Acute kidney disease in hospitalized acute kidney injury patients

Heterogeneity in the definition of major adverse kidney events: a scoping review

Acute kidney injury (AKI) is associated with persistent renal dysfunction, the receipt of dialysis, dialysis dependence, and mortality. Accordingly, the concept of major adverse kidney events (MAKE) has been adopted as an endpoint for assessing the impact of AKI. However, applied criteria or observation periods for operationalizing MAKE appear to vary across studies. To evaluate this heterogeneity for MAKE evaluation, we performed a systematic scoping review of studies that employed MAKE as an AKI endpoint. Four major academic databases were searched, and we identified 122 studies with increasing numbers over time. We found marked heterogeneity in applied criteria and observation periods for MAKE across these studies, with some even lacking a description of criteria. Moreover, 13 different observation periods were employed, with 30 days and 90 days as the most common. Persistent renal dysfunction was evaluated by estimated glomerular filtration rate (34%) or serum creatinine concentration (48%); however, 37 different definitions for this component were employed in terms of parameters, cut-off criteria, and assessment periods. The definition for the dialysis component also showed significant heterogeneity regarding assessment periods and duration of dialysis requirement (chronic vs temporary). Finally, MAKE rates could vary by 7% [interquartile range: 1.7-16.7%] with different observation periods or by 36.4% with different dialysis component definitions. Our findings revealed marked heterogeneity in MAKE definitions, particularly regarding component assessment and observation periods. Dedicated discussion is needed to establish uniform and acceptable standards to operationalize MAKE in terms of selection and applied criteria of components, observation period, and reporting criteria for future trials on AKI and related conditions.

Acute Kidney Disease following Acute Kidney Injury in Children-A Retrospective Observational Cohort Study on Risk Factors and Outcomes

Background: Acute kidney disease (AKD) is a known risk factor for increased mortality and evolution towards chronic kidney disease (CKD) in adults. The data regarding AKD in children are scarce. The purpose of our study was to explore the risk factors for developing AKD based on exposures and susceptibilities in children with AKI doubled by the biological parameters from the first day of identified AKI. In addition, we followed the trajectory of AKD following an acute kidney injury (AKI) episode in children during hospital admission and after discharge with special considerations towards mortality and progression to new-onset CKD. Methods: We retrospectively evaluated 736 children, ages between 2 and 18 years old, with identified AKI during hospital admission in a tertiary care hospital from west Romania over a 9-year period. Results: AKD incidence following an AKI episode was 17%. Patients who developed AKD were older, with higher baseline serum creatinine, urea, C reactive protein and lower proteins, haemoglobin and sodium levels. In the adjusted model, no biological parameters influenced AKD development. Regarding certain exposures and personal susceptibilities in children with AKI, only anaemia independently increased the risk of AKD development by 2.47 times. However, out of the AKI causes, only the intrinsic causes of AKI independently increased the risk of progressing to AKD (glomerulonephritis by 4.94 and acute tubule-interstitial nephritis by 2.76 times). AKD increased the overall mortality by 2.6 times. The factors that independently increased the risk of CKD were AKD, acute tubular necrosis and higher baseline serum creatinine values. Conclusions: Only anaemia, glomerulonephritis and acute tubule-interstitial nephritis increased the risk of AKD development in children with AKI. AKD was an independent risk factor for mortality and new-onset CKD in children.

Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy

PURPOSE

Acute kidney disease (AKD) is believed to be involved in the transition from acute kidney injury (AKI) to chronic kidney disease in general populations, but little is understood about this possibility among kidney surgical populations. This study aimed to elucidate the incidence of AKD after partial nephrectomy and risk factors that promote the AKI to AKD transition.

METHODS

From January 2010 to January 2020, this study retrospectively collected a dataset of consecutive patients with renal masses undergoing partial nephrectomy in 4 urological centers. Cox proportional regression analyses were adopted to identify risk factors that promoted the AKI to AKD transition. To avoid overfitting, the results were then verified by logistic least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then constructed and validated for AKI to AKD transition prediction.

RESULTS

AKI and AKD occurred in 228 (21.4%) and 42 (3.9%) patients among a total of 1062 patients, respectively. In patients with AKI, multivariable Cox regression analysis and LASSO regression identified that age (HR 1.078, 1.029-1.112, p < 0.001), baseline eGFR (HR 1.015, 1.001-1.030, p < 0.001), RENAL score (HR1.612, 1.067-2.437, p = 0.023), ischemia time > 30 min (HR 7.284, 2.210-23.999, p = 0.001), and intraoperative blood loss > 300ml (HR 8.641, 2.751-27.171, p < 0.001) were risk factors for AKD transition. These five risk factors were then integrated into a nomogram. The nomogram showed excellent discrimination, calibration, and clinical net benefit ability.

CONCLUSION

Around 3.9% patients following partial nephrectomy would transit from AKI to AKD. Intraoperative blood loss and ischemia time need to be diminished to avoid on-going functional decline. Our nomogram can accurately predict the transition from AKI to AKD.

Mitochondrial quality control in acute kidney disease

Acute kidney disease (AKD) involves multiple pathogenic mechanisms,  including maladaptive repair of renal cells that are rich in mitochondria. Maintenance of mitochondrial homeostasis and quality control is crucial for normal kidney function. Mitochondrial quality control serves to maintain mitochondrial function under various conditions, including mitochondrial bioenergetics, mitochondrial biogenesis, mitochondrial dynamics (fusion and fission) and mitophagy. To date, increasing evidence indicates that mitochondrial quality control is disrupted when acute kidney disease develops. This review describes the mechanisms of mitochondria quality control in acute kidney disease, aiming to provide clues to help design new clinical treatments.

Development and validation of a nomogram for predicting in-hospital mortality of elderly patients with persistent sepsis-associated acute kidney injury in intensive care units: a retrospective cohort study using the MIMIC-IV database

OBJECTIVES

To identify the clinical risk factors that influence in-hospital mortality in elderly patients with persistent sepsis-associated acute kidney injury (S-AKI) and to establish and validate a nomogram to predict in-hospital mortality.

DESIGN

Retrospective cohort analysis.

SETTING

Data from critically ill patients at a US centre between 2008 and 2021 were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database (V.1.0).

PARTICIPANTS

Data from 1519 patients with persistent S-AKI were extracted from the MIMIC-IV database.

PRIMARY OUTCOME

All-cause in-hospital death from persistent S-AKI.

RESULTS

Multiple logistic regression revealed that gender (OR 0.63, 95% CI 0.45-0.88), cancer (2.5, 1.69-3.71), respiratory rate (1.06, 1.01-1.12), AKI stage (2.01, 1.24-3.24), blood urea nitrogen (1.01, 1.01-1.02), Glasgow Coma Scale score (0.75, 0.70-0.81), mechanical ventilation (1.57, 1.01-2.46) and continuous renal replacement therapy within 48 hours (9.97, 3.39-33.9) were independent risk factors for mortality from persistent S-AKI. The consistency indices of the prediction and the validation cohorts were 0.780 (95% CI: 0.75-0.82) and 0.80 (95% CI: 0.75-0.85), respectively. The model's calibration plot suggested excellent consistency between the predicted and actual probabilities.

CONCLUSIONS

This study's prediction model demonstrated good discrimination and calibration abilities to predict in-hospital mortality of elderly patients with persistent S-AKI, although it warrants further external validation to verify its accuracy and applicability.

Risk factors and 180-day mortality of acute kidney disease in critically ill patients: A multi-institutional study

Background

Critically ill patients with acute kidney injury (AKI) have a poor prognosis. Recently, the Acute Disease Quality Initiative (ADQI) proposed to define acute kidney disease (AKD) as acute or subacute damage and/or loss of kidney function post AKI. We aimed to identify the risk factors for the occurrence of AKD and to determine the predictive value of AKD for 180-day mortality in critically ill patients.

Methods

We evaluated 11,045 AKI survivors and 5,178 AKD patients without AKI, who were admitted to the intensive care unit between 1 January 2001 and 31 May 2018, from the Chang Gung Research Database in Taiwan. The primary and secondary outcomes were the occurrence of AKD and 180-day mortality.

Results

The incidence rate of AKD among AKI patients who did not receive dialysis or died within 90 days was 34.4% (3,797 of 11,045 patients). Multivariable logistic regression analysis indicated that AKI severity, underlying early CKD, chronic liver disease, malignancy, and use of emergency hemodialysis were independent risk factors of AKD, while male gender, higher lactate levels, use of ECMO, and admission to surgical ICU were negatively correlated with AKD. 180-day mortality was highest among AKD patients without AKI during hospitalization (4.4%, 227 of 5,178 patients), followed by AKI with AKD (2.3%, 88 of 3,797 patients) and AKI without AKD (1.6%, 115 of 7,133 patients). AKI with AKD had a borderline significantly increased risk of 180-day mortality (aOR 1.34, 95% CI 1.00-1.78; p = 0.047), while patients with AKD but no preceding AKI episodes had the highest risk (aOR 2.25, 95% CI 1.71-2.97; p < 0.001).

Conclusion

The occurrence of AKD adds limited additional prognostic information for risk stratification of survivors among critically ill patients with AKI but could predict prognosis in survivors without prior AKI.

Outcomes associated with acute kidney disease: A systematic review and meta-analysis

BACKGROUND

Acute kidney disease (AKD) defines the period after kidney damage and it is a critical period of both repair and fibrotic pathways. However, the outcomes of patients with AKD have not been well-defined.

METHODS

In this meta-analysis, PubMed, Embase, Cochrane and China National Knowledge Infrastructure were searched on July 31,2022. We excluded studies including patients undergoing kidney replacement therapy at enrollment. The data was used to conduct a random-effects model for pool outcomes between patients with AKD and non-AKD (NKD). This study is registered with PROSPERO, CRD 42021271773.

FINDINGS

The search generated 739 studies of which 21 studies were included involving 1,114,012 patients. The incidence rate of community-acquired AKD was 4.60%, 2.11% in hospital-acquired AKD without a prior AKI episode, and 26.11% in hospital-acquired AKD with a prior AKI episode. The all-cause mortality rate was higher in the AKD group (26.54%) than in the NKD group (7.78%) (odds ratio [OR]: 3.62, 95% confidence interval [CI]: 2.64 to 4.95, p < 0.001, I² = 99.11%). The rate of progression to end-stage kidney disease (ESKD) was higher in the AKD group (1.3%) than in the NKD group (0.14%) (OR: 6.58, p < 0.001, I² = 94.95%). The incident rate of CKD and progressive CKD was higher in the AKD group (37.2%) than in the NKD group (7.45%) (OR:4.22, p < 0.001, I² = 96.67%). Compared to the NKD group, patients with AKD without prior AKI had a higher mortality rate (OR: 3.00, p < 0.001, I² = 99.31%) and new-onset ESKD (OR:4.96, 95% CI, p = 0.002, I² = 97.37%).

INTERPRETATION

AKD is common in community and hospitalized patients who suffer from AKI and also occurs in patients without prior AKI. The patients with AKD, also in those without prior AKI had a higher risk of mortality, and new-onset ESKD than the NKD group.

FUNDING

This study was supported by Ministry of Science and Technology (MOST) of the Republic of China (Taiwan) [grant number, MOST 107-2314-B-002-026-MY3, 108-2314-B-002-058, 110-2314-B-002-241, 110-2314-B-002-239], National Science and Technology Council (NSTC) [grant number, NSTC 109-2314-B-002-174-MY3, 110-2314-B-002-124-MY3, 111-2314-B-002-046, 111-2314-B-002-058], National Health Research Institutes [PH-102-SP-09], National Taiwan University Hospital [109-S4634, PC-1246, PC-1309, VN109-09, UN109-041, UN110-030, 111-FTN0011] Grant MOHW110-TDU-B-212-124005, Mrs. Hsiu-Chin Lee Kidney Research Fund and Chi-mei medical center CMFHR11136. JAN is supported, in part, by grants from the National Institute of Health, NIDDK (R01 DK128208 and P30 DK079337) and NHLBI (R01 HL148448-01).

Mechanism study of Cordyceps sinensis alleviates renal ischemia–reperfusion injury

Cordyceps sinensis (C. sinensis) is a kind of traditional Chinese medicine commonly used to protect renal function and relieve kidney injury. This study aimed to reveal the renal protective mechanism of C. sinensis in renal ischemia–reperfusion injury (RIRI). First, we obtained 8 active components and 99 common targets of C. sinensis against RIRI from public databases. Second, we have retrieved 38 core targets through STRING database analysis. Third, Gene Ontology analysis of 38 core targets is indicated that C. sinensis treatment RIRI may related hormone regulation, oxidative stress, cell proliferation, and immune regulation. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of 38 core targets is indicated that C. sinensis treatment RIRI may involve in PI3K–Akt, HIF-1, and MAPK signaling pathways, as well as advanced glycation end product (AGE)–receptor for AGE (RAGE) signaling pathway in diabetic complications. Lastly, molecular docking was used to detect the binding activity and properties of active components and core target using molecular docking. And the results showed that eight active components of C. sinensis had low affinity with core targets. In conclusion, C. sinensis may improve RIRI by regulating oxidative stress and immunity through PI3K–Akt, HIF-1, and MAPK pathways.

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.

Prediction of Mortality Risk After Ischemic Acute Kidney Injury With a Novel Prognostic Model: A Multivariable Prediction Model Development and Validation Study

Background and Objectives:

Acute kidney injury (AKI) that results from ischemia is a common clinical syndrome and correlates with high morbidity and mortality among hospitalized patients. However, a clinical tool to predict mortality risk of ischemic AKI is not available. In this study, we aimed to develop and validate models to predict the 30-day and 1-year mortality risk of hospitalized patients with ischemic AKI.

Methods

A total of 1,836 admissions with ischemic AKI were recruited from 277,898 inpatients admitted to three affiliated tertiary general hospitals of Central South University in China between January 2015 and December 2015. Patients in the final analysis were followed up for 1 year. Study patients were randomly divided in a 7:3 ratio to form the training cohort and validation cohort. Multivariable regression analyses were used for developing mortality prediction models.

Results

Hepatorenal syndrome, shock, central nervous system failure, Charlson comorbidity index (≥2 points), mechanical ventilation, renal function at discharge were independent risk factors for 30-day mortality after ischemic AKI, while malignancy, sepsis, heart failure, liver failure, Charlson comorbidity index (≥2 points), mechanical ventilation, and renal function at discharge were predictors for 1-year mortality. The area under the receiver operating characteristic curves (AUROCs) of 30-day prediction model were 0.878 (95% confidence interval (CI): 0.849-0.908) in the training cohort and 0.867 (95% CI: 0.820–0.913) in the validation cohort. The AUROCs of the 1-year mortality prediction in the training and validation cohort were 0.803 (95% CI: 0.772–0.834) and 0.788 (95% CI: 0.741–0.835), respectively.

Conclusion

Our easily applied prediction models can effectively identify individuals at high mortality risk within 30 days or 1 year in hospitalized patients with ischemic AKI. It can guide the optimal clinical management to minimize mortality after an episode of ischemic AKI.

Outcome prediction for acute kidney injury among hospitalized children via eXtreme Gradient Boosting algorithm

Acute kidney injury (AKI) is common among hospitalized children and is associated with a poor prognosis. The study sought to develop machine learning-based models for predicting adverse outcomes among hospitalized AKI children. We performed a retrospective study of hospitalized AKI patients aged 1 month to 18 years in the Second Xiangya Hospital of Central South University in China from 2015 to 2020. The primary outcomes included major adverse kidney events within 30 days (MAKE30) (death, new renal replacement therapy, and persistent renal dysfunction) and 90-day adverse outcomes (chronic dialysis and death). The state-of-the-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), and the traditional logistic regression were used to establish prediction models for MAKE30 and 90-day adverse outcomes. The models’ performance was evaluated by split-set test. A total of 1394 pediatric AKI patients were included in the study. The incidence of MAKE30 and 90-day adverse outcomes was 24.1% and 8.1%, respectively. In the test set, the area under the receiver operating characteristic curve (AUC) of the XGBoost model was 0.810 (95% CI 0.763–0.857) for MAKE30 and 0.851 (95% CI 0.785–0.916) for 90-day adverse outcomes, The AUC of the logistic regression model was 0.786 (95% CI 0.731–0.841) for MAKE30 and 0.759 (95% CI 0.654–0.864) for 90-day adverse outcomes. A web-based risk calculator can facilitate the application of the XGBoost models in daily clinical practice. In conclusion, XGBoost showed good performance in predicting MAKE30 and 90-day adverse outcomes, which provided clinicians with useful tools for prognostic assessment in hospitalized AKI children.

Acute Kidney Disease in Hospitalized Pediatric Patients With Acute Kidney Injury in China

Objective

The epidemiology and outcomes of acute kidney disease (AKD) after acute kidney injury (AKI) in hospitalized children are poorly described. The aim of this study is to investigate the prevalence, predictive factors, and clinical outcomes of AKD in hospitalized children with AKI.

Methods

Children (1 month-18 years) with AKI during hospitalization in the Second Xiangya Hospital from January 2015 to December 2020 were identified. AKD was defined based on the consensus report of the Acute Disease Quality Initiative 16 workgroup. The endpoints include adverse outcomes in 30 and 90 days. Multivariable logistic regression analyses were used to estimate the odds ratio of 30- and 90-day adverse outcomes associated with AKD and identify the risk factors of AKD.

Results

AKD was developed in 42.3% (419/990) of the study patients, with 186 in AKD stage 1, 107 in AKD stage 2, and 126 in AKD stage 3. Pediatric patients with AKD stages 2-3 had significantly higher rates of developing 30- and 90-day adverse outcomes than those with AKD stage 0 and 1. The adjusted odds ratio of AKD stage 2-3 was 12.18 (95% confidence interval (CI), 7.38 - 20.09) for 30-day adverse outcomes and decreased to 2.49 (95% CI, 1.26 - 4.91) for 90-day adverse outcomes. AKI stages 2 and 3, as well as glomerulonephritis, were the only predictive factors for AKD stage 2-3.

Conclusion

AKD is frequent among hospitalized pediatric AKI patients. AKD stage 2-3 represents a high-risk subpopulation among pediatric AKI survivors and is independently associated with 30- and 90-day adverse outcomes. Awareness of the potential risks associated with AKD stage 2-3 and its risk factors may help improve outcomes through careful monitoring and timely intervention.

Predictors of Acute Kidney Disease Severity in Hospitalized Patients with Acute Kidney Injury

Acute kidney disease (AKD) forms part of the continuum of acute kidney injury (AKI) and worsens clinical outcomes. Currently, the predictors of AKD severity have yet to be established. We conducted a retrospective investigation involving 310 hospitalized patients with AKI and stratified them based on the AKD stages defined by the Acute Dialysis Quality Initiative criteria. Demographic, clinical, hematologic, and biochemical profiles, as well as 30-day outcomes, were compared between subgroups. In the analysis, the use of offending drugs (odds ratio, OR (95% confidence interval, CI), AKD stage 3 vs. non-AKD, 3.132 (1.304−7.526), p = 0.011, AKD stage 2 vs. non-AKD, 2.314 (1.049−5.107), p = 0.038), high AKI severity (OR (95% CI), AKD stage 3 vs. non-AKD, 6.214 (2.658−14.526), p < 0.001), and early dialysis requirement (OR (95% CI), AKD stage 3 vs. non-AKD, 3.366 (1.008−11.242), p = 0.049) were identified as independent predictors of AKD severity. Moreover, a higher AKD severity was associated with higher 30-day mortality and lower dialysis-independent survival rates. In conclusion, our study demonstrated that offending drug use, AKI severity, and early dialysis requirement were independent predictors of AKD severity, and high AKD severity had negative impact on post-AKI outcomes.

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI. Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7-90 days according to Acute Disease Quality Initiative-16. Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD. Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon-renal plays an important role in predicting the occurrence of AKD.