Acute kidney disease in hospitalized acute kidney injury patients

Yan, Ping; Duan, Xiangjie; Liu, Yu; Wu, Xiaoxiang; Zhang, Ning-Ya; Yuan, Fang; Tang, Hao; Liu, Qian; Deng, Ying-Hao; Wang, Hongshen; Wang, Mei; Duan, Shao‐Bin

doi:10.7717/peerj.11400

Cited by 18 publications

(16 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, we depicted the clinical course of AKI – AKD – MAKE30 – 90-day adverse outcomes, aiming to provide information on the renal function progression after AKI in pediatric patients. We found that a further progression of renal injury after AKI could add the risk of 30- and 90-day adverse outcomes, which supported the results of our previous study of AKD in hospitalized adult patients with AKI ( 8 ). AKI stage 2–3, which was generally considered to be associated with increased risks of CKD, kidney failure, and mortality ( 1 , 16 – 18 ), was no longer a predictor for adverse outcomes in our multivariable logistic regression analysis.…”

Section: Discussionsupporting

confidence: 91%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

ObjectiveThe 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.MethodsChildren (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.ResultsAKD 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.ConclusionAKD 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.

show abstract

Section: Discussionsupporting

confidence: 91%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given the heterogeneous and multifactor etiology of ischemic AKI, the incidence estimates vary widely among different patient-related risk factors. The incidence of AKI in hospitalized patients is 11.6-23.9% (8,9), while the incidence of ischemic AKI of overall AKI patients is range from 55.6 to 77.8% (14)(15)(16)26), approximately. In our study, the incidence of ischemic AKI among hospitalized patients is 5.3% and 71.8% in overall AKI patients, demonstrating ischemic AKI common among hospitalized AKI patients.…”

Section: Discussionmentioning

confidence: 99%

“…When AKI occurred in COVID-19 positive patients, the proportion of death could be as high as 50% (12). Not surprisingly, some reports showed that prerenal AKI [one form of ischemic AKI (13)] accounted for more than half of the hospitalized AKI patients (14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.MethodsA 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.ResultsHepatorenal 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.ConclusionOur 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.

show abstract

“…The model displayed good predictive power with an AUROC of 0.834 (95% CI:0.773-0.895) in the training dataset and an AUROC of 0.851 (95% CI:0.753-0949) in the validation dataset. Yan et al also established a prediction model using multivariable logistic regression analysis (37). The 8-variable model showed good discrimination and calibration in predicting AKD stage 2-3 with the AUROC being 0.85 (95% CI:0.83-0.87).…”

Section: Discussionmentioning

confidence: 99%

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

Lin

Duan

2021

Front. Med.

View full text Add to dashboard Cite

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.

show abstract

Acute kidney disease in hospitalized acute kidney injury patients

Cited by 18 publications

References 41 publications

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

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

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

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

Contact Info

Product

Resources

About