Early rise in postoperative creatinine for identification of acute kidney injury after cardiac surgery

Association between Changes in Preoperative Serum Creatinine and Acute Kidney Injury after Cardiac Surgery: A Retrospective Cohort Study

INTRODUCTION

Limited information exists regarding the impact of preoperative serum creatinine changes on cardiac surgery-associated acute kidney injury (CSA-AKI). This study aimed to investigate the development of AKI in patients with a baseline estimated glomerular filtration rate of ≥60 mL/min/1.73 m2 who present with an elevation in preoperative serum creatinine.

METHODS

This retrospective cohort study assessed patients who underwent open-heart surgery. Preoperative serum creatinine change was calculated as the ratio of the maximum preoperative serum creatinine value to the baseline creatinine (MCR). Patients were categorized into three groups based on MCR: non-elevation (≤1.0), mild elevation (1.0 to 1.5), and pronounced elevation (≥1.5). Multivariable logistic regression was used to estimate the risk of AKI, severe AKI, and non-recovery from AKI.

RESULTS

There were significant increases in the odds of AKI (adjusted odds ratio [OR], 1.42; 95% confidence interval [CI], 1.29-1.57; per 0.1 increase in MCR), severe AKI (adjusted OR, 1.28; 95% CI, 1.15-1.41), and AKI non-recovery (adjusted OR, 1.29; 95% CI, 1.16-1.43). Pronounced elevation in preoperative serum creatinine was associated with a higher risk of AKI (adjusted OR, 15.45; 95% CI, 6.63-36.00), severe AKI (adjusted OR, 3.62; 95% CI, 1.20-10.87), and AKI non-recovery (adjusted OR, 4.74; 95% CI, 1.63-13.89) than non-elevation. Mild elevation in preoperative serum creatinine was also significantly associated with AKI (adjusted OR, 3.76; 95% CI, 1.92-7.37).

CONCLUSIONS

Elevation in preoperative serum creatinine from baseline was associated with an increased risk of AKI; even mild elevation significantly increased the risk of AKI.

Artificial intelligence-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 using artificial intelligence-based algorithms.

METHODS

Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modeling. Cardiac surgery-associated acute kidney injury was defined according to the Kidney Disease Improving Global Outcomes criteria. Different machine learning algorithms were trained and validated to detect cardiac surgery-associated acute kidney injury within 12 hours after surgery. Demographic characteristics, comorbidities, preoperative cardiac status, intra- and postoperative variables including creatinine and hemoglobin values were retrieved for analysis.

RESULTS

From 7507 patients analyzed, 1699 patients (22.6%) developed cardiac surgery-associated acute kidney injury. The ultimate detection model, 'Detect-A(K)I', recognizes cardiac surgery-associated acute kidney injury within 12 hours with an area under the curve of 88.0%, sensitivity of 78.0%, specificity of 78.9%, and accuracy of 82.1%. The optimal parameter set includes serial changes of creatinine and hemoglobin, operative emergency, bleeding-associated variables, cardiac ischaemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease.

CONCLUSIONS

The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 hours after surgery with the best discriminatory characteristics reported so far.

Postoperative Serum Creatinine Serves as a Prognostic Predictor of Cardiac Surgery Patients

Background

Serum creatinine, an important diagnostic indicator for acute kidney injury (AKI), was considered to be a risk factor for cardiovascular disease. This study aimed to investigate the significance of postoperative serum creatinine in predicting the prognosis of cardiac surgery patients.

Methods

The Medical Information Mart for Intensive Care III (MIMIC-III) database was used to extract the clinical data. Adult (≥18 years) cardiac surgery patients in the database were enrolled. The correlation of postoperative serum creatinine with lengths of intensive care unit (ICU) stay was analyzed with Spearman correlation, and the association of postoperative serum creatinine with hospital mortality was analyzed with chi-square tests. Multivariable logistic regression was used to identify postoperative serum creatinine as an independent prognostic factor for hospital mortality.

Results

A total of 6,001 patients were enrolled in our study, among whom, 108 patients (1.8%) died in the hospital. Non-survivors had much higher postoperative serum creatinine levels (initial: 0.8 vs. 1.2 mg/dl, P < 0.001; maximum: 1.1 vs. 2.8 mg/dl, P < 0.001; minimum: 0.8 vs.1.1 mg/dl, P < 0.001). Positive correlations were observed between postoperative serum creatinine (P < 0.001) and lengths of ICU stay. For all models, postoperative initial creatinine, postoperative maximum creatinine, and postoperative minimum creatinine were all positively associated with hospital mortality (all P < 0.001). The predictive performance of postoperative serum creatinine was moderately good (area under the curve (AUC) for initial creatinine = 0.7583; AUC for maximum creatinine = 0.8413; AUC for minimum creatinine = 0.7063).

Conclusions

This study demonstrated the potential to use postcardiac surgery serum creatinine as an outcome indicator.

Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury

Background

Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics‐based biomarkers in patients with CSA‐AKI.

Methods and Results

A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time‐dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA‐AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879–0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time‐dependent manner (R², 0.480–0.945). Use of this AKI predictive model was then confirmed in the validation cohort (area under curve, 0.972; 95% CI, 0.947–0.996). The predictive model remained robust when tested in a subset of patients with early‐stage AKI in the validation cohort (area under curve, 0.943; 95% CI, 0.883–1.000).

Conclusions

High‐resolution metabolomics is sufficiently powerful for developing novel biomarkers. Plasma levels of 3 metabolites were useful for the early identification of CSA‐AKI.

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.

Acute Kidney Injury following Cardiopulmonary Bypass: A Challenging Picture

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

Point-of-care creatinine measurements to predict acute kidney injury

BACKGROUND

Plasma creatinine (Cr) is a marker of kidney function and typically measured once daily. We hypothesized that Cr measured by point-of-care technology early after ICU admission would be a good predictor of acute kidney injury (AKI) the next day in critically ill patients.

METHODS

We conducted a retrospective database audit in a single tertiary ICU database. We included patients with normal first admission Cr (Cr_F ) and identified a Cr value (Cr_P ) obtained within 6-12 hours from ICU admission. We used their difference converted into percentage (delta-Cr-%) to predict subsequent AKI (based on Cr and/or need for renal replacement therapy) the next day. We assessed predictive value by calculating area under the receiver characteristic curve (AUC), logistic regression models for AKI with and without delta-Cr-%, and the category-free net reclassifying index (cfNRI).

RESULTS

We studied 780 patients. Overall, 70 (9.0%) fulfilled the Cr AKI definition by Cr_P measurement. On day 2, 148 patients (19.0%) were diagnosed with AKI. AUC (95% CI) for delta-Cr-% to predict AKI on day 2 was 0.82 (95% CI 0.78-0.86), and 0.74 (95% CI 0.69-0.80) when patients with AKI based on the Cr_P were excluded. Using a cut-off of 17% increment, the positive likelihood ratio (95% CI) for delta-Cr-% to predict AKI was 3.5 (2.9-4.2). The cfNRI was 90.0 (74.9-106.1).

CONCLUSIONS

Among patients admitted with normal Cr, early changes in Cr help predict AKI the following day.

Volatile sedation in the intensive care unit: A systematic review and meta-analysis

BACKGROUND

Volatile sedation in the intensive care unit (ICU) may reduce the number of adverse events and improve patient outcomes compared with intravenous (IV) sedation. We performed a systematic review and meta-analysis comparing the effects of volatile and IV sedation in adult ICU patients.

METHODS

We searched the PubMed, Embase, Cochrane Central Register, and Web of Science databases for all randomized trials comparing volatile sedation using an anesthetic-conserving device (ACD) with IV sedation in terms of awakening and extubation times, lengths of ICU and hospital stay, and pharmacologic end-organ effects.

RESULTS

Thirteen trials with a total of 1027 patients were included. Volatile sedation (sevoflurane or isoflurane) administered through an ACD shortened the awakening time [mean difference (MD), -80.0 minutes; 95% confidence intervals (95% CIs), -134.5 to -25.6; P = .004] and extubation time (MD, -196.0 minutes; 95% CIs, -305.2 to -86.8; P < .001) compared with IV sedation (midazolam or propofol). No differences in the lengths of ICU and hospital stay were noted between the 2 groups. In the analysis of cardiac effects of sedation from 5 studies, patients who received volatile sedation showed lower serum troponin levels 6 hours after ICU admission than patients who received IV sedation (P < .05). The effect size of troponin was largest between 12 and 24 hours after ICU admission (MD, -0.27 μg/L; 95% CIs, -0.44 to -0.09; P = .003).

CONCLUSION

Compared with IV sedation, volatile sedation administered through an ACD in the ICU shortened the awakening and extubation times. Considering the difference in serum troponin levels between both arms, volatile anesthetics might have a myocardial protective effect after cardiac surgery even at a subanesthetic dose. Because the included studies used small sample sizes with high heterogeneity, further large, high-quality prospective clinical trials are needed to confirm our findings.