome in critically ill patients utilizing risk prediction score (PRAIOC-RISKS) study. A prospective controlled trial of AKI prevention

BACKGROUND

Acute kidney injury (AKI) has significant impact on mortality and morbidity in critically ill patients.

METHODS

A prospective controlled interventional pilot study composed of observation and intervention arms was run at two different Intensive care unit (ICU) sites. A recently validated risk prediction score was used to predict the AKI in critically ill patients at high risk of developing AKI. All patients with established AKI at the time of recruitment were excluded from the study. A package of early preventive measures, including an early nephrology review was applied to high risk patients in the intervention arm to prevent AKI development.

RESULTS

We have recruited 108 patients at the intervention site and 98 patients at the observation site. The primary outcome measure was the AKI incidence. AKI incidence was significantly lower in the intervention arm than its incidence in the observation arm (11% vs 26%, p = 0.002). The median Time till recovery of AKI episodes was significantly lower in the intervention arm (3(1) vs. 5(2) days, p = 0.014) 0.30 day mortality was lower in the intervention arm, however, not statistically significant.

CONCLUSION

Our pilot study showed that it was feasible to apply a simple risk score to implement early preventive measures to high risk patients, consequently, mitigating the risk of AKI development and reducing the time till recovery of AKI episodes. Multicentre studies are needed to confirm this favourable effect.

[Risk assessment of acute kidney injury in patients with acute cardiovascular disease without invasive intervention]

OBJECTIVE

To evaluate the prevalence, predictors, prognostic value of cardiorenal interrelations in patients with acute cardiovascular disease (CVD), and to develop an algorithm for stratification these patients at risk of acute kidney injury (AKI).

MATERIALS AND METHODS

566 patients (pts) were examined: 278 with acute decompensated heart failure (ADHF) and 288 with non-ST-elevation acute coronary syndrome (NSTE-ACS). The levels of electrolytes, glucose, urea, creatinine were evaluated, glomerular filtration rate (GFR) was determined according to the formula CKD-EPI. Chest x-ray, electrocardiography at admission and in dynamics, echocardiography at admission with assessment of systolic and diastolic myocardial functions were performed. Chronic kidney disease (CKD), AKI, ADHF, NSTE-ACS were diagnosed according to Russian and international Guidelines. Mann-Whitney test and multivariate logistic regression analysis were considered significant if p&lt;0.05.

RESULTS

Different variants of cardiorenal interrelations were revealed in 366 (64.7%) pts. CKD was diagnosed in 259 (45.8%), with more than half of the cases (61%) diagnosed for the first time at this hospitalization, 62 (11%) pts had signs of kidney damage of unknown duration (which did not allow to diagnose CKD). AKI occurred in 228 (40,3%) pts, more frequently in patients with ADHF vs with NSTE-ACS (43.5 and 37.2%). In all groups stage 1 of AKI was prevalent. In-hospital mortality was significantly higher in pts with AKI vs without AKI (14.9 vs 3.6%, p&lt;0.001). The risk of AKI was determined by kidney function and blood pressure levels at admission, and comorbidities.

CONCLUSION

Prevalence of cardiorenal interactions in patients with acute CVD (ADHF and NSTE-ACS) was 64.7%. Development of AKI was associated with poor prognosis in both groups. Renal function and blood pressure levels on admission are the main predictors of AKI.

High-sensitivity cardiac troponin release after conventional and minimally invasive cardiac surgery

After cardiac surgery, a certain degree of myocardial injury is common. The arbitrarily proposed biomarker cut-off point in the Third Universal Definition for diagnosing coronary artery bypass grafting (CABG)–related perioperative myocardial infarction (PMI) is controversial and unvalidated for non-CABG surgery. Minimally invasive cardiac surgery is often thought to be associated with less myocardial damage compared to conventional surgical approaches. We conducted a real-life prospective study with serial sampling of high-sensitivity cardiac troponin T (hs-cTnT) in patients undergoing conventional and minimally invasive cardiac surgery. Four different types of cardiac surgery were performed in 400 patients (February 2014–January 2015): CABG, aortic valve replacement, minimally invasive mitral/tricuspid valve surgery through the HeartPort (HP) technique and combined CABG/valve surgery. Each group was further subdivided for comparison between the different surgical techniques. Blood samples were collected consecutively at intensive care unit (ICU) admission and 3, 6, 9, 12, 18, 24 and 48 h thereafter. The hs-cTnT values by peak timepoint differed significantly depending on the surgical approach. The overall peak timepoint for hs-cTnT occurred 6 h after ICU admission. The combined surgery and multiple-valve HP groups had the highest values (medians of 1067.5 (744.9–1455) ng/L and 1166 (743.7–2470) ng/L, respectively). The peak hs-cTnT values for patients developing PMI showed high variability. Differentiation between cardiac surgery–related necrosis and PMI remains challenging. This study emphasizes the importance of a clinically reliable biomarker cut-off value in addition to electrocardiography and echocardiography to optimize PMI diagnosis.

Prediction of Postoperative Outcomes and Long-Term Survival in Cardiac Surgical Patients Using the Intensive Care National Audit & Research Centre Score

OBJECTIVES

Scoring systems used in cardiac surgery, such as the European System for Cardiac Operative Risk Evaluation and the Society of Thoracic Surgeons scoring systems, do not adjust for events that take place intraoperatively. The authors hypothesized that intensive care unit scoring systems such as the Intensive Care National Audit & Research Centre (ICNARC) could predict accurately not only in-hospital mortality, but also other significant complications, as well as long-term survival after cardiac surgery.

DESIGN

Prospective cohort study using perioperative data from the ICNARC Audit and Dendrite database.

SETTING

Single tertiary referral cardiac surgery center.

PARTICIPANTS

A total of 4,446 consecutive cardiac surgical patients who had surgery between January 2011 and April 2018.

INTERVENTIONS

Comparison of scoring systems to predict postoperative outcomes.

MEASUREMENTS AND RESULTS

Receiver operating curves (ROCs) were used to evaluate how well the ICNARC scores predicted in-hospital mortality and postoperative complications (renal failure, pulmonary complications, gastrointestinal complications, and multiorgan failure). Cox regression analysis was used to determine factors affecting long-term survival. The C-indices for the ROC graphs for the ICNARC score were 0.840 for in-hospital mortality, 0.858 for renal failure, 0.665 for pulmonary complications, 0.764 for gastrointestinal complications, 0.702 for neurological complications in general and 0.654 for confusion, and 0.885 for multiorgan failure. From Cox regression analysis, the significant (p < 0.05) predictors of midterm mortality (5 years) were a higher ICNARC score, a higher age at surgery, chronic obstructive pulmonary disease, preoperative renal failure, preoperative neurological comorbidity, arteriopathy, and non-coronary artery bypass graft surgery.

CONCLUSION

The ICNARC scoring system is simple and can be used as an early warning screening tool to predict which patients are at higher risk for postoperative organ failure.

Development and validation of an automated algorithm for identifying patients at higher risk for drug-induced acute kidney injury

PURPOSE

Using information from institutional electronic health records, we aimed to develop dynamic predictive models to identify patients at high risk of acute kidney injury (AKI) among those who received a nephrotoxic medication during their hospital stay.

METHODS

Candidate predictors were measured for each of the first 5 hospital days where a patient received a nephrotoxic medication (risk model days) to predict an AKI, using logistic regression with reduced backward variables elimination in 100 bootstrap samples. An AKI event was defined as an increase of serum creatinine ≥ 200% of a baseline SCr within 5 days after a risk model day. Final models were internally validated by replication in 100 bootstrap samples and a risk score for each patient was calculated from the validated model. As performance measures, the area under the receiver operation characteristic curves (AUC) and the number of AKI events among patients who had high risk scores were estimated.

RESULTS

The study population included 62,561 admissions followed by 1,212 AKI events (1.9 events/100 admissions). We constructed 5 risk models corresponding to the first 5 hospital days where patients were exposed to at least one nephrotoxic medication. Validated AUCs of the 5 models ranged from 0.78 to 0.81. Depending on risk model day, admissions ranked in the 90th percentile of the risk score captured between 43% to 49% of all AKI events.

CONCLUSION

A dynamic prediction model was built successfully for inpatient AKI with excellent discriminative validity and good calibration, allowing clinicians to focus on a select high-risk population that captures the majority of AKI events.

Early Postoperative Serum Creatinine Adjusted for Fluid Balance Precisely Predicts Subsequent Acute Kidney Injury After Cardiac Surgery

OBJECTIVES

Cumulative fluid overload may influence acute kidney injury (AKI) diagnosis due to the dilution effect. The authors hypothesized a small increase of early postoperative serum creatinine (SCr) adjusted for fluid balance might have superior discrimination ability in subsequent AKI prediction.

DESIGN

Retrospective analyses.

SETTING

A single-center study in a university hospital.

PARTICIPANTS

The study comprised 1,016 adult patients who underwent elective isolated or combined valve surgery in 2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Baseline characteristics, intraoperative parameters, and intraoperative and postoperative fluid balance were collected through a retrospective chart review. Early postoperative SCr level was drawn within 12 hours of surgical completion and then measured daily. Early relative changes of SCr were categorized as a cutoff value of 10% with or without adjustment for cumulative fluid balance. Kidney Disease: Improving Global Outcomes criteria were used to detect AKI. Logistic analyses were performed to determine risk factors for subsequent AKI with the inclusion of measured or fluid-adjusted early relative changes of SCr, respectively. In this study, 355 patients (34.9%) developed AKI. Multivariate logistic analyses showed age, weight, European System for Cardiac Operative Risk Evaluation II, and cardiopulmonary bypass duration were associated independently with the development of AKI. Model discrimination for AKI prediction was improved significantly when the addition of measured (area under the receiver operating characteristic curve [AUROC] 0.830) and fluid-adjusted early changes of SCr to the basic model (AUROC 0.850).

CONCLUSIONS

Early fluid-adjusted relative changes of SCr could improve the predictive ability for subsequent development of AKI in valve surgery patients.

Correlation of cardiopulmonary bypass duration with acute renal failure after cardiac surgery

OBJECTIVES

Prolonged cardiopulmonary bypass (CPB) is recognized as a risk factor for acute renal failure (ARF), but the dose effect of time on bypass is unknown. We therefore examined the risk of ARF associated with increasing CPB time stratified by preoperative renal function.

METHODS

A retrospective analysis was performed on 3889 patients undergoing cardiac surgery on CPB without circulatory arrest between 2011 and 2017 excluding those with a diagnosis of dialysis-dependent renal failure and those who had an intra-aortic balloon pump. Postoperative ARF was defined as a 3-fold increase in creatinine level, creatinine level > 4 mg/dL, or requirement for dialysis. A logistic regression model was built to identify predictors of ARF and to determine the probability of ARF.

RESULTS

Postoperative ARF occurred in 72 patients (2%) overall. Of 100 patients with an estimated glomerular filtration rate <30 mL/min/1.73 m², 22% developed ARF, of which 16 required dialysis. Thirty-day mortality was 31% for those with ARF compared with <1% for those without ARF (P < .01). Risk factors for ARF included obesity (odds ratio, 3.03; P < .01), increasing preoperative creatinine level (odds ratio, 4.21; P < .01), CPB time scaled by a factor of 10 minutes (odds ratio, 1.06; P = .04), and postoperative transfusion (odds ratio, 11.94; P < .01). The adjusted probability of ARF as a function of CPB time was determined and stratified by preoperative glomerular filtration rate.

CONCLUSIONS

Increasing CPB duration is associated with postoperative ARF, particularly among those with preoperative renal impairment. For patients with an estimated glomerular filtration rate <30 mL/min/1.73 m² the risk increases exponentially with time.

Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized logistic regression (with and without stability selection), and a recurrent neural network are trained on two years of adult inpatient EHR data (3,387 attributes for 34,505 patients who generated 90,013 training samples with 5,618 cases and 84,395 controls). Predictions are internally evaluated with 50 iterations of 5-fold grouped cross-validation with special emphasis on calibration, an analysis of which is performed at the patient as well as hospitalization level. Error is assessed with respect to diagnosis, race, age, gender, AKI identification method, and hospital utilization. In an additional experiment, the regularization penalty is severely increased to induce parsimony and interpretability. Predictors identified for rehospitalized patients are also reported with a special analysis of medications that might be modifiable risk factors. Insights from this study might be used to construct a predictive tool for AKI in rehospitalized patients. An accurate estimate of AKI risk at hospital entry might serve as a prior for an admitting provider or another predictive algorithm.

The Japanese clinical practice guideline for acute kidney injury 2016

Acute kidney injury (AKI) is a syndrome which has a broad range of etiologic factors depending on different clinical settings. Because AKI has significant impacts on prognosis in any clinical settings, early detection and intervention is necessary to improve the outcomes of AKI patients. This clinical guideline for AKI was developed by a multidisciplinary approach with nephrology, intensive care medicine, blood purification, and pediatrics. Of note, clinical practice for AKI management which was widely performed in Japan was also evaluated with comprehensive literature search.

Early postoperative change in serum creatinine predicts acute kidney injury after cardiothoracic surgery: a retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) is one of the most severe complications after cardiothoracic surgery (CTS). However, diagnosis of AKI by elevation of serum creatinine (SCr) misses a critical time period for prevention and treatment of AKI. We have observed that patients who develop AKI show a smaller SCr decrease after CTS than those without AKI. Hence, we hypothesized that the magnitude of the SCr change (ΔSCr) measured early after CTS can predict subsequent AKI.

METHODS

We conducted a retrospective analysis from January 2014 to December 2016 to examine the association of ΔSCr with AKI. ΔSCr was calculated as follows: (early postoperative SCr on intensive care unit [ICU] admission) - (preoperative SCr). Established risk factors and demographics were included in the multivariate-adjusted logistic regression model. AKI was defined by SCr criteria of the Kidney Disease: Improving Global Outcomes group.

RESULTS

Among 252 patients who underwent CTS, 69 developed AKI. The median ΔSCr was - 0.14 mg/dL (range - 0.96-0.45). Patients were divided into three groups based on ΔSCr: Group 1, ≤ - 0.2 mg/dL (n = 84); Group 2, > - 0.2 to < - 0.1 mg/dL (n = 76); and Group 3, ≥ - 0.1 mg/dL (n = 92). In the multivariate analysis, Group 3 had a significantly higher incidence of AKI than Group 1 (odds ratio, 7.34; 95% confidence interval 2.55-23.3). ΔSCr was an independent risk factor for AKI (odds ratio for every 0.1-mg/dL increase in ΔSCr, 1.55; 95% confidence interval 1.23-1.97).

CONCLUSIONS

A minor change in the SCr level early after CTS can predict subsequent AKI just after ICU admission.

The Japanese Clinical Practice Guideline for acute kidney injury 2016

Acute kidney injury (AKI) is a syndrome which has a broad range of etiologic factors depending on different clinical settings. Because AKI has significant impacts on prognosis in any clinical settings, early detection and intervention are necessary to improve the outcomes of AKI patients. This clinical guideline for AKI was developed by a multidisciplinary approach with nephrology, intensive care medicine, blood purification, and pediatrics. Of note, clinical practice for AKI management which was widely performed in Japan was also evaluated with comprehensive literature search.

Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery

OBJECTIVES

Iron regulation is an important modifier of renal ischemia-reperfusion injury, but the role of iron-binding proteins during cardiopulmonary bypass remains unclear. The goal was to characterize iron-binding proteins throughout ischemia-reperfusion injury to determine their association with acute kidney injury development.

METHODS

A prospective observational cohort of adult patients who underwent cardiac surgery (n = 301) was obtained, and acute kidney injury was defined by Kidney Disease Improving Global Outcomes. Serum ferritin, transferrin saturation, and urine hepcidin-25 were measured.

RESULTS

Intraoperative serum ferritin was lower at the start of cardiopulmonary bypass (P = .005) and 1-hour cardiopulmonary bypass (P = .001) in patients with acute kidney injury versus patients without acute kidney injury. Lower serum ferritin and higher transferrin saturation at 1-hour cardiopulmonary bypass were independent predictors of acute kidney injury (serum ferritin odds ratio, 0.66; 95% confidence interval [CI], 0.48-0.91; transferrin saturation odds ratio, 1.26; 95% CI, 1.02-1.55) and improved model discrimination (area under the curve [AUC], 0.76; 95% CI, 0.67-0.85) compared with clinical prediction alone (AUC, 0.72; 95% CI, 0.62-0.81; ΔAUC and net reclassification index, P = .01). Lower ferritin, higher transferrin saturation at 1-hour cardiopulmonary bypass, and lower urine hepcidin-25 at postoperative day 1 were also independent predictors for acute kidney injury development, and this model demonstrated an AUC of 0.80 (0.72-0.87), which was superior to clinical prediction (ΔAUC P = .002, integrated discrimination improvement and net reclassification index P = .003).

CONCLUSIONS

Our findings suggest that lower levels of intraoperative iron-binding proteins may reflect an impaired capacity to rapidly handle catalytic iron released during cardiopulmonary bypass, leading to kidney injury. These data highlight the importance of iron homeostasis in human ischemia-reperfusion injury and suggest it is a potentially modifiable risk during cardiac surgery. Intraoperative detection of incipient acute kidney injury may be feasible and could be used as an enrichment strategy for clinical trials.

The KDIGO acute kidney injury guidelines for cardiac surgery patients in critical care: a validation study

BACKGROUND

The Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury (AKI) guidelines assign the same stage of AKI to patients whether they fulfil urine output criteria, serum creatinine criteria or both criteria for that stage. This study explores the validity of the KDIGO guidelines as a tool to stratify the risk of adverse outcomes in cardiac surgery patients.

METHODS

Prospective data from consecutive adult patients admitted to the cardiac intensive care unit (CICU) following cardiac surgery between January 2013 and May 2015 were analysed. Patients were assigned to groups based on the criteria they met for each stage of AKI according to the KDIGO guidelines. Short and mid-term outcomes were compared between these groups.

RESULTS

A total of 2267 patients were included with 772 meeting criteria for AKI-1 and 222 meeting criteria for AKI-2. After multivariable adjustment, patients meeting both urine output and creatinine criteria for AKI-1 were more likely to experience prolonged CICU stay (OR 4.9, 95%CI 3.3-7.4, p < 0.01) and more likely to require renal replacement therapy (OR 10.5, 95%CI 5.5-21.9, p < 0.01) than those meeting only the AKI-1 urine output criterion. Patients meeting both urine output and creatinine criteria for AKI-1 were at an increased risk of mid-term mortality compared to those diagnosed with AKI-1 by urine output alone (HR 2.8, 95%CI 1.6-4.8, p < 0.01). Patients meeting both urine output and creatinine criteria for AKI-2 were more likely to experience prolonged CICU stay (OR 16.0, 95%CI 3.2-292.0, p < 0.01) or require RRT (OR 11.0, 95%CI 4.2-30.9, p < 0.01) than those meeting only the urine output criterion. Patients meeting both urine output and creatinine criteria for AKI-2 were at a significantly increased risk of mid-term mortality compared to those diagnosed with AKI-2 by urine output alone (HR 3.6, 95%CI 1.4-9.3, p < 0.01).

CONCLUSIONS

Patients diagnosed with the same stage of AKI by different KDIGO criteria following cardiac surgery have significantly different short and mid-term outcomes. The KDIGO criteria need to be revisited before they can be used to stratify reliably the severity of AKI in cardiac surgery patients. The utility of the criteria also needs to be explored in other settings.

Calibration drift in regression and machine learning models for acute kidney injury

Objective

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.

Materials and Methods

Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years.

Results

Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods.

Conclusions

Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

Risk model for deaths and renal replacement therapy dependence in patients with acute kidney injury after cardiac surgery

OBJECTIVES

Acute kidney injury (AKI) is a serious complication after cardiac surgery and is associated with increased in-hospital deaths. Renal replacement therapy (RRT) is becoming a routine strategy for severe AKI. Our goal was to evaluate the risk factors for death and RRT dependence in patients with AKI after cardiac surgery.

METHODS

We included 190 eligible adult patients who had AKI following cardiac surgery and who required RRT at our centre from November 2010 to March 2015. We collected preoperative, intraoperative, postoperative and RRT data for all patients.

RESULTS

In this cohort, 87 patients had successful RRT in the hospital, whereas 103 patients had RRT that failed (70 deaths and 33 cases of RRT dependence). The multivariable logistic analysis identified old age [odds ratio (OR): 1.042, 95% confidence interval (CI): 1.012-1.074; P = 0.011], serum uric acid (OR: 1.015, 95% CI: 1.003-1.031; P = 0.024), intraoperative concentrated red blood cell transfusions (OR: 1.144, 95% CI: 1.006-1.312; P = 0.041), postoperative low cardiac output syndrome (OR: 3.107, 95% CI: 1.179-8.190; P = 0.022) and multiple organ failure (OR: 5.786, 95% CI: 2.115-15.832; P = 0.001) as factors associated with a higher risk for RRT failure. The prediction model (-4.3 + 0.002 × preuric acid + 0.10 × concentrated red blood cells + 0.04 × age + 1.12 × [low cardiac output syndrome = 1] + 1.67 × [multiple organ failure = 1]) based on the multivariate analysis had statistically significant different incriminatory power with an area under the curve of 0.786.

CONCLUSIONS

The prediction model may serve as a simple, accurate tool for predicting in-hospital RRT failure for patients with AKI following cardiac surgery.