Risk factors and mortality in patients with sepsis, septic and non septic acute kidney injury in ICU

Assessment of urine CCL2 as a potential diagnostic biomarker for acute kidney injury and septic acute kidney injury in intensive care unit patients

Acute kidney injury (AKI) is a prevalent and serious condition in the intensive care unit (ICU), associated with significant morbidity and mortality. Septic acute kidney injury (SAKI) contributes substantially to AKI cases in the ICU. However, current diagnostic methods have limitations, necessitating the exploration of novel biomarkers. In this study, we investigated the potential of plasma and urine CCL2 levels as diagnostic markers for AKI and SAKI in 216 ICU patients. Our findings revealed significant differences in plasma (p < 0.01) and urine CCL2 (p < 0.0001) levels between AKI and non-AKI patients in the ICU. Notably, urine CCL2 demonstrated promising predictive value for AKI, exhibiting high specificity and sensitivity (AUC = 0.8976; p < 0.0001). Furthermore, we observed higher urine CCL2 levels in SAKI compared to non-septic AKI (p < 0.001) and urine CCL2 could also differentiate SAKI from non-septic AKI (AUC = 0.7597; p < 0.0001). These results suggest that urine CCL2 levels hold promise as early biomarkers for AKI and SAKI, offering valuable insights for timely intervention and improved management of ICU patients.

From Molecular Mechanisms to Clinical Therapy: Understanding Sepsis-Induced Multiple Organ Dysfunction

Sepsis-induced multiple organ dysfunction arises from the highly complex pathophysiology encompassing the interplay of inflammation, oxidative stress, endothelial dysfunction, mitochondrial damage, cellular energy failure, and dysbiosis. Over the past decades, numerous studies have been dedicated to elucidating the underlying molecular mechanisms of sepsis in order to develop effective treatments. Current research underscores liver and cardiac dysfunction, along with acute lung and kidney injuries, as predominant causes of mortality in sepsis patients. This understanding of sepsis-induced organ failure unveils potential therapeutic targets for sepsis treatment. Various novel therapeutics, including melatonin, metformin, palmitoylethanolamide (PEA), certain herbal extracts, and gut microbiota modulators, have demonstrated efficacy in different sepsis models. In recent years, the research focus has shifted from anti-inflammatory and antioxidative agents to exploring the modulation of energy metabolism and gut microbiota in sepsis. These approaches have shown a significant impact in preventing multiple organ damage and mortality in various animal sepsis models but require further clinical investigation. The accumulation of this knowledge enriches our understanding of sepsis and is anticipated to facilitate the development of effective therapeutic strategies in the future.

The Role of Biomarkers in Diagnosis of Sepsis and Acute Kidney Injury

Sepsis and acute kidney injury (AKI) are two major public health concerns that contribute significantly to illness and death worldwide. Early diagnosis and prompt treatment are essential for achieving the best possible outcomes. To date, there are no specific clinical, imaging, or biochemical indicators available to diagnose sepsis, and diagnosis of AKI based on the KDIGO criterion has limitations. To improve the diagnostic process for sepsis and AKI, it is essential to continually evolve our understanding of these conditions. Delays in diagnosis and appropriate treatment can have serious consequences. Sepsis and AKI often occur together, and patients with kidney dysfunction are more prone to developing sepsis. Therefore, identifying potential biomarkers for both conditions is crucial. In this review, we talk about the main biomarkers that evolve the diagnostic of sepsis and AKI, namely neutrophil gelatinase-associated lipocalin (NGAL), proenkephalin (PENK), and cell-free DNA.

Characteristics, Risk Factors, and Outcomes in Acute Kidney Injury Patients: A Retrospective Cross-Sectional Study, Palestine

Background

Acute kidney injury (AKI) is a major medical problem affecting patients' quality of life and healthcare costs.

Objectives

This study evaluated the severity, risk factors, and outcomes of patients diagnosed with acute kidney injury (AKI), including community-acquired AKI (CA-AKI) and hospital-acquired AKI (HA-AKI), who were admitted to tertiary institutions in Palestine.

Methods

This retrospective cross-sectional study was conducted at multiple tertiary care hospitals in Palestine by reviewing patient charts from January 2020 to March 2023. The study included all patients aged ≥18 years who were admitted to the hospital and diagnosed with AKI at admission (CA-AKI) or who developed AKI 48 hours after admission (HA-AKI). Patients with incomplete medical records and those with no reported creatinine levels during their stay, pregnant women, kidney transplant patients, and end-stage renal disease patients were excluded. Data were analyzed using SPSS v22.0. The incidence of AKI in each group was compared using the chi-squared test.

Results

This study included 259 participants. HA-AKI was present in 27.3% of the patients, while CA-AKI was 72.7%. The most common stage among patients was stage 3 (55.7%, HA-AKI) (42.9%, CA-AKI), and the most common comorbidity contributing to AKI was CKD. NSAIDs, ACE-I/ARBs, and DIURETICs were the most nephrotoxic drugs contributing to AKI. Patients with hyperphosphatemia, hyperkalemia, severe metabolic acidosis, or stage 3 AKI require renal replacement therapy. In addition, our findings revealed a significant association among AKI mortality, age, and heart disease.

Conclusion

CA-AKI was more prevalent than HA-AKI in Palestinian patients admitted for AKI. Risk factors for AKI included diabetes, CKD, and medications (antibiotics, NSAID, diuretics, and ACE-I/ARB). Preventive measures, medication management, and disease state management are necessary to minimize AKI during hospital admission or in the community.

Liproxstatin-1 Alleviated Ischemia/Reperfusion-Induced Acute Kidney Injury via Inhibiting Ferroptosis

Ferroptosis, as a novel regulable cell death, is characterized by iron overload, glutathione depletion, and an accumulation of lipid peroxides. Recently, it has been discovered that ferroptosis is involved in ischemia/reperfusion (I/R)-induced acute kidney injury (AKI) and plays a crucial role in renal tubular cell death. In this study, we tried to investigate the effect and mechanism of liproxstatin-1 (Lip-1) in I/R-induced AKI and seek the key regulator of ferroptosis in I/R-induced AKI. Mice were administrated with clamping bilateral renal pedicles for 30 min. We found that early growth response 1 (EGR1) might be a key regulator of ferroptosis, and Lip-1 could suppress ferroptosis via EGR1. Meanwhile, Lip-1 could reduce macrophage recruitment and the release of inflammatory cytokines. These findings indicated that Lip-1 alleviated I/R-induced AKI via regulating EGR1, and it might pave the theoretical basis of a new therapeutic strategy for I/R-induced AKI.

Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach

BACKGROUND

Acute kidney injury (AKI) is a common complication in critically ill patients with sepsis and is often associated with a poor prognosis. We aimed to construct and validate an interpretable prognostic prediction model for patients with sepsis-associated AKI (S-AKI) using machine learning (ML) methods.

METHODS

Data on the training cohort were collected from the Medical Information Mart for Intensive Care IV database version 2.2 to build the model, and data of patients were extracted from Hangzhou First People's Hospital Affiliated to Zhejiang University School of Medicine for external validation of model. Predictors of mortality were identified using Recursive Feature Elimination (RFE). Then, random forest, extreme gradient boosting (XGBoost), multilayer perceptron classifier, support vector classifier, and logistic regression were used to establish a prognosis prediction model for 7, 14, and 28 days after intensive care unit (ICU) admission, respectively. Prediction performance was assessed using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret the ML models.

RESULTS

In total, 2599 patients with S-AKI were included in the analysis. Forty variables were selected for the model development. According to the areas under the ROC curve (AUC) and DCA results for the training cohort, XGBoost model exhibited excellent performance with F1 Score of 0.847, 0.715, 0.765 and AUC (95% CI) of 0.91 (0.90, 0.92), 0.78 (0.76, 0.80), and 0.83 (0.81, 0.85) in 7 days, 14 days and 28 days group, respectively. It also demonstrated excellent discrimination in the external validation cohort. Its AUC (95% CI) was 0.81 (0.79, 0.83), 0.75 (0.73, 0.77), 0.79 (0.77, 0.81) in 7 days, 14 days and 28 days group, respectively. SHAP-based summary plot and force plot were used to interpret the XGBoost model globally and locally.

CONCLUSIONS

ML is a reliable tool for predicting the prognosis of patients with S-AKI. SHAP methods were used to explain intrinsic information of the XGBoost model, which may prove clinically useful and help clinicians tailor precise management.

[Association of neutrophil, lymphocyte, platelet ratio with acute kidney injury in sepsis]

Background

Acute kidney injury (AKI) is frequent in sepsis (25 to 51%), with high mortality (40 to 80%) and long-term complications. Despite its importance we do not have accessible markers in intensive care. In other entities (post-surgical and COVID-19) the neutrophil/lymphocyte and platelet (N/LP) ratio has been associated with acute kidney injury; however, this relationship has not been studied in a pathology with a severe inflammatory response such as sepsis.

Objective

To demonstrate the association between N/LP with AKI secondary to sepsis in intensive care.

Material and methods

Ambispective cohort study in patients over 18 years who were admitted to intensive care with a diagnosis of sepsis. The N/LP ratio was calculated from admission up to the seventh day and up to the diagnosis of AKI and outcome. Statistical analysis was performed with chi squared test, Cramer's V and multivariate logistic regression.

Results

Out of the 239 patients studied, the incidence of AKI developed in 70%. 80.9% of patients with N/LP ratio > 3 had AKI (p < 0.0001, Cramer's V 0.458, OR 3.05, 95% CI 1.602-5.8) and increased renal replacement therapy (21.1 vs. 11.1%, p = 0.043).

Conclusion

N/LP ratio > 3 has a moderate association with AKI secondary to sepsis in the intensive care unit.

Epidemiological profile and risk factors associated with death in patients receiving invasive mechanical ventilation in an adult intensive care unit from Brazil: a retrospective study

Introduction

Understanding the epidemiological profile and risk factors associated with invasive mechanical ventilation (IMV) is essential to manage the patients better and to improve health services. Therefore, our objective was to describe the epidemiological profile of adult patients in intensive care that required IMV in-hospital treatment. Also, to evaluate the risks associated with death and the influence of positive end-expiratory pressure (PEEP) and arterial oxygen pressure (PaO2) at admission in the clinical outcome.

Methods

We conducted an epidemiological study analyzing medical records of inpatients who received IMV from January 2016 to December 2019 prior to the Coronavirus Disease (COVID)-19 pandemic in Brazil. We considered the following characteristics in the statistical analysis: demographic data, diagnostic hypothesis, hospitalization data, and PEEP and PaO2 during IMV. We associated the patients' features with the risk of death using a multivariate binary logistic regression analysis. We adopted an alpha error of 0.05.

Results

We analyzed 1,443 medical records; out of those, 570 (39.5%) recorded the patients' deaths. The binary logistic regression was significant in predicting the patients' risk of death [X2(9) = 288.335; p < 0.001]. Among predictors, the most significant in relation to death risk were: age [elderly ≥65 years old; OR = 2.226 (95%CI = 1.728-2.867)]; male sex (OR = 0.754; 95%CI = 0.593-0.959); sepsis diagnosis (OR = 1.961; 95%CI = 1.481-2.595); need for elective surgery (OR = 0.469; 95%CI = 0.362-0.608); the presence of cerebrovascular accident (OR = 2.304; 95%CI = 1.502-3.534); time of hospital care (OR = 0.946; 95%CI = 0.935-0.956); hypoxemia at admission (OR = 1.635; 95%CI = 1.024-2.611), and PEEP >8 cmH2O at admission (OR = 2.153; 95%CI = 1.426-3.250).

Conclusion

The death rate of the studied intensive care unit was equivalent to that of other similar units. Regarding risk predictors, several demographic and clinical characteristics were associated with enhanced mortality in intensive care unit patients under mechanical ventilation, such as diabetes mellitus, systemic arterial hypertension, and older age. The PEEP >8 cmH2O at admission was also associated with increased mortality since this value is a marker of initially severe hypoxia.

Machine learning for the prediction of cognitive impairment in older adults

Objective

The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm.

Methods

The complete data of 2,226 participants aged 60-80 years were extracted from the 2011-2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application.

Results

The study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application.

Conclusions

ML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

Acute Kidney Injury Among Patients with Multi-Drug Resistant Infection: A Study from Jordan

Background

Acute kidney injury (AKI) is a well-known complication for hospitalized patients. Sepsis and various infections play a significant role in increasing the incidence of AKI. The present study evaluated the risk for Multidrug-resistant (MDR) infections and its effect on the incidence of AKI, hospitalization, need for dialysis, and mortality.

Methods

In a retrospective study design, data were collected from all adult patients with a positive multi-drug resistant culture who were admitted to King Abdullah University Hospital (KAUH). Records of 436 patients were reviewed between January 2017 - December 2018 with at least one year of follow-up.

Results

The mean age was 57.3 years (SD± 23.1), and 58.5% were males. The most common source of positive cultures was sputum, with 50% positive cultures. The incidence of AKI was 59.2%. The most isolated microorganism was Acinetobacter baumannii (76.8%), followed by Pseudomonas aeruginosa (14.9%).On multivariate analysis, age (OR 1.1, 95% CI 1.1-1.2, P=0.001), HTN (OR 1.8, 95% CI 1.0-3.3, P=0.02), DM (OR 1.1, 95% CI 0.6-1.9, P=0.69) and the use of Foley catheter on chronic bases (OR 4.3, 95% CI 2.6-6.8, P<0.0001) were strong predictors of AKI. Among patients with AKI, 74.4% died compared to 44.4% among non-AKI patients (p<0.001).

Conclusion

In patients with MDR, AKI incidence, hospitalization, and mortality were high. Early detection and addressing the problem may decrease bad outcomes, and health education for reducing antibiotic abuse is needed to lower MDR.

Association between dexmedetomidine administration and outcomes in critically ill patients with sepsis-associated acute kidney injury

STUDY OBJECTIVE

To investigate the association between dexmedetomidine administration and outcomes in critically ill patients with sepsis-associated acute kidney injury (SA-AKI).

DESIGN

A single-center, retrospective, cohort study.

SETTING

Intensive care unit (ICU).

PATIENTS

A total of 2192 critically ill patients with SA-AKI were included in the analysis, which identified from the Medical Information Mart for Intensive Care (MIMIC-IV) database between 2008 and 2019.

INTERVENTIONS

Intravenous infusion of dexmedetomidine.

MEASUREMENTS

The primary outcome was recovery of renal function. In-hospital mortality, vasopressor requirements, length of ICU and hospital stay were considered secondary outcomes. The Cox proportional hazards, logistic regression, and linear regression models were used to assess the association between dexmedetomidine and outcomes. Propensity score matching (PSM) analysis was used to match patients receiving dexmedetomidine to those without treatment.

MAIN RESULTS

After PSM, 719 matched patient pairs were derived from patients who received dexmedetomidine and those who did not. The administration of dexmedetomidine was associated with a higher rate of renal recovery [61.8% vs. 55.8%, hazard ratio (HR) 1.35; P = 0.01], reduced in-hospital mortality [28.3% vs. 41.3%, HR 0.56; P < 0.001], and prolonged intensive care unit (ICU) stay [15.8d vs 12.6d, HR 2.34; P < 0.001] and hospital stay [23.7d vs 19.7d, HR 4.47; P < 0.001]. No significant difference was found in vasopressor requirements in patients with SA-AKI. Nevertheless, results illustrated that dose receiving between 0.30 and 1.00 μg/kg/h and duration using under 48 h of dexmedetomidine was associated with improvements in renal function recovery in SA-AKI patients.

CONCLUSION

Dexmedetomidine administration was associated with improvements in renal function recovery and in-hospital survival in critically ill patients with SA-AKI. The results need to be verified in further randomized controlled trials.

The Intersection of Acute Kidney Injury and Non-Coding RNAs: Inflammation

Acute renal injury (AKI) is a complex clinical syndrome, involving a series of pathophysiological processes, in which inflammation plays a key role. Identification and verification of gene signatures associated with inflammatory onset and progression are imperative for understanding the molecular mechanisms involved in AKI pathogenesis. Non-coding RNAs (ncRNAs), involved in epigenetic modifications of inflammatory responses, are associated with the aberrant expression of inflammation-related genes in AKI. However, its regulatory role in gene expression involves precise transcriptional regulation mechanisms which have not been fully elucidated in the complex and volatile inflammatory response of AKI. In this study, we systematically review current research on the intrinsic molecular mechanisms of ncRNAs that regulate the inflammatory response in AKI. We aim to provide potential research directions and strategies for developing ncRNA-targeted gene therapies as an intervention for the inflammatory damage in AKI.

Machine learning for the prediction of acute kidney injury in patients with sepsis

BACKGROUND

Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis.

METHODS

Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model.

RESULTS

A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models.

CONCLUSION

The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Acute Kidney Injury among Hospital-Admitted COVID-19 Patients: A Study from Jordan

Objective

During the COVID-19 pandemic, many patients have been admitted to hospitals with severe respiratory disease and suffered complications. Acute kidney injury (AKI) is among the more dangerous complications contributing to morbidity and mortality among patients.

Methods

This retrospective study focused on all hospital-admitted COVID-19 patients between September and December 2020. A total of 1,044 patients were enrolled. Patient demographics, medical records, and laboratory data were gathered. Patients were split into two groups: AKI and non-AKI. Comparisons comprised demographics, labs, ICU transfer, need for ventilation and oxygen therapy, medications, hospital stay, and deaths.

Results

AKI incidence in the cohort was 25.3%, and a majority were stage 1 (53.3%). Among these, hemodialysis was started in 1.8%. Higher age (P<0.001), diabetes mellitus (P=0.001), hypertension (P=0.001), ACEI/ARB use (P=0.008), erythrocyte-sedimentation rate (P=0.002), CRP (P<0.0001), and ferritin (P=0.01) were predictors of AKI. Among all admitted COVID-19 patients, 30.2% died in hospital. Among those with AKI, 75.9% died in comparison to 24.1% of non-AKI patients (P<0.001). Among COVID-19 patients admitted to the ICU, 80.5% died: 70.5% were from the AKI group and 29.5% from the non-AKI group (P<0.001).

Conclusion

High mortality and morbidity is associated with COVID-19 infection, and AKI is contributing significantly to the outcomes of hospitalized patients with the infection. Early recognition of and treatment for AKI will decrease mortality and hospitalization in patients with COVID-19.

Recalibration and validation of the Charlson Comorbidity Index in acute kidney injury patients underwent continuous renal replacement therapy

Background

Comorbid conditions impact the survival of patients with severe acute kidney injury (AKI) who require continuous renal replacement therapy (CRRT). The weights assigned to comorbidities in predicting survival vary based on type of index, disease, and advances in management of comorbidities. We developed a modified Charlson Comorbidity Index (CCI) for use in patients with AKI requiring CRRT (mCCI-CRRT) and improved the accuracy of risk stratification for mortality.

Methods

A total of 828 patients who received CRRT between 2008 and 2013, from three university hospital cohorts was included to develop the comorbidity score. The weights of the comorbidities were recalibrated using a Cox proportional hazards model adjusted for demographic and clinical information. The modified index was validated in a university hospital cohort (n = 919) using the data of patients treated from 2009 to 2015.

Results

Weights for dementia, peptic ulcer disease, any tumor, and metastatic solid tumor were used to recalibrate the mCCI-CRRT. Use of these calibrated weights achieved a 35.4% (95% confidence interval [CI], 22.1%–48.1%) higher performance than unadjusted CCI in reclassification based on continuous net reclassification improvement in logistic regression adjusted for age and sex. After additionally adjusting for hemoglobin and albumin, consistent results were found in risk reclassification, which improved by 35.9% (95% CI, 23.3%–48.5%).

Conclusion

The mCCI-CRRT stratifies risk of mortality in AKI patients who require CRRT more accurately than does the original CCI, suggesting that it could serve as a preferred index for use in clinical practice.

Association of Oliguria With Acute Kidney Injury Diagnosis, Severity Assessment, and Mortality Among Patients With Critical Illness

Importance

The current definition and staging of acute kidney injury (AKI) considers alterations in serum creatinine (sCr) level and urinary output (UO). However, the relevance of oliguria-based criteria is disputed.

Objective

To determine the contribution of oliguria, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, to AKI diagnosis, severity assessment, and short- and long-term outcomes.

Design, Setting, and Participants

This cohort study included adult patients admitted to a multidisciplinary intensive care unit from January 1, 2010, to June 15, 2020. Patients receiving long-term dialysis and those who declined consent were excluded. Daily sCr level and hourly UO measurements along with sociodemographic characteristics and severity scores were extracted from electronic medical records. Long-term mortality was assessed by cross-referencing the database with the Swiss national death registry. The onset and severity of AKI according to the KDIGO classification was determined using UO and sCr criteria separately, and their agreement was assessed.

Main Outcomes and Measures

Using a multivariable model accounting for baseline characteristics, severity scores, and sCr stages, the association of UO criteria with 90-day mortality was evaluated. Sensitivity analyses were conducted to assess how missing sCr, body weight, and UO values, as well as different sCr baseline definitions and imputations methods, would affect the main results.

Results

Among the 15 620 patients included in the study (10 330 men [66.1%] with a median age of 65 [IQR, 53-75] years, a median Simplified Acute Physiology Score II score of 40.0 [IQR, 30.0-53.0], and a median follow-up of 67.0 [IQR, 34.0-100.0] months), 12 143 (77.7%) fulfilled AKI criteria. Serum creatinine and UO criteria had poor agreement on AKI diagnosis and staging (Cohen weighted κ, 0.36; 95% CI, 0.35-0.37; P < .001). Compared with the isolated use of sCr criteria, consideration of UO criteria enabled identification of AKI in 5630 patients (36.0%). Those patients had a higher 90-day mortality than patients without AKI (724 of 5608 [12.9%] vs 288 of 3462 [8.3%]; P < .001). On multivariable analysis accounting for sCr stage, comorbidities, and illness severity, UO stages 2 and 3 were associated with a higher 90-day mortality (odds ratios, 2.4 [95% CI, 1.6-3.8; P < .001] and 6.2 [95% CI, 3.7-10.5; P < .001], respectively). These results remained significant in all sensitivity analyses.

Conclusions and Relevance

The findings of this cohort study suggest that oliguria lasting more than 12 hours (KDIGO stage 2 or 3) has major AKI diagnostic implications and is associated with outcomes irrespective of sCr elevations.

A-Lipoic Acid Alleviates Folic Acid-Induced Renal Damage Through Inhibition of Ferroptosis

Folic acid (FA)-induced acute kidney injury (AKI) is characterized by the disturbance of redox homeostasis, resulting in massive tubular necrosis and inflammation. Α-lipoic acid (LA), as an antioxidant, has been reported to play an important role in renal protection, but the underlying mechanism remains poorly explored. The aim of this study is to investigate the protective effect of LA on FA-induced renal damage. Our findings showed that LA could ameliorate renal dysfunction and histopathologic damage induced by FA overdose injection. Moreover, FA injection induced severe inflammation, indicated by increased release of pro-inflammatory cytokines tumor necrosis factor (TNF)-α and IL-1β, as well as infiltration of macrophage, which can be alleviated by LA supplementation. In addition, LA not only reduced the cellular iron overload by upregulating the expressions of Ferritin and ferroportin (FPN), but also mitigated reactive oxygen species (ROS) accumulation and lipid peroxidation by increasing the levels of antioxidant glutathione (GSH) and glutathione peroxidase-4 (GPX4). More importantly, we found that LA supplementation could reduce the number of Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive tubular cells caused by FA, indicating that the tubular cell death mediated by ferroptosis may be inhibited. Further study demonstrated that LA supplementation could reverse the decreased expression of cystine/glutamate antiporter xCT (SLC7A11), which mediated GSH synthesis. What is more, mechanistic study indicated that p53 activation was involved in the inhibitory effect of SLC7A11 induced by FA administration, which could be suppressed by LA supplementation. Taken together, our findings indicated that LA played the protective effect on FA-induced renal damage mainly by inhibiting ferroptosis.

A Prediction Model for Assessing Prognosis in Critically Ill Patients with Sepsis-associated Acute Kidney Injury

BACKGROUND

Sepsis-associated acute kidney injury (SA-AKI) is a common problem in critically ill patients and is associated with high morbidity and mortality. Early prediction of the survival of hospitalized patients with SA-AKI is necessary, but a reliable and valid prediction model is still lacking.

METHODS

We conducted a retrospective cohort analysis based on a training cohort of 2,066 patients enrolled from the Multiparameter Intelligent Monitoring in Intensive Care Database III (MIMIC III) and a validation cohort of 102 patients treated at Nanfang Hospital of Southern Medical University. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to identify predictors for survival. Areas under the ROC curves (AUC), the concordance index (C-index), and calibration curves were used to evaluate the efficiency of the prediction model (SAKI) in both cohorts.

RESULTS

The overall mortality of SA-AKI was approximately 18%. Age, admission type, liver disease, metastatic cancer, lactate, BUN/SCr, admission creatinine, positive culture, and AKI stage were independently associated with survival and combined in the SAKI model. The C-index in the training and validation cohorts was 0.73 and 0.72. The AUC in the training cohort was 0.77, 0.72, and 0.70 for the 7-day, 14-day, and 28-day probability of in-hospital survival, respectively, while in the external validation cohort, it was 0.83, 0.73, and 0.67. SAPSII and SOFA scores showed poorer performance. Calibration curves demonstrated a good consistency.

CONCLUSIONS

Our SAKI model has predictive value for in-hospital mortality of SA-AKI in critically ill patients and outperforms generic scores.

Down-regulation of lncRNA SNHG5 relieves sepsis-induced acute kidney injury by regulating the miR-374a-3p/TLR4/NF-κB pathway

Sepsis is an acute systemic infectious disease engendered by infectious factors, which can cause the dysfunction of multiple organs, including acute kidney injury (AKI). Recently, more and more researchers are focussing on long noncoding RNA (lncRNA) that is closely associated with the development and progression of various diseases; however, the role and mechanism of lncRNA in sepsis-induced AKI are not fully understood. Here, we found a significant increase in the expression of lncRNA small nuclear RNA host gene 5 (SNHG5) in the serum of patients with sepsis than healthy controls. Similar results were obtained from mouse model of sepsis. Further investigations revealed that knockdown of SNHG5 improves the viability and reduces the rate of apoptosis and the generation of inflammatory cytokines in HK-2 and TCMK-1 cells treated with lipopolysaccharide. Mechanistically, we showed that SNHG5 can combine with microRNA-374a-3p (miR-374a-3p), which inhibits nuclear factor-κB (NF-κB) activity by targeting TLR4. In conclusion, our results demonstrate that SNHG5 may regulate sepsis-induced AKI via the miR-374a-3p/TLR4/NF-κB pathway, therefore providing a new insight into the treatment of this disease.

Circ_0091702 serves as a sponge of miR-545-3p to attenuate sepsis-related acute kidney injury by upregulating THBS2

Circular RNA (circRNA) has been shown to play an important function in the progression of human diseases, including sepsis with acute kidney injury (AKI). However, the role and mechanism of circ_0091702 in sepsis-induced AKI have yet to be confirmed. Lipopolysaccharide (LPS) was used to induce HK2 cells to construct AKI cell models in vitro. Quantitative real-time PCR was used to measure the expression of circ_0091702, inflammatory cytokines, microRNA (miR)-545-3p and thrombospondin 2 (THBS2). Cell counting kit 8 assay and flow cytometry were used to assess cell viability and apoptosis. Besides, the protein levels of apoptosis markers and THBS2 were evaluated by western blot analysis. In addition, the concentrations of inflammatory cytokines were detected by enzyme-linked immunosorbent assay (ELISA). Cell oxidative stress was determined by detecting the contents of oxidative stress markers. Dual-luciferase reporter assay and RIP assay were used to confirm the relationship between miR-545-3p and circ_0091702 or miR-545-3p and THBS2. Circ_0091702 was downregulated in septic AKI patients and LPS-induced HK2 cells. Circ_0091702 could attenuate LPS-induced HK2 cell injury, while its silencing had an opposite effect. In the terms of mechanism, circ_0091702 could act as a sponge of miR-545-3p, and miR-545-3p could directly target THBS2. Functional experiments revealed that miR-545-3p could reverse the alleviating effect of circ_0091702 on LPS-induced HK2 cell injury, and THBS2 knockdown also could overturn the suppressing effect of miR-545-3p inhibitor on LPS-induced HK2 cell injury. Additionally, we also suggested that circ_0091702 could sponge miR-545-3p to regulate THBS2 expression. In conclusion, our results showed that circ_0091702 could suppress LPS-induced HK2 cell injury via the miR-545-3p/THBS2 axis, indicating that circ_0091702 might be an important biomarker for relieving sepsis-related AKI.

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Background

Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.

Methods

Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model.

Results

A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800–0.838], 0.797 [95% CI 0.781–0.813] and 0.857 [95% CI 0.839–0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value.

Conclusions

Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

Association of Chloride Ion and Sodium-Chloride Difference With Acute Kidney Injury and Mortality in Critically Ill Patients

Objectives

Derangements of chloride ion concentration ([Cl^-]) have been shown to be associated with acute kidney injury and other adverse outcomes. For a physicochemical approach, however, chloride ion concentration should be considered with sodium ion concentration. This study aimed to examine the association of chloride ion concentration and the main strong ion difference (difference between sodium ion concentration and chloride ion concentration) during the first 24 hours after admission into ICU with the development of acute kidney injury and mortality.

Design

Retrospective analyses using the eICU Collaborative Research Database.

Setting

ICUs in 208 hospitals across the United States between 2014 and 2015.

Patients

Critically ill patients who were admitted into the ICU.

Interventions

None.

Measurements and Main Results

A total of 34,801 patients records were analyzed. A multivariable logistic regression analysis for the development of acute kidney injury within 7 days of ICU admission shows that, compared with main strong iron difference 32-34 mEq/as a reference, there were significantly high odds for the development of acute kidney injury in nearly all groups with main strong iron difference more than 34 mEq/L (main strong iron difference = 34-36 mEq/L, odds ratio = 1.17, p = 0.02; main strong iron difference = 38-40 mEq/L, odds ratio = 1.40, p < 0.001; main strong iron difference = 40-42 mEq/L, odds ratio = 1.46, p = 0.001; main strong iron difference > 42 mEq/L, odds ratio = 1.56, p < 0.001). With chloride ion concentration 104-106 mEq/L as a reference, the odds for acute kidney injury were significantly higher only in chloride ion concentration less than or equal to 94 mEq/L and chloride ion concentration 98-100 mEq/L groups. Analyses conducted using inverse probability weighting showed significantly greater odds for ICU mortality in all groups with main strong iron difference greater than 34mEq/L other than the 36-38mEq/L group, as well as in the less than 26-mEq/L group.

Conclusions

Main strong iron difference measured on ICU presentation to the ICU predicts acute kidney injury within 7 days, with low and, in particular, high values representing increased risk. The association between the chloride levels and acute kidney injury is statistically insignificant in models incorporating main strong iron difference, suggesting main strong iron difference is a better predictive marker than chloride on ICU admission.

Incidence, Risk Factors, and Outcome of Acute Kidney Injury in the Intensive Care Unit: A Single-Center Study from Jordan

Background

Acute kidney injury (AKI) is a common serious problem affecting critically ill patients in intensive care unit (ICU). It increases their morbidity, mortality, length of ICU stay, and long-term risk of chronic kidney disease (CKD).

Methods

A retrospective study was carried out in a tertiary hospital in Jordan. Medical records of patients admitted to the medical ICU between 2013 and 2015 were reviewed. We aimed to identify the incidence, risk factors, and outcomes of AKI. Acute kidney injury network (AKIN) classification was used to define and stage AKI.

Results

2530 patients were admitted to medical ICU, and the incidence of AKI was 31.6%, mainly in stage 1 (59.4%). In multivariate analysis, increasing age (odds ratio (OR) = 1.2 (95% CI 1.1–1.3), P = 0.0001) and higher APACHE II score (OR = 1.5 (95% CI 1.2–1.7), P = 0.001) were predictors of AKI, with 20.4% of patients started on hemodialysis. At the time of discharge, 58% of patients with AKI died compared to 51.3% of patients without AKI (P = 0.05). 88% of patients with AKIN 3 died by the time of discharge compared to patients with AKIN 2 and 1 (75.3% and 61.2% respectively, P = 0.001).

Conclusion

AKI is common in ICU patients, and it increases mortality and morbidity. Close attention for earlier detection and addressing risk factors for AKI is needed to decrease incidence, complications, and mortality.

The Charlson Comorbidity Index: can it predict the outcome in acute kidney injury?

PURPOSE

Comorbidity has a significant impact on the health status and treatment outcome of a patient. The Charlson comorbidity index (CCI) is a frequently used scoring system, which evaluates the prognosis based on the patient's comorbid conditions. The aim of this study was to evaluate the usefulness of CCI in predicting the mortality and renal recovery in non-critically ill patients with severe AKI.

METHODS

A total of 530 adult patients who were referred from the emergency department and underwent intermittent urgent hemodialysis (uHD) were enrolled in the study. Personal history for comorbidities were recorded and then assessed using the CCI.

RESULTS

The mean CCI score was 3.3 ± 2.6. In our multivariate analysis, higher white blood cell count was associated with mortality (p = 0.023). The other parameters including CCI score were not found to be significantly associated with mortality excluding patients with sepsis. Moreover, the CCI was not significantly useful in the discrimination of patients with complete recovery from patients who remained dependent to dialysis.

CONCLUSIONS

We could not find significant association between CCI and short-term hospital mortality and renal outcome. Whereas, malnutrition, inflammation and general aging may have impact on short-term mortality among patients.