The impact of major perioperative renal insult on long-term renal function and survival after cardiac surgery

Continuous Urine Output-Based Alert Identifies Cardiac Surgery-associated Acute Kidney Injury Earlier Than Serum Creatinine: A Prospective and Retrospective Observational Study

OBJECTIVE(S)

Acute kidney injury (AKI) is defined and staged by reduced urine output (UO) and increased serum creatinine (SCr). UO is typically measured manually and documented in the electronic health record, making early and reliable detection of oliguria-based AKI and electronic data extraction challenging. The authors investigated the diagnostic performance of continuous UO, enabled by active drain line clearance-based alerts (Accuryn AKI Alert), compared with AKI stage 2 SCr criteria and their associations with length of stay, need for continuous renal replacement therapy, and 30-day mortality.

DESIGN

This study was a prospective and retrospective observational study.

SETTING

Nine tertiary centers participated.

PARTICIPANTS

Cardiac surgery patients were enrolled.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 522 patients were analyzed. AKI stages 1, 2, and 3 were diagnosed in 32.18%, 30.46%, and 3.64% of patients based on UO, compared with 33.72%, 4.60%, and 3.26% of patients using SCr, respectively. Continuous UO-based alerts diagnosed stage ≥1 AKI 33.6 (IQR =15.43, 95.68) hours before stage ≥2 identified by SCr criteria. A SCr-based diagnosis of AKI stage ≥2 has been designated a Hospital Harm by the Centers for Medicare & Medicaid Services. Using this criterion as a benchmark, AKI alerts had a discriminative power of 0.78. The AKI Alert for stage 1 was significantly associated with increased intensive care unit and hospital length of stay and continuous renal replacement therapy, and stage ≥2 alerts were associated with mortality.

CONCLUSIONS

AKI Alert, based on continuous UO and enabled by active drain line clearance, detected AKI stages 1 and 2 before SCr criteria. Early AKI detection allows for early kidney optimization, potentially improving patient outcomes.

Optimization of kidney function in cardiac surgery patients with intra-abdominal hypertension: expert opinion

Cardiac surgery-associated acute kidney injury (CSA-AKI) affects up to 42% of cardiac surgery patients. CSA-AKI is multifactorial, with low abdominal perfusion pressure often overlooked. Abdominal perfusion pressure is calculated as mean arterial pressure minus intra-abdominal pressure (IAP). IAH decreases cardiac output and compresses the renal vasculature and renal parenchyma. Recent studies have highlighted the frequent occurrence of IAH in cardiac surgery patients and have linked the role of low perfusion pressure to the occurrence of AKI. This review and expert opinion illustrate current evidence on the pathophysiology, diagnosis, and therapy of IAH and ACS in the context of AKI.

Cardiac surgery-associated acute kidney injury and perioperative plasma viscosity: is there a relationship?

Plasma viscosity (PV) is a key factor in microcirculatory flow resistance and capillary perfusion during hemodilution, we hypothesized a possible relationship between cardiac surgery-associated acute kidney injury (CSA-AKI) and PV. We conducted a prospective, observational, single-center study on 50 adult cardiac surgery patients with cardiopulmonary bypass (age 64 years, male sex 80%, baseline serum creatinine 1.04 mg/dL). We assessed perioperative characteristics, management, short-term outcomes, blood analysis, PV, serum creatinine, and diuresis. CSA-AKI was identified using KDIGO criteria. Data were collected at 10 time points during the first perioperative week. CSA-AKI occurred in 17 patients (34%): 12 (24%) stage 1, 1 (2%) stage 2, and 4 (8%) stage 3. Most patients (88%) developed CSA-AKI within 48 h post-surgery. Patients with CSA-AKI had higher body mass index (BMI), more frequent chronic kidney disease (CKD), and lower hemoglobin and hematocrit levels. The median baseline PV for the entire cohort was 1.50 cP on EDTA and 1.37 cP on citrate. No significant differences in PV levels were found between patients with CSA-AKI and normal kidney function, both at baseline and at the 48-h. Logistic and Cox regression analyses showed no significant relationship between PV and CSA-AKI. However, CSA-AKI was related to increased BMI, lower hemoglobin and hematocrit levels, and pre-existing CKD. The present study found no significant association between PV and CSA-AKI. Nevertheless, more research is needed to validate this finding and to investigate the role of PV in other clinical settings.

Acute kidney injury in neurocritical patients: a retrospective cohort study

BACKGROUND /OBJECTIVE

Acute kidney injury (AKI) is a significant complication in critical care units (CCU). Non-neurological complications such as AKI are an independent predictor of poor clinical outcomes, with an increase in morbidity and mortality, financial costs, and worse functional recovery. This work aims to estimate the incidence of AKI and evaluate the risk factors and complications of AKI in neurocritical patients hospitalized in the CCU.

METHODS

A retrospective cohort study was conducted. Patients admitted to the neurocritical care unit between 2016 and 2018 with a stay longer than 48 h were retrospectively analyzed in regard to the incidence, risk factors, and outcomes of AKI.

RESULTS

The study population comprised 213 neurocritical patients. The incidence of AKI was 23.5%, with 58% KDIGO 1 and 2% requiring renal replacement therapy. AKI was an independent predictor of prolonged use of mechanical ventilation, cerebral edema, and mortality. Cerebral edema [OR 4.40 (95% CI 1.98-9.75) p < 0.001] and a change in chloride levels greater than 4 mmol/L at 48 h (OR 2.44 (95% CI 1.10-5.37) p = 0.027) were risk factors for developing AKI in the first 14 days of hospitalization.

CONCLUSION

There is a high incidence of AKI in neurocritical patients; it is associated with worse clinical outcomes regardless of the CCU admission etiology or AKI severity.

Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review

Acute kidney injury (AKI) is a common and serious complication of cardiac surgery and is associated with increased mortality and morbidity, accompanied by a substantial economic burden. The pathogenesis of cardiac surgery-associated acute kidney injury (CSA-AKI) is multifactorial and complex, with a variety of pathophysiological theories. In addition to the existing diagnostic criteria, the exploration and validation of biomarkers is the focus of research in the field of CSA-AKI diagnosis. Prevention remains the key to the management of CSA-AKI, and common strategies include maintenance of renal perfusion, individualized blood pressure targets, balanced fluid management, goal-directed oxygen delivery, and avoidance of nephrotoxins. This article reviews the pathogenesis, definition and diagnosis, and pharmacological and nonpharmacological prevention strategies of AKI in cardiac surgical patients.

Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery

Background

Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population.

Methods

Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score.

Results

A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score.

Conclusion

Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery.

Clinical trial registration

Trial registration: Clinicaltrials.gov, NCT04237636.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a 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.

Optimal timing of renal replacement therapy for favourable outcome in patients of acute renal failure following cardiac surgery

Objectives

Acute renal failure is a serious complication following cardiac surgery. This may lead to fatal outcome if not treated timely. Continuous renal replacement therapy (RRT) has shown improvement in outcome. There is no clear consensus on the timing of the initiation of RRT in these patients. This study evaluates the factors predicting favourable outcome in this group of patients.

Methods

Patients undergoing cardiac surgery between January 2015 and December 2018 are included in this retrospective study. RRT is required in 24 patients out of 2254 operated during this period. Patients are divided into groups, survivors (group 1, n = 8) and dead (group 2, n = 16). The preoperative information is accessed from the hospital information system and intensive care unit data. Multivariate analysis of pre continuous renal replacement therapy (CRRT) bicarbonate level, pH, potassium, time of initiating CRRT and central venous pressure is performed.

Results

The incidence of acute renal failure requiring RRT is 1.06%. Patients in two groups were similar in demographics and presence of risk factors. There was difference in the pre RRT bicarbonate level (p = 0.007). On multivariate analysis, pre RRT bicarbonate levels predict survival (p = 0.003). ROC curve for pre RRT bicarbonate predicts survival for value above 16.83 mg/dl with 80% sensitivity and 78.6% specificity.

Conclusion

Bicarbonate level in blood predicts the best evidence for initiating the renal replacement therapy in of acute renal failure following cardiac surgery. When urine output drops to < 0.5 ml/kg and not responding to infusion of furosemide, RRT must be initiated at sodium bicarbonate in blood above 16.9 mg%.

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

Acute Kidney Injury after Cardiac Surgery in Patients Without Chronic Kidney Disease

Introduction

Among patients undergoing cardiac surgery, the occurrence of acute renal injury appears to be associated with worse prognosis and increased mortality. The objective of this study was to evaluate risk factors and the impact this complication on mortality and survival after cardiac surgery among patients without chronic kidney disease.

Methods

In this retrospective study, we reviewed the medical records of 142 patients who underwent elective coronary artery bypass grafting, valve replacement (single or multiple), or both (simultaneously) at a tertiary care hospital.

Results

Among the 142 patients evaluated, the mean age was 58.28±13.87 years and 80 (56.33%) were female. The postoperative incidence of acute renal injury was 43.66%. Univariate analysis between the groups with and without acute renal injury revealed no significant differences, whereas multivariate analysis showed that risk factors for acute renal injury included valve replacement (OR=4.7, P=0.002, 95% CI=1.76-12.62, age (OR=1.044, P=0.012, 95% CI=1.01-1.07), previous cardiac surgery (OR=36.1, P=0.015, 95% CI=1.99-653.85), postoperative use of the vasoactive drug norepinephrine (OR=3.32, P=0.013, 95% CI=1.29-8.58) and dobutamine (OR=5.3, P=0.019, 95% CI=1.32-21.64). In our sample, there were 30 deaths, of which 25 had acute kidney injury. Survival was also lower among the patients with this complication, especially those who had required hemodialysis (OR=2.60, P<0.001, 95% CI=1.01-6.70) or had previously undergone cardiac surgery (OR=3.68, P<0.001, 95% CI=1.09-12.37).

Conclusion

Our findings underscore the importance of identifying risk factors for developing acute renal injury after cardiac surgery, which can further the development of effective renoprotective strategies.

Postoperative acute kidney injury defined by RIFLE criteria predicts early health outcome and long-term survival in patients undergoing redo coronary artery bypass graft surgery

Objective

To investigate the impact of postoperative acute kidney injury (AKI) on early health outcome and on long-term survival in patients undergoing redo coronary artery bypass grafting (CABG).

Methods

We performed a Cox analysis with 398 consecutive patients undergoing redo CABG over a median follow-up of 7 years (interquartile range, 4-12.2 years). Renal function was assessed using baseline and peak postoperative levels of serum creatinine. AKI was defined according to the risk, injury, failure, loss, and end-stage (RIFLE) criteria. Health outcome measures included the rate of in-hospital AKI and all-cause 30-day and long-term mortality, using data from the United Kingdom's Office of National Statistics. Propensity score matching, as well as logistic regression analyses, were used. The impact of postoperative AKI at different time points was related to survival.

Results

In patients with redo CABG, the occurrence of postoperative AKI was associated with in-hospital mortality (odds ratio [OR], 3.74; 95% confidence interval [CI], −1.3 to 10.5; P < .01], high Euroscore (OR, 1.27; 95% CI, 1.07-1.52; P < .01), use of IABP (OR, 6.9; 95% CI, 2.24-20.3; P < .01), and reduced long-term survival (hazard ratio [HR], 2.42; 95% CI, 1.63-3.6; P = .01). Overall survival at 5 and 10 years was lower in AKI patients with AKI compared with those without AKI (64% vs 85% at 5 years; 51% vs 68% at 10 years). On 1:1 propensity score matching analysis, postoperative AKI was independently associated with reduced long term survival (HR, 2.8; 95% CI, 1.15-6.7).

Conclusions

In patients undergoing redo CABG, the occurrence of postoperative AKI is associated with increased 30-day mortality and major complications and with reduced long-term survival.

Acute Kidney Injury in Cardiac Surgery and Cardiac Intensive Care

Acute kidney injury (AKI) is a serious postoperative complication following cardiac surgery. Despite the incidence of AKI requiring temporary renal replacement therapy being low, it is nonetheless associated with high morbidity and mortality. Therefore, preventing AKI associated with cardiac surgery can dramatically improve outcomes in these patients. The pathogenesis of AKI is multifactorial and many attempts to prevent or treat renal injury have been met with limited success. In this article, we will discuss the incidence and risk factors for cardiac surgery associated AKI, including the pathophysiology, potential biomarkers of injury, and treatment modalities.

Long-Term Risk of Progressive Chronic Kidney Disease in Patients with Severe Acute Kidney Injury Requiring Dialysis after Coronary Artery Bypass Surgery

AIM

Few studies have evaluated patients after cardiac surgery for subsequent chronic kidney disease (CKD) which increases cardiovascular morbidity and mortality. This study aimed to ascertain the long-term renal outcome in adult patients with severe acute kidney injury (AKI) after coronary artery bypass graft (CABG) surgery.

METHODS

This is a single-center retrospective cohort study of consecutive adult patients who received acute dialysis for AKI after CABG between February 8, 2009 and January 30, 2011. Data on pre- and intra-operative factors were retrieved from electronic medical records. The primary endpoint was CKD progression as defined by dialysis dependence or doubling of serum creatinine from the pre-operative level. Secondary endpoints included in-hospital mortality and renal function at 3 months and 1 year.

RESULTS

Fifty-five patients required acute dialysis after CABG. The median age was 67 years (IQR: 61, 75), and 70.9% were male. Median pre-operative serum creatinine was 157 µmol/l (IQR: 122, 203). A total of 19 patients (34.5%) died. The median follow-up time for hospital survivors was 44.2 months (IQR: 25.0, 49.4) after surgery. Among the 36 survivors, 14 patients (38.9%) reached the primary endpoint. Patients with CKD progression had higher pre-operative serum creatinine [median 214 µmol/l (IQR: 159, 399) vs. 155 µmol/l (112, 187), p = 0.015] and lower eGFR [median 20.4 ml/min/1.73 m(2) (IQR: 11.9, 38.2) vs. 39.9 ml/min/1.73 m(2) (25.9, 55.5), p = 0.027] compared to those who did not have CKD progression.

CONCLUSION

Patients with severe AKI after CABG are at high risk of long-term renal dysfunction and should be monitored regularly for deterioration.

Renal replacement therapy after cardiac surgery; renal function recovers

OBJECTIVES

To assess renal outcome in patients discharged from hospital following cardiac surgery-associated acute kidney injury (CSA-AKI) with need for renal replacement therapy.

DESIGN

In April 2012 we conducted a cross-sectional study of patients treated with renal replacement therapy following cardiac surgery during 2008-2010. We included all adult patients with a pre-operative serum creatinine (sCr) < 200 μM, surviving to discharge. Primary endpoint was use of renal replacement therapy after hospital discharge; secondary endpoint was a sCr > 200 μM at the time of follow-up.

RESULTS

We reviewed the records of 3828 patients receiving cardiac surgery in the defined period. A total of 107 adult patients with sCr concentrations < 200 μM were treated with post-operative renal replacement therapy of whom 70 survived to discharge. Fifty-six patients were alive at follow-up and none had required renal replacement therapy after initial discharge. Median sCr concentration at follow-up was 111 [56-257] μM and two patients had sCr above 200 μM.

CONCLUSIONS

In this study, renal function recovered in patients discharged from hospital following renal replacement therapy after CSA-AKI. No patients needed further renal replacement therapy and only two (4%) had a sCr > 200 μM at follow-up.