Predictive value of neutrophil to lymphocyte ratio on acute kidney injury after on-pump coronary artery bypass: a retrospective, single-center study

Clinical significance of neutrophil-to-lymphocyte ratio on the risk of abdominal aortic calcification and decreased bone mineral density in patients with end-stage kidney disease

Inflammation plays a major role in the pathogenesis of chronic kidney disease (CKD), but the relationship between systemic inflammation and CKD-mineral bone disease is unclear. We aimed to investigate whether the neutrophil-to-lymphocyte ratio (NLR) is related to abdominal aortic calcification (AAC) and bone mineral density (BMD) in dialysis patients. In this cross-sectional analysis using baseline data of a multicenter cohort, a total of 759 patients were divided into three groups according to NLR level, and the associations between NLR and Kauppila AAC score (AACS) and BMD were assessed. The highest tertile NLR group had more males, alcohol consumers, higher diabetes prevalence, and higher comorbidity index than the lowest tertile NLR group. Fasting glucose and C-reactive protein levels were higher, while serum albumin, serum iron, and lipid profiles except triglycerides were lower in the highest tertile group. AACS was significantly higher in the highest tertile group than in the lowest and middle tertile groups (p = 0.017), but the mean areal BMD and T-score of the lumbar spine and femur were not different between groups. NLR level was positively correlated with AACS in all aortic wall segments except L1 and L3 anterior. In multivariable logistic regression analysis, the highest tertile NLR group was independently associated with AAC (odds ratio 2.876, 95% confidence interval 1.250-6.619, p = 0.013) but was not associated with osteoporosis in the lumbar spine and femur after adjusting for confounding factors. The NLR can be used as a potential indicator of AAC in dialysis patients.

Association of preoperative neutrophil-lymphocyte ratio with acute kidney injury in patients with non-cardiac surgery: difference among surgical types

PURPOSE

To examine the relationship between Neutrophil-Lymphocyte Ratio (NLR) and Acute Kidney Injury (AKI) in patients undergoing noncardiac surgery, and subgroup analysis was performed for different types of non-cardiac surgery.

METHODS

The present retrospective cohort study included 10,159 adult patients who underwent major noncardiac surgery at Nanfang Hospital, Southern Medical University, between 2008 and 2018. Postoperative AKI was defined as an increase in serum creatinine level of at least 0.3 mg/dl within 48 h, or 1.5 times higher than baseline within 7 days postoperatively according to the Kidney Disease Improving Global Outcome. The correlation between preoperative NLR and postoperative AKI was determined by stepwise multivariate logistic regression analysis, and the predictive value of NLR was evaluated by the receiver operating characteristics curve (ROC) analysis.

RESULTS

Four hundred and eighty-five (4.77%) patients developed AKI postoperatively. Preoperative NLR was independently associated with postoperative AKI in all patients undergoing non-cardiac surgery (Odds ratio [OR], 1.03; 95% confidence interval [CI], 1.00-1.06). The optimal cut-off value of NLR was 2.12 according ROC analysis. The OR and 95% CI of AKI for NLR > 2.12 was 1.48 (1.21-1.81) compared with NLR ≤ 2.12. In addition, the positive association was mainly shown in patients undergone digestive system surgery with a cut-off value of 2.12 but not in neurological and musculoskeletal system surgeries.

CONCLUSION

The present study confirmed the association of preoperative NLR with postoperative AKI in digestive system surgical patients. A NLR value of 2.12 may be a useful cut-off to evaluate the risk of AKI.

Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models

OBJECTIVE

To explore the correlation between neutrophil-to-lymphocyte ratio (NLR) and contrast-induced acute kidney injury (CI-AKI). To develop machine-learning (ML) methods based on NLR and other relevant high-risk factors to establish new and effective predictive models of CI-AKI. Methods: The data of 2230 patients, who underwent elective vascular intervention, coronary angiography and percutaneous coronary intervention were retrospectively collected. The patients were divided into a CI-AKI group and a non-CI-AKI group. Logistic regression was used to analyze the correlation of NLR with CI-AKI and high-risk factors for CI-AKI, and logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and naïve Bayes (NB) models based on NLR and the high-risk factors were established.

RESULTS

A high NLR(>2.844) was an independent risk factor for CI-AKI (odds ratio = 2.304, p < 0.001). The area under the ROC curve (AUC) of the NB model was the largest (0.774), indicating that it had the best performance. NLR, serum creatinine concentration, fasting plasma glucose concentration, and use of β-blocker all accounted for a large proportion of the predictive performance of each model and were the four most important factors affecting the occurrence of CI-AKI.

CONCLUSIONS

There was a significant correlation between NLR and CI-AKI The NB model exhibited the best predictive performance out of the five ML models based on NLR exhibited the best predictive performance out of the five ML models.

Syndecan-1 as a marker to predict acute kidney injury after isolated coronary artery bypass graft operations

OBJECTIVE

Postoperative acute kidney injury is an important problem that can occur after coronary artery bypass graft operations, and it is important to identify risky patient groups preoperatively. This study aimed to investigate the importance of preoperative syndecan-1 levels in predicting acute kidney injury after elective coronary artery bypass graft operations accompanied by cardiopulmonary bypass.

METHODS

Patients who underwent coronary artery bypass graft operation in our clinic between March 1 and May 10, 2022, were included in this prospective study. Patients who did not develop acute kidney injury in the postoperative period were recorded as group 1 and patients who developed it were recorded as group 2.

RESULTS

A total of 79 patients undergoing coronary artery bypass graft surgery with cardiopulmonary bypass were included in the study. There were 55 patients in group 1 and 24 patients in group 2. There was no difference between the groups in terms of age, gender, diabetes mellitus, body mass index, smoking, and hyperlipidemia rates. In multivariate logistic regression analysis, increased blood product use (odds ratio 1.634; 95%CI 1.036-2.579; p=0.035), preoperative high creatinine (odds ratio 59.387; 95%CI 3.034-1162.496; p=0.007), and high syndecan-1 (odds ratio 1.015; 95%CI 1.002-1.028; p=0.025) were independent predictors of acute kidney injury.

CONCLUSION

This study revealed that elevated preoperative syndecan-1 is associated with acute kidney injury after isolated coronary artery bypass graft accompanied by cardiopulmonary bypass and has prognostic utility independent of other recognized risk factors.

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.

Neutrophil-to-lymphocyte ratio-a new diagnostic and prognostic marker of acute kidney injury. Barriers to broad clinical application

Hospital-acquired acute kidney injury is a heterogeneous clinical syndrome that has multiple aetiologies, widely differing pathogeneses, variable clinical manifestations, and diverse outcomes. There is a persistent unmet need for novel biomarkers that offer timely diagnosis and accurate prediction of the short- and long-term sequelae of acute kidney injury (AKI). AKI is associated with systemic and intrarenal inflammation. The neutrophil-to-lymphocyte ratio (NLR), a readily available marker of inflammation and physiologic stress, has gained increasing attention as universal marker in AKI patients. Numerous retrospective cross-sectional studies assessed the clinical usefulness of this test in high-risk patients with a known time point of the renal injury (surgery, radiological procedures). Strong associations have been demonstrated between high NLR and early onset, progression or recovery of AKI, and the in-hospital and post-discharge mortality of these patients. However, the results were contradictory. The huge heterogeneity of reporting concerning the timing and numbers of blood samples, calculation of the optimal cut-off and the demographic and clinical features of the patient cohorts were confounders. Uncertainty in the optimal cut-off values defining high NLR, the lack of prospective validation of this test and limited understanding of the strengths of associations between NLR and clinical outcomes were further barriers for the clinical adoption of NLR as a valid diagnostic and prognostic test in AKI patients.

The Predictive Role of Systemic Inflammatory Markers in the Development of Acute Kidney Failure and Mortality in Patients with Abdominal Trauma

Background: Abdominal trauma is defined as a variety of injuries to the abdominal wall, solid or hollow intra-abdominal organs, and various intra-abdominal vessels. Recently, there has been a significant amount of interest in the establishment of a reliable biomarker that can predict the outcome in patients with an abdominal injury. The purpose of this study is to confirm the predictive role of inflammatory biomarkers and underlying risk factors and the risk of acute kidney insufficiency (AKI) developing and mortality in abdominal trauma patients; Materials and methods: The current study was intended as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of abdominal trauma confirmed through a CT scan admitted to the County Emergency Clinical Hospital of Targu-Mureș, Romania between January 2017, and December 2021; Results: Non-survivor patients had a greater age (p = 0.02), as well as a higher prevalence of ischemic heart disease (IHD) (p = 0.007), history of myocardial infarction (MI) (p = 0.002), peripheral arterial disease (PAD) (p = 0.01), chronic kidney disease (CKD) (p = 0.01), and all risk factors (p = 0.0004 and p < 0.0001). In terms of injured organs, we have in the second group a higher incidence of kidney injury (p = 0.003) and hemoperitoneum (p = 0.008). Multivariate analysis showed a high baseline value for all inflammatory biomarkers that are independent predictors of adverse outcomes for all recruited patients. Furthermore, for all hospitalized patients, the history of MI (p = 0.03; p = 0.001; and p = 0.003), PAD (p = 0.01; p = 0.01; and p = 0.002), obesity (for all p < 0.0001), CKD (p < 0.001; p = 0.01; and p = 0.001), and kidney injury (p = 0.02; p = 0.004; and p = 0.01) were independent predictors of all outcomes. Moreover, IHD (p = 0.008 and p = 0.02), tobacco (p < 0.0001 and p = 0.02), and hemoperitoneum (p = 0.009 and p = 0.01) were predictors of mortality and composite endpoint, but not for AKI risk, as well as atrial fibrillation [AF] (p = 0.04) as predictors of the composite endpoint Conclusions: Higher monocyte to lymphocyte ratio (MLR), platelets to lymphocyte ratio (PLR), systemic inflammatory index (SII), neutrophil to lymphocyte ratios (NLR), aggregate inflammatory systemic index (AISI), and systemic inflammatory response index (SIRI) levels at admission, according to our data, highly predict AKI risk and death.

Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis

Background

Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication following cardiac surgery. Early prediction of CSA-AKI is of great significance for improving patients' prognoses. The aim of this study is to systematically evaluate the predictive performance of machine learning models for CSA-AKI.

Methods

Cochrane Library, PubMed, EMBASE, and Web of Science were searched from inception to 18 March 2022. Risk of bias assessment was performed using PROBAST. Rsoftware (version 4.1.1) was used to calculate the accuracy and C-index of CSA-AKI prediction. The importance of CSA-AKI prediction was defined according to the frequency of related factors in the models.

Results

There were 38 eligible studies included, with a total of 255,943 patients and 60 machine learning models. The models mainly included Logistic Regression (n = 34), Neural Net (n = 6), Support Vector Machine (n = 4), Random Forest (n = 6), Extreme Gradient Boosting (n = 3), Decision Tree (n = 3), Gradient Boosted Machine (n = 1), COX regression (n = 1), κNeural Net (n = 1), and Naïve Bayes (n = 1), of which 51 models with intact recording in the training set and 17 in the validating set. Variables with the highest predicting frequency included Logistic Regression, Neural Net, Support Vector Machine, and Random Forest. The C-index and accuracy wer 0.76 (0.740, 0.780) and 0.72 (0.70, 0.73), respectively, in the training set, and 0.79 (0.75, 0.83) and 0.73 (0.71, 0.74), respectively, in the test set.

Conclusion

The machine learning-based model is effective for the early prediction of CSA-AKI. More machine learning methods based on noninvasive or minimally invasive predictive indicators are needed to improve the predictive performance and make accurate predictions of CSA-AKI. Logistic regression remains currently the most commonly applied model in CSA-AKI prediction, although it is not the one with the best performance. There are other models that would be more effective, such as NNET and XGBoost.

Systematic review registration

https://www.crd.york.ac.uk/; review registration ID: CRD42022345259.