Prognostic Utility of KDIGO Urine Output Criteria After Cardiac Surgery

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.

Cardiac surgery-Associated acute kidney injury - A narrative review

Cardiac Surgery-Associated Acute Kidney Injury (CSA-AKI) is a serious complication seen in approximately 20-30% of cardiac surgery patients. The underlying pathophysiology is complex, often involving both patient- and procedure related risk factors. In contrast to AKI occurring after other types of major surgery, the use of cardiopulmonary bypass comprises both additional advantages and challenges, including non-pulsatile flow, targeted blood flow and pressure as well as the ability to manipulate central venous pressure (congestion). With an increasing focus on the impact of CSA-AKI on both short and long-term mortality, early identification and management of high-risk patients for CSA-AKI has evolved. The present narrative review gives an up-to-date summary on definition, diagnosis, underlying pathophysiology, monitoring and implications of CSA-AKI, including potential preventive interventions. The review will provide the reader with an in-depth understanding of how to identify, support and provide a more personalized and tailored perioperative management to avoid development of CSA-AKI.

Elevated postoperative systemic immune-inflammation index associates with acute kidney injury after cardiac surgery: a large-scale cohort study

Objective

To investigate whether postoperative systemic immune-inflammation index (SII) is associated with acute kidney injury (AKI) after cardiac surgery.

Methods

We included patients undergoing cardiac surgery from the Medical Information Mart for Intensive Care-Ⅳ database to conduct a retrospective cohort study. The outcomes are AKI, severe AKI, and 30-day mortality after cardiac surgery. Analytical techniques including receiver operating characteristic (ROC) analysis, restricted cubic splines (RCS), and multivariable logistic regression were used to assess the association between SII and outcomes. Sensitivity analyses using inverse probability of treatment weighting (IPTW) and the E-value were conducted to validate the stability of the results.

Results

3,799 subjects were included in this study. We used ROC to calculate an optimal cutoff value for predicting AKI after cardiac surgery, and subsequently patients were divided into two groups based on the cutoff value (Low SII: ≤ 949 × 109/L; High SII: > 949 × 109/L). ROC showed moderately good performance of SII for predicting AKI, while RCS also indicated a positive association between SII and AKI. The multivariate logistic analysis further affirmed the heightened risk of AKI in patients in the high SII group (OR, 5.33; 95%CI, 4.34-6.53; P < 0.001). Similar associations were observed between SII and severe AKI. Sensitivity and subgroup analyses indicated the robustness of the findings.

Conclusion

Elevated SII was independently associated with a higher risk of AKI in adults undergoing cardiac surgery. The potential causal relationship between postoperative SII and cardiac surgery associated AKI warrants prospective research.

Urine output is an early and strong predictor of acute kidney injury and associated mortality: a systematic literature review of 50 clinical studies

BACKGROUND

Although the present diagnosis of acute kidney injury (AKI) involves measurement of acute increases in serum creatinine (SC) and reduced urine output (UO), measurement of UO is underutilized for diagnosis of AKI in clinical practice. The purpose of this investigation was to conduct a systematic literature review of published studies that evaluate both UO and SC in the detection of AKI to better understand incidence, healthcare resource use, and mortality in relation to these diagnostic measures and how these outcomes may vary by population subtype.

METHODS

The systematic literature review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Data were extracted from comparative studies focused on the diagnostic accuracy of UO and SC, relevant clinical outcomes, and resource usage. Quality and validity were assessed using the National Institute for Health and Care Excellence (NICE) single technology appraisal quality checklist for randomized controlled trials and the Newcastle-Ottawa Quality Assessment Scale for observational studies.

RESULTS

A total of 1729 publications were screened, with 50 studies eligible for inclusion. A majority of studies (76%) used the Kidney Disease: Improving Global Outcomes (KDIGO) criteria to classify AKI and focused on the comparison of UO alone versus SC alone, while few studies analyzed a diagnosis of AKI based on the presence of both UO and SC, or the presence of at least one of UO or SC indicators. Of the included studies, 33% analyzed patients treated for cardiovascular diseases and 30% analyzed patients treated in a general intensive care unit. The use of UO criteria was more often associated with increased incidence of AKI (36%), than was the application of SC criteria (21%), which was consistent across the subgroup analyses performed. Furthermore, the use of UO criteria was associated with an earlier diagnosis of AKI (2.4-46.0 h). Both diagnostic modalities accurately predicted risk of AKI-related mortality.

CONCLUSIONS

Evidence suggests that the inclusion of UO criteria provides substantial diagnostic and prognostic value to the detection of AKI.

Development and validation of a digital biomarker predicting acute kidney injury following cardiac surgery on an hourly basis

Objectives

To develop and validate a digital biomarker for predicting the onset of acute kidney injury (AKI) on an hourly basis up to 24 hours in advance in the intensive care unit after cardiac surgery.

Methods

The study analyzed data from 6056 adult patients undergoing coronary artery bypass graft and/or valve surgery between April 1, 2012, and December 31, 2018 (development phase, training, and testing) and 3572 patients between January 1, 2019, and June 30, 2022 (validation phase). The study used 2 dynamic predictive modeling approaches, namely logistic regression and bootstrap aggregated regression trees machine (BARTm), to predict AKI. The mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values across all lead times before the occurrence of AKI were reported. The clinical practicality was assessed using calibration.

Results

Of all included patients, 8.45% and 16.66% had AKI in the development and validation phases, respectively. When applied to testing data, AKI was predicted with the mean AUC of 0.850 and 0.802 by BARTm and logistic regression, respectively. When applied to validation data, BARTm and LR resulted in a mean AUC of 0.844 and 0.786, respectively.

Conclusions

This study demonstrated the successful prediction of AKI on an hourly basis up to 24 hours in advance. The digital biomarkers developed and validated in this study have the potential to assist clinicians in optimizing treatment and implementing preventive strategies for patients at risk of developing AKI after cardiac surgery in the intensive care unit.

Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery

OBJECTIVE

Acute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification.

METHODS

Cardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy.

RESULTS

Within the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases.

CONCLUSIONS

Ensemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.

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.

Interleukin-10: A Potential Pre-Cannulation Marker for Development of Acute Kidney Injury in Patients Receiving Veno-Arterial Extracorporeal Membrane Oxygenation

INTRODUCTION

Acute kidney injury (AKI) in patients treated with veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is associated with high mortality. The objective of this study was to investigate whether cytokine levels before the initiation of ECMO treatment could predict AKI. We also aimed to investigate the impact of AKI on 30-day and 1-year mortality.

METHODS

Serum cytokine levels were analyzed in 100 consecutive VA-ECMO-treated patients at pre-cannulation, at 48 h post-cannulation, and at 8 days. Clinical data to establish the incidence and outcome of AKI after the start of ECMO was retrieved from the local ECMO registry.

SETTING

The study was conducted at tertiary care, university hospital. Participants included 100 patients treated with VA-ECMO.

INTERVENTIONS

The blood samples for cytokine analysis were collected before VA-ECMO treatment, at 48 h after VA-ECMO treatment was started, and at 8 days.

RESULTS

Pre-cannulation serum IL-10 levels were significantly higher in patients who developed AKI (212 [38.9, 620.7]) versus those who did not (49.0 [11.9, 102.2]; p = 0.007), and the development of AKI can be predicted by pre-cannulation IL-10 levels (p = 0.025, OR = 1.2 [1.02-1.32]). The development of AKI during ECMO treatment is associated with increased 30-day mortality (p = 0.049) compared to patients who did not develop AKI and had a pre-cannulation estimated glomerular filtration rate ≥ 45 mL/min. The 1-year survival rate for patients with AKI who survived the first 30 days of ECMO treatment is comparable to that of patients without AKI.

CONCLUSION

Increased pre-cannulation IL-10 levels are associated with the development of AKI during VA-ECMO support. AKI is associated with increased 30-day mortality compared to patients with no AKI and better renal function. However, patients with AKI who survive the first 30 days have a 1-year survival rate similar to those without AKI.

Association of intraoperative hypotension and postoperative acute kidney injury after adrenalectomy for pheochromocytoma: a retrospective cohort analysis

BACKGROUND

Perioperative acute kidney injury (AKI) has been one of the leading causes of morbidity and mortality for surgical patients. Pheochromocytoma is a rare, catecholamine-secreting neuroendocrine neoplasm characterized by typical long-term hypertension that needs surgical resection. Our objective was to determine whether intraoperative mean arterial pressures (MAPs) less than 65 mmHg are associated with postoperative AKI after elective adrenalectomy in patients with pheochromocytoma.

METHODS

We performed a retrospective review of patients undergoing adrenalectomy for pheochromocytoma between 1991 and 2019 at Peking Union Medical College Hospital, Beijing, China. Two intraoperative phases, before and after tumor resection, were recognized based on distinctly different hemodynamic characteristics. The authors evaluated the association between AKI and each blood pressure exposure in these two phases. The association between the time spent under different absolute and relative MAP thresholds and AKI was then evaluated adjusting for potential confounding variables.

RESULTS

We enrolled 560 cases with 48 patients who developed AKI postoperatively. The baseline and intraoperative characteristics were similar in both groups. Though time-weighted average MAP was not associated with postoperative AKI during the whole operation (OR 1.38; 95% CI, 0.95-2.00; P = 0.087) and before tumor resection phase (OR 0.83; 95% CI, 0.65-1.05; P = 0.12), both time-weighted MAP and time-weighted percentage changes from baseline were strongly associated with postoperative AKI after tumor resection, with OR 3.50, 95% CI (2.25, 5.46) and 2.03, 95% CI (1.56, 2.66) in the univariable logistic analysis respectively, and with OR 2.36, 95% CI (1.46, 3.80) and 1.63, 95% CI (1.23, 2.17) after adjusting sex, surgical type (open vs. laparoscopic) and estimated blood loss in the multiple logistic analysis. At any thresholds of MAP less than 85, 80, 75, 70, and 65 mmHg, prolonged exposure was associated with increased odds of AKI.

CONCLUSIONS

We found a significant association between hypotension and postoperative AKI in patients with pheochromocytoma undergoing adrenalectomy in the period after tumor resection. Optimizing hemodynamics, especially blood pressure after the adrenal vessel ligation and tumor is resected, is crucial for the prevention of postoperative AKI in patient with pheochromocytoma, which could be different from general populations.

The impact of urine flow on urine oxygen partial pressure monitoring during cardiac surgery

PURPOSE

Urine oxygen partial pressure (PuO₂) may be useful for assessing acute kidney injury (AKI) risk. The primary purpose of this study was to quantify the ability of a novel urinary oxygen monitoring system to make real-time PuO₂ measurements intraoperatively which depends on adequate urine flow. We hypothesized that PuO₂ data could be acquired with enough temporal resolution to provide real-time information in both AKI and non-AKI patients.

METHODS

PuO₂ and urine flow were analyzed in 86 cardiac surgery patients. PuO₂ data associated with low (< 0.5 ml/kg/hr) or retrograde urine flow were discarded. Patients were excluded if > 70% of their data were discarded during the respective periods, i.e., during cardiopulmonary bypass (CPB), before CPB (pre-CPB), and after CPB (post-CPB). The length of intervals of discarded data were recorded for each patient. The median length of intervals of discarded data were compared between AKI and non-AKI patients and between surgical periods.

RESULTS

There were more valid PuO₂ data in CPB and post-CPB periods compared to the pre-CPB period (81% and 90% vs. 31% of patients included, respectively; p < 0.001 and p < 0.001). Most intervals of discarded data were < 3 minutes during CPB (96%) and post-CPB (98%). The median length was < 25 s during all periods and there was no significant difference in the group median length of discarded data intervals for AKI and non-AKI patients.

CONCLUSIONS

PuO₂ measurements were acquired with enough temporal resolution to demonstrate real-time PuO₂ monitoring during CPB and the post-CPB period.

CLINICALTRIALS

GOV IDENTIFIER

NCT03335865, First Posted Date: Nov. 8th, 2017.

Incidence, predictors and vascular sequelae of distal limb ischemia in minimally invasive cardiac surgery with femoral artery cannulation: an observational cohort study

Literature regarding monitoring and consequences of distal limb ischemia due to femoral artery cannulation for Minimally Invasive Cardiac Surgery (MICS) remains limited. The primary objective was to determine its incidence, defined as a ≥ 15% difference in regional Oxygen Saturation (rSO₂) lasting ≥ four consecutive minutes between the cannulated and non-cannulated limb. The secondary objectives included: determination of distal limb ischemia, defined as a Tissue Oxygenation Index (TOI) < 50% in the cannulated limb, identification of predictors for distal limb ischemia, determination of a possible association of NIRS-diagnosed ischemia with acute kidney injury, and the need for vascular surgery up to six months after cardiac surgery. A prospective, observational cohort study with blinded rSO₂-measurements to prevent intraoperative clinical decision-making. A single-center, community-hospital, clinical study. All consecutive patients ≥ 18 years old, and scheduled for predefined MICS. Patients underwent MICS with bilateral calf muscle rSO₂-measurements conducted by Near-Infrared Spectroscopy (NIRS). In total 75/280 patients (26.79%) experienced distal limb ischemia according to the primary objective, while 18/280 patients (6.42%) experienced distal limb ischemia according to the secondary objective. Multivariate logistic regression showed younger age to be an independent predictor for distal limb ischemia (p = 0.003). None of the patients who suffered intraoperative ischemia required vascular surgery within the follow-up period. The incidence of NIRS-diagnosed ischemia varied from 6.4% to 26.8% depending on the used criteria. Short and long-term vascular sequelae, however, are limited and not intraoperative ischemia related. The added value of intraoperative distal limb NIRS monitoring for vascular reasons seems limited. Future research on femoral artery cannulation in MICS should shift focus to other outcome parameters such as acute kidney injury, postoperative pain or paresthesias.