Risk factors and prognosis assessment for acute kidney injury: The 2020 consensus of the Taiwan AKI Task Force

Risk of acute kidney injury following repeated contrast exposure in trauma patients

PURPOSE

This study investigates the risk of contrast-associated acute kidney injury (CA-AKI) in trauma patients, focusing on the impact of cumulative contrast medium doses.

METHODS

A retrospective review was conducted at a level 1 trauma center (2019-2021). The study included patients who underwent intravascular contrast-enhanced examinations for torso trauma within 7 days post-injury. The total contrast medium volume within 7 days was calculated. Multivariate logistic regression (MLR) identified AKI risk factors.

RESULTS

Among the 264 patients, 7.2% (19/264) developed AKI, with 3.4% (9/264) classified as KDIGO stage 3. Approximately 42.8% of patients underwent at least two contrast-enhanced examinations. The mean total contrast medium given was 129.5mL (range 80-410 ml). Multiple logistic regression (MLR) analysis identified four independent risk factors for AKI: diabetes mellitus, initial eGFR < 30, use of inotropic agents, and contrast medium exposure. The odds ratio of AKI increased by 2.92 (95% CI 1.30-6.53) for every 100 ml increase in contrast dose. The contrast volume exposure only plays an important role in severe trauma patients (ISS ≥ 25). Moreover, when correlated with eGFR, the contrast medium exposure volume demonstrated better predictive ability for AKI with a best cut-off value of Contrast volume to eGFR ratio > 1.86.

CONCLUSION

While repetitive contrast-enhanced examinations are sometimes inevitable, they do come with costs. The CA-AKI risk increases as the amount of contrast medium accumulates in trauma patients who require repetitive examinations.

Evaluating the risk of acute kidney injury and mortality associated with concomitant use of vancomycin with piperacillin/tazobactam or meropenem in critically ill and non-critically ill patients: a systematic review and meta-analysis

BACKGROUND

There are conflicting findings regarding the risk of acute kidney injury (AKI) and mortality with vancomycin/piperacillin-tazobactam combination (VPT) and vancomycin/meropenem (VM). The aim of this meta-analysis was to compare the risk of AKI and mortality between VPT and VM.

METHODS

Observational studies reporting the incidence of AKI and mortality in patients receiving VPT or VM between January 2017 and September 2024 were retrieved from PubMed, the Cochrane Library, and Web of Science. The primary outcome of the analysis was the risk of AKI, and the secondary outcomes were the mortality rate, need for renal replacement therapy (RRT), and hospital length of stay (LOS). This meta-analysis was conducted using a random-effects model to estimate the odds ratios (OR) and 95% confidence intervals (CI) for AKI, mortality, and RRT or mean difference and 95% CI for the LOS.

RESULTS

Seventeen studies involving a total of 80,595 patients were included in the analysis. The odds of developing AKI were higher among patients who received the VPT versus those who received the VM combination (OR = 2.02; 95%CI 1.56-2.62). There were no differences between VPT and VM in the mortality and hospital length of stay; however, the odds of requiring RRT were higher among VPT group versus VM group (OR = 1.55; 95%CI 1.23-1.96).

CONCLUSION

The findings suggest that the use of VPT is associated with a higher risk of AKI compared to VM and highlight the need for cautious antibiotic selection and monitoring of renal function in patients receiving these combinations.

The expert consensus on care and education for patients with diabetic kidney disease in Taiwan

Increasing prevalence of type 2 DM (T2DM) and diabetic kidney disease (DKD) has posed a great impact in Taiwan. However, guidelines focusing on multidisciplinary patient care and patient education remain scarce. By literature review and expert discussion, we propose a consensus on care and education for patients with DKD, including general principles, specifics for different stages of chronic kidney disease (CKD), and special populations. (i.e. young ages, patients with atherosclerotic cardiovascular disease or heart failure, patients after acute kidney injury, and kidney transplant recipients). Generally, we suggest performing multidisciplinary patient care and education in alignment with the government-led Diabetes Shared Care Network to improve the patients' outcomes for all patients with DKD. Also, close monitoring of renal function with early intervention, control of comorbidities in early stages of CKD, and nutrition adjustment in advanced CKD should be emphasized.

Early Identification of Sepsis-Induced Acute Kidney Injury by Using Monocyte Distribution Width, Red-Blood-Cell Distribution, and Neutrophil-to-Lymphocyte Ratio

Sepsis-induced acute kidney injury (AKI) is a common complication in patients with severe illness and leads to increased risks of mortality and chronic kidney disease. We investigated the association between monocyte distribution width (MDW), red-blood-cell volume distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), sepsis-related organ-failure assessment (SOFA) score, mean arterial pressure (MAP), and other risk factors and sepsis-induced AKI in patients presenting to the emergency department (ED). This retrospective study, spanning 1 January 2020, to 30 November 2020, was conducted at a university-affiliated teaching hospital. Patients meeting the Sepsis-2 consensus criteria upon presentation to our ED were categorized into sepsis-induced AKI and non-AKI groups. Clinical parameters (i.e., initial SOFA score and MAP) and laboratory markers (i.e., MDW, RDW, and NLR) were measured upon ED admission. A logistic regression model was developed, with sepsis-induced AKI as the dependent variable and laboratory parameters as independent variables. Three multivariable logistic regression models were constructed. In Model 1, MDW, initial SOFA score, and MAP exhibited significant associations with sepsis-induced AKI (area under the curve [AUC]: 0.728, 95% confidence interval [CI]: 0.668-0.789). In Model 2, RDW, initial SOFA score, and MAP were significantly correlated with sepsis-induced AKI (AUC: 0.712, 95% CI: 0.651-0.774). In Model 3, NLR, initial SOFA score, and MAP were significantly correlated with sepsis-induced AKI (AUC: 0.719, 95% CI: 0.658-0.780). Our novel models, integrating MDW, RDW, and NLR with initial SOFA score and MAP, can assist with the identification of sepsis-induced AKI among patients with sepsis presenting to the ED.

Prognostic nutritional index as a predictive marker for acute kidney injury in adult critical illness population: a systematic review and diagnostic test accuracy meta-analysis

BACKGROUND

The prognostic nutritional index (PNI), integrating nutrition and inflammation markers, has been increasingly recognized as a prognostic predictor in diverse patient cohorts. Recently, its effectiveness as a predictive marker for acute kidney injury (AKI) in various clinical settings has gained attention. This study aims to assess the predictive accuracy of the PNI for AKI in critically ill populations through systematic review and meta-analysis.

METHODS

A systematic review was conducted using the databases MEDLINE, EMBASE, PubMed, and China National Knowledge Infrastructure up to August 2023. The included trials reported the PNI assessment in adult population with critical illness and its predictive capacity for AKI. Data on study characteristics, subgroup covariates, and diagnostic performance of PNI, including sensitivity, specificity, and event rates, were extracted. A diagnostic test accuracy meta-analysis was performed. Subgroup analyses and meta-regression were utilized to investigate the sources of heterogeneity. The GRADE framework evaluated the confidence in the meta-analysis's evidence.

RESULTS

The analysis encompassed 16 studies with 17 separate cohorts, totaling 21,239 patients. The pooled sensitivity and specificity of PNI for AKI prediction were 0.67 (95% CI 0.58-0.74) and 0.74 (95% CI 0.67-0.80), respectively. The pooled positive likelihood ratio was 2.49 (95% CI 1.99-3.11; low certainty), and the negative likelihood ratio was 0.46 (95% CI 0.37-0.56; low certainty). The pooled diagnostic odds ratio was 5.54 (95% CI 3.80-8.07), with an area under curve of summary receiver operating characteristics of 0.76. Subgroup analysis showed that PNI's sensitivity was higher in medical populations than in surgical populations (0.72 vs. 0.55; p < 0.05) and in studies excluding patients with chronic kidney disease (CKD) than in those including them (0.75 vs. 0.56; p < 0.01). Overall, diagnostic performance was superior in the non-chronic kidney disease group.

CONCLUSION

Our study demonstrated that PNI has practical accuracy for predicting the development of AKI in critically ill populations, with superior diagnostic performance observed in medical and non-CKD populations. However, the diagnostic efficacy of the PNI has significant heterogeneity with different cutoff value, indicating the need for further research.

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19

PURPOSE

To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients.

MATERIALS AND METHODS

Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed.

RESULTS

Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]).

CONCLUSIONS

An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

Predicting AKI in patients with AMI: Development and assessment of a new predictive nomogram

Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI) and is associated with both long- and short-term consequences. This study aimed to investigate relevant risk variables and create a nomogram that predicts the probability of AKI in patients with AMI, so that prophylaxis could be initiated as early as possible. Data were gathered from the medical information mart for the intensive care IV database. We included 1520 patients with AMI who were admitted to the coronary care unit or the cardiac vascular intensive care unit. The primary outcome was AKI during hospitalization. Independent risk factors for AKI were identified by applying least absolute shrinkage and selection operator regression models and multivariate logistic regression analyses. A multivariate logistic regression analysis was used to build a predictive model. The discrimination, calibration, and clinical usefulness of the prediction model were assessed using C-index, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. Of 1520 patients, 731 (48.09%) developed AKI during hospitalization. Hemoglobin, estimated glomerular filtration rate, sodium, bicarbonate, total bilirubin, age, heart failure, and diabetes were identified as predictive factors for the nomogram construction (P < .01). The model displayed good discrimination, with a C-index of 0.857 (95% CI:0.807-0.907), and good calibration. A high C-index value of 0.847 could still be reached during interval validation. Decision curve analysis showed that the AKI nomogram was clinically useful when the intervention was determined at an AKI possibility threshold of 10%. The nomogram constructed herein can successfully predict the risk of AKI in patients with AMI early and provide critical information that can facilitate prompt and efficient interventions.

Prognosis and mortality within 90 days in community-acquired acute kidney injury in the Southwest of Sweden

BACKGROUND

Community-acquired acute kidney injury (CA-AKI) is common among hospitalized patients and has a poor prognosis. Research is scarce on the impact of a CA-AKI episode among patients without preexisting kidney disease and has not previously been investigated in Sweden. The aim was to describe the outcomes of patients with normal pre-hospitalization kidney function, admitted with community-acquired AKI and to investigate the association between AKI severity with outcomes.

METHODS

A retrospective population-based study was applied including patients with CA-AKI according to KDIGO classification, admitted via emergency department (ED) 2017-2019 and with a 90-day follow-up period from the ED-admission, collecting data from the Regional Healthcare Informative Platform. Age, gender and AKI stages, mortality and follow-up regarding recovery and readmission was registered. Hazard ratio (HR) and 95% confidence Interval (CI) for mortality was analyzed using Cox regression adjusted for age, comorbidities, and medication.

RESULTS

There were 1646 patients included, mean age was 77.5 years. CA-AKI stage 3 occurred in 51% of patients < 65 years of age and 34% among those > 65 years. In this study, 578 (35%) patients died and 233 (22%) recovered their kidney function. Mortality rate peaked within the first two weeks and among those at AKI stage 3. Nephrology referral post discharge occurred in 3% and 29% were readmitted. HRs for mortality was 1.9 (CI 1.38-2.62) for those who are > 65 years, 1.56 (CI 1.30-1.88) for atherosclerotic-cardiovascular disease. Medication with RAASi related to a decreased HR 0.27 (95% CI 0.22-0.33).

CONCLUSIONS

CA-AKI is associated with high mortality within 90 days, increased risk for developing chronic kidney disease (CKD) and only one fifth recover their kidney function after hospitalization with an AKI. Nephrology referral was sparse. Patient follow-up after a hospitalization with AKI should be carefully planned during the first 90 days and focused on identifying those with a higher risk of developing CKD.

Clinical and Biochemical Profile Associated with Renal Recovery after Acute Kidney Injury in A Mexican Population: Retrospective Cohort Study

Background and Objectives: Our primary objective was to study the clinical and biochemical characteristics associated with acute kidney injury (AKI) remission in a group of Mexican patients. Materials and methods: We retrospectively enrolled 75 patients who were diagnosed with AKI and separated the sample into two groups: nonremitting patients (n = 27, 36%) vs. remitting patients (n = 48, 64%). Results: We found significant relationships between nonremitting AKI and previous diagnosis of chronic kidney disease (p = 0.009), higher serum creatinine (Cr) at admission (p < 0.0001), lower estimated glomerular filtration rate (eGFR) (p < 0.0001), maximum serum creatinine during hospitalization (p < 0.0001), higher fractional excretion of sodium (FENa) (p < 0.0003) and 24-h urine protein (p = 0.005), higher serum potassium on admission (p = 0.025), abnormal levels of procalcitonin (p = 0.006), and increased risk of death (p = 0.015). Conclusion: Chronic kidney disease (CKD), lower eGFR, higher levels of serum creatinine during hospitalization, higher FENa and 24-h urine protein, abnormal levels of procalcitonin, and higher serum potassium on admission were associated with nonremitting AKI. These findings may facilitate the rapid identification of patients at risk for nonremitting AKI based on clinical and biochemical characteristics. Furthermore, these findings may inform the design of timely strategies for the vigilance, prevention, and treatment of AKI.

Acute Kidney Injury and Renal Replacement Therapy: A Review and Update for the Perioperative Physician

Post-operative acute kidney injury is a devastating complication with significant morbidity and mortality associated with it. The perioperative anesthesiologist is in a unique position to potentially mitigate the risk of postoperative AKI, however, understanding the pathophysiology, risk factors and preventative strategies is paramount. There are also certain clinical scenarios, where renal replacement therapy may be indicated intraoperatively including severe electrolyte abnormalities, metabolic acidosis and massive volume overload. A multidisciplinary approach including the nephrologist, critical care physician, surgeon and anesthesiologist is necessary to determine the optimal management of these critically ill patients.

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

Low preoperative serum fibrinogen level is associated with postoperative acute kidney injury in patients with in acute aortic dissection

OBJECTIVE

Acute kidney injury (AKI) after cardiac surgery is associated with serious complication and high risk of mortality. The relationship between hemostatic system and the prognosis of patients with acute type A aortic dissection (ATAAD) has not been evaluated. The purpose of this study was to investigate the association between preoperative serum fibrinogen level and risk of postoperative AKI in patients with ATAAD.

METHODS

A total of 172 consecutive patients undergoing urgent aortic arch surgery for ATAAD between April 2020 and December 2021 were identified from Beijing Anzhen Hospital aortic surgery database. The primary outcome was postoperative AKI as defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria. The univariate and multivariate logistic regression analysis were done to assess the independent predictors of risk for postoperative AKI. Receiver operating characteristic (ROC) curve was generated to evaluate the predictive probabilities of risk factors for AKI.

RESULTS

In our study, 51.2% (88/172) patients developed postoperative AKI. Multivariate logistic regression analysis identified low preoperative serum fibrinogen level (OR, 1.492; 95% CI, 1.023 to 2.476; p = 0.021) and increased body mass index (BMI) (OR, 1.153; 95% CI, 1.003 to 1.327; p = 0.046) as independent predictors of postoperative AKI in patients with ATAAD. A mixed effect analysis of variance modeling revealed that obese patients with low preoperative serum fibrinogen level had higher incidence of postoperative AKI (p = 0.04). The ROC curve indicated that low preoperative serum fibrinogen level was a significant predictor of AKI [area under the curve (AUC), 0.771; p < 0.001].

CONCLUSIONS

Low preoperative serum fibrinogen level and obesity were associated with the risk of postoperative AKI in patients with ATAAD. These data suggested that low preoperative serum fibrinogen level was preferred marker for predicting the postoperative AKI, especially in obese patients with ATAAD.

Association between early, small creatinine elevation and severe acute kidney injury in critically ill adult patients

BACKGROUND

Early prediction of AKI is crucial for critically ill patients. We investigated the association between small increase in creatinine and subsequent severe AKI in ICU patients.

METHODS

We conducted this retrospective cohort with a multi-institutional database between 2007 and 2019. We included adult patients admitted to the ICU with creatinine changes that did not meet the criteria for AKI diagnosis within 48 h of ICU admission. The outcomes were stage 2 or 3 AKI, kidney replacement therapy, and mortality.

RESULTS

We identified 44,805 patients and divided them into 3 groups by baseline creatinine levels: <1 mg/dL, 1 to 2 mg/dL, and ≥ 2 mg/dL. Compared with patients with higher baseline creatinine levels, patients with normal baseline creatinine levels had fewer comorbidities and less severe condition at ICU admission. The odds ratios of their outcomes increased exponentially with creatinine elevation within the first 48 h of ICU admission. The increasing odds ratios were more prominent in patients with normal baseline creatinine (P for interaction <0.001).

CONCLUSION

Small creatinine elevation within the first 48 h of ICU admission was strongly associated with the AKI, kidney replacement therapy, and death. This association was more prominent in patients with normal baseline creatinine.

Aminoglycosides use has a risk of acute kidney injury in patients without prior chronic kidney disease

The outcome of acute kidney injury (AKI) as a result of aminoglycosides (AGs) use remains uncertain in patients without prior chronic kidney disease (CKD). Therefore, we explored the outcomes of AGs use on AKI episodes associated with renal recovery and progress in patients without prior CKD in Taiwan. This was a retrospective cohort study by using the Taipei Medical University Research Database from January 2008 to December 2019. 43,259 individuals without CKD who had received parenteral AGs were enrolled. The exposed and unexposed groups underwent propensity score matching for age, gender, patients in intensive care unit/emergency admission, and covariates, except serum hemoglobin and albumin levels. We identified an exposed group of 40,547 patients who used AGs (median age, 54.4 years; 44.3% male) and an unexposed group of 40,547 patients without AG use (median age, 55.7 years; 45.5% male). There was the risk for AKI stage 1 (adjusted hazard ratio [HR] 1.34; 95% confidence interval [CI] 1.00-1.79; p = 0.05) in patients that used AGs in comparison with the control subjects. Moreover, patients using AGs were significantly associated neither with the progression to acute kidney disease (AKD) stages nor with the progression to end-stage renal disease (ESRD) on dialysis. Further analyzed, there was an increased risk of AKI episodes for serum albumin levels less than 3.0 g/dL and hemoglobin levels less than 11.6 g/dL. Among patients without prior CKD, AGs-used individuals were associated with AKI risks, especially those at relatively low albumin (< 3.0 g/dL) or low hemoglobin (< 11.6 g/dL). That could raise awareness of AGs prescription in those patients in clinical practice.

Establishment and external validation of an online dynamic nomogram for predicting in-hospital death risk in sepsis-associated acute kidney disease

OBJECTIVES

Approximately one-third of patients with sepsis-associated acute kidney injury (AKI) progress to acute kidney disease (AKD) with higher short-term mortality. We aimed to identify the clinical characteristics that influence in-hospital death in sepsis-associated AKD and develop a nomogram to facilitate early warning.

METHODS

Logical regression was applied to screen variables based on clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. A nomogram was established to predict in-hospital death risk in patients with sepsis-associated AKD. The eICU Collaborative Research Database (eICU-CRD) was used for external validation. The receiver operating characteristic and calibration curves were used to determine the model's performance.

RESULTS

A total of 1,779 patients with sepsis-associated AKD were included from the MIMIC-IV and 344 from the eICU-CRD. Age, Glasgow coma scale score, systolic blood pressure, peripheral oxygen saturation, platelet count, white blood cell count, and bicarbonate levels were significantly correlated with death. The nomogram demonstrated high discrimination in the training (C-index, 0.829; 95% confidence interval [CI] [0.807-0.852]) and testing sets (C-index: 0.760; 95% CI [0.706-0.814]). At the optimal cut-off value of 0.270, the model's sensitivity in the training and validation datasets was 72.8% (95% CI [68.3-76.9%]) and 64.5% (95% CI [54.9-73.4%]), while the specificity was 79.2% (95% CI [76.9-81.4%]) and 74.8% (95% CI [68.7-80.2%]), respectively.

CONCLUSION

We identified seven predictors of in-hospital death in patients with sepsis-associated AKD. In addition, we developed an online dynamic nomogram to accurately and conveniently predict short-term outcomes, which performed well in the external dataset.

Nutrition support for acute kidney injury 2020-consensus of the Taiwan AKI task force

BACKGROUND

We used evidence-based medicine to suggest guidelines of nutritional support for Taiwanese patients with acute kidney injury (AKI).

METHODS

Our panel reviewed the medical literature in group meetings to reach a consensus on answering clinical questions related to the effects of the nutritional status, energy/protein intake recommendations, timing of enteral, and parenteral nutrition supplementation.

RESULTS

Markers of the nutritional status of serum albumin, protein intake, and nitrogen balance had positive relationships with low mortality. A forest plot of the comparison of mortality between a body mass index (BMI) of <18.5 and ≥18.5 kg/m2 was produced using data from seven observational studies which showed that a lower BMI was associated with higher mortality. The energy recommendation of 20-30 kcal/kg body weight (BW)/day was determined to be valid for all stages of AKI. The protein recommendation for noncatabolic AKI patients is 0.8-1.0 g/kg BW/day, and 1.2-2.0 g/kg BW/day is the same as that for the underlying disease that is causing AKI. Protein intake should be at least 1.5 g/kg BW/day and up to 2.5 g/kg BW/day in patients receiving continuous renal replacement therapy. Considering that patients with AKI often have other critical comorbid situations, early enteral nutrition (EN) is suggested, and parenteral nutrition is needed when >60% energy and protein requirements cannot be met via the enteral route in 7-10 days. Low energy intake is suggested in critically ill patients with AKI, which should gradually be increased to meet 80%-100% of the energy target.

CONCLUSION

By examining evidence-based research, we provide practicable nutritional guidelines for AKI patients.

Epigenetic restoration of endogenous Klotho expression alleviates acute kidney injury-diabetes comorbidity

AIMS

The present study aimed at exploring the mechanisms behind Klotho regulation in hyperglycemia augmented AKI. In addition, epigenetic ways to restore the Klotho expression in AKI-diabetes comorbidity have been evaluated.

MAIN METHODS

Bilateral ischemia-reperfusion injury (IRI) and chemical hypoxia-reperfusion injury (HRI) were developed in diabetic rats and, NRK52E cells under high glucose conditions respectively, to mimic the AKI condition. Plasma, urine, tubular lysate of the kidney and NRK52E cell lysate were used for biochemical, ELISA, histology, immunoblotting, RT-PCR and RNA interference studies.

KEY FINDINGS

Hyperglycemia significantly aggravated IRI/HRI induced AKI as evidenced by biochemical and histological results. We also observed a significant increase in expressions of kidney specific histone deacetylases (HDACs), apoptotic and inflammatory proteins, and decrease in levels of endogenous Klotho, H3K9Ac and H3K27Ac proteins in hyperglycemic IRI/HRI groups.

SIGNIFICANCE

Diabetes comorbidity exaggerates AKI, where endogenous Klotho loss could be a potential connecting link. However, kidney-specific HDACs inhibition showed reno-protection via restoring the endogenous Klotho loss and thus prevention of inflammation and apoptosis, which could prove to be a potential therapeutic strategy against diabetes-AKI comorbidity.

Association Between Iron Metabolism and Acute Kidney Injury in Critically Ill Patients With Diabetes

OBJECTIVE

Iron overload plays an important role in the pathogenesis of diabetes and acute kidney injury (AKI). The aim of this present study was to explore the relationship between iron metabolism and AKI in patients with diabetes.

METHODS

The clinical data of diabetes patients from MIMIC-III database in intensive care unit (ICU) were retrospectively analyzed. Regression analyses were used to explore the risk factors of AKI and all-cause death in critical patients with diabetes. Area under the receiver operating characteristic curves (AUROCs) were used to analyze serum ferritin (SF), and regression model to predict AKI in critical patients with diabetes. All diabetes patients were followed up for survival at 6 months, and Kaplan-Meier curves were used to compare the survival rate in patients with different SF levels.

RESULTS

A total of 4,997 diabetic patients in ICU were enrolled, with a male-to-female ratio of 1.37:1 and a mean age of 66.87 ± 12.74 years. There were 1,637 patients in the AKI group (32.8%) and 3,360 patients in the non-AKI group. Multivariate logistic regression showed that congestive heart failure (OR = 2.111, 95% CI = 1.320-3.376), serum creatinine (OR = 1.342, 95% CI = 1.192-1.512), Oxford Acute Severity of Illness Score (OR = 1.075, 95% CI = 1.045-1.106), increased SF (OR = 1.002, 95% CI = 1.001-1.003), and decreased transferrin (OR = 0.993, 95% CI = 0.989-0.998) were independent risk factors for AKI in critical patients with diabetes. Multivariate Cox regression showed that advanced age (OR = 1.031, 95% CI = 1.025-1.037), AKI (OR = 1.197, 95% CI = 1.011-1.417), increased Sequential Organ Failure Assessment score (OR = 1.055, 95% CI = 1.032-1.078), and increased SF (OR = 1.380, 95% CI = 1.038-1.835) were independent risk factors for 6-month all-cause death in critical diabetic patients. The AUROCs of SF and the regression model to predict AKI in critical patients with diabetes were 0.782 and 0.851, respectively. The Kaplan-Meier curve showed that the 6-month survival rate in SF-increased group was lower than that in SF-normal group (log-rank χ² = 16.989, P < 0.001).

CONCLUSION

Critically ill diabetic patients with AKI were easily complicated with abnormal iron metabolism. Increase of SF is an important risk factor for AKI and all-cause death in critically ill patients with diabetes.

Influence of Red Blood Cell Distribution Width on All-Cause Death in Critical Diabetic Patients with Acute Kidney Injury

OBJECTIVE

To explore the relationship between red blood cell distribution width (RDW) and all-cause death in critical diabetic patients with acute kidney injury (AKI).

METHODS

The clinical data of critical diabetic patients with AKI in MIMIC-III database were analyzed retrospectively. According to the survival status of 28-day after AKI and levels of RDW, patients were divided into survival and death groups, high RDW (RDW > 15.3%) and low RDW groups (RDW ≤ 15.3%). Kaplan-Meier curves were used to compare the survival rates of diabetic patients with AKI in different RDW and AKI stages, and Cox regression analysis was used to evaluate the risk factors of 28-day all-cause death in critical diabetic patients with AKI.

RESULTS

A total of 5200 patients with critical diabetic patients with AKI were included in this study with the male to female ratio of 1.53:1. The mean follow-up time was 24.97 ± 7.14 days, and the 28-day all-cause mortality was 17.9% (931/5200). Age, RDW, blood urea nitrogen, serum creatinine, lactic acid, proportion of AKI stage, sepsis and respiratory failure in the death group were higher than those in the survival group, while mean arterial pressure (MAP) and red blood cell count were lower than those in the survival group. Kaplan-Meier analysis showed that the 28-day survival rate of the high RDW group was significantly lower than that of the low RDW group (log-rank χ ² = 9.970, P = 0.002). Multivariate Cox regression analysis showed that advanced age (HR = 1.042, 95% CI = 1.021-1.063), decreased MAP (HR = 0.984, 95% CI = 0.969-0.998), stage 3 AKI (HR = 3.318, 95% CI = 1.598-6.890) and increased RDW (HR = 1.255, 95% CI = 1.123-1.403) were independent risk factors of 28-day all-cause death in critical diabetic patients with AKI (P < 0.05).

CONCLUSION

High level of RDW is an important risk factor of all-cause death in critical diabetic patients with AKI, and it may be used as a valuable index to classify the mortality.