Mortality and serum hepcidin are associated with persistent and transient acute kidney injury in septic patients

Qiu, Zhijun; Yan, Biqing; Xu, Dongwei; Zhao, Rui; Shen, Kan; Lu, Shiqi

doi:10.5414/cn110437

Cited by 6 publications

(8 citation statements)

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“…These advantages include the ability to detect novel transcripts (such as AC068768 and AC022107), lower noise signals, increased sensitivity in detecting differential expression, and the ability to quantify a large dynamic range of expression levels [ 32 – 34 ]. A machine learning model has been developed for the prediction of persistent or transient AKI using clinical data alone [ 11 , 12 ]; the model performance was moderate, with an AUC below 0.80, which was consistent with our study. Nevertheless, the gene model was able to increase accuracy by a large magnitude owing to the sensitivity of RNA-Seq to identify novel and lowly expressed genes.…”

Section: Discussionsupporting

confidence: 88%

“…KDIGO defines persistent AKI as renal dysfunction beyond 48 h from AKI onset; otherwise, AKI is considered transient [ 10 ]. The characteristics of these renal function trajectories have been described in the literature [ 9 , 11 , 12 ]. However, the current AKI definition criteria cannot fully capture AKI progression on subsequent days.…”

Section: Introductionmentioning

confidence: 99%

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Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

et al. 2022

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Background Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized. Methods Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI. Results A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong’s test). Conclusions Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

et al. 2022

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“…Methods for determining the baseline serum creatinine (sCr) varied across studies, with some using the first sCr measured within 3 days of ICU admission [14,19 ▪▪ ,27,29,31,34] and others using the lowest value during ICU admission [5 ▪▪ ,9,18]. Studies also established baseline sCr from the lowest sCr prior to admission (ranging from 365 days to 1 week prior [25 ▪▪ ,29,30,32 ▪▪ ] or within 7 days prior [24]), mean sCr from 180 to 7 days prior to admission [21 ▪▪ ,26 ▪ ], preoperative sCr [6,31] or the Modified Diet and Renal Disease (MDRD) equation if a measured sCr was not available [9,12,26 ▪ ,30]. Eleven studies did not define baseline sCr [7 ▪▪ ,10 ▪▪ ,11–13,15,20 ▪▪ ,22 ▪▪ ,23 ▪ ,28,33].…”

Section: Acute Kidney Injury Algorithmsmentioning

confidence: 99%

“…The most common source of patient data was Medical Information Mart for Intensive Care (MIMIC) III, which was used in 15 studies [5 ▪▪ ,7 ▪▪ ,10 ▪▪ ,13–15,16 ▪▪ ,17 ▪▪ ,18,19 ▪▪ –22 ▪▪ ,23 ▪ ,24]. Although most studies ( n = 16) included mixed ICU populations [5 ▪▪ ,7 ▪▪ ,8,10 ▪▪ ,13,16 ▪▪ ,17 ▪▪ ,18,19 ▪▪ –22 ▪▪ ,23 ▪ ,25 ▪▪ ,26 ▪ ,27], there were seven studies focused on sepsis patients [11,12,14,24,28–30], four focused on surgical patients [6,9,31,32 ▪▪ ], one on diabetic ketoacidosis [15], one on haemorrhagic shock [33] and one on acute brain injury [34].…”

Section: Acute Kidney Injury Algorithmsmentioning

confidence: 99%

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

Ozrazgat‐Baslanti

Loftus

Ren

et al. 2021

Current Opinion in Critical Care

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Purpose of reviewAcute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. Recent findingsAmong recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies.

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“…Because it can cause multiple organ dysfunction, sepsis has a high mortality rate, which poses a great threat to human health, especially the lives of patients in the intensive care unit (ICU) (2,3). Epidemiological statistics show that acute kidney injury (AKI) is a more common complication of sepsis with a higher mortality rate (4). However, the complicated pathophysiological mechanism of septic AKI has not been revealed, and the current status of diagnosis and treatment of septic AKI is not ideal (5,6).…”

Section: Introductionmentioning

confidence: 99%

Circ_0008882 Stimulates Pde7a to Suppress Septic Acute Kidney Injury Progression by Sponging Mir-155-5p

You

Feng

2023

Shock

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Background: The importance of circular RNA (circRNA) in the progression of septic acute kidney injury (AKI) was gradually recognized. It has been confirmed that circ_0008882 expression was decreased in the blood of patients with AKI. However, the role of circ_0008882 in septic AKI progression remains unclear. Methods: Human kidney-2 (HK2) cells were stimulated with lipopolysaccharide (LPS) to establish a septic AKI cell model. The RNA and protein expression of circ_0008882, miR-155-5p, phosphodiesterase 7A (PDE7A), PCNA, Bax, and Bcl-2 were detected by quantitative real-time polymerase chain reaction and Western blot. Cell viability was investigated by cell counting kit-8 assay. Enzyme-linked immunosorbent assay (ELISA) was adopted to measure the levels of inflammatory factors (TNF-α, IL-1β, and IL-6). Flow cytometry was implemented to evaluate cell cycle and cell apoptosis. The Caspase3 activity was examined using Caspase3 Assay Kit. Dual-luciferase reporter assay and RNA immunoprecipitation assay were applied to verify the molecular target relations. Results: Septic AKI serum samples and LPS-induced HK2 cells displayed low expression of circ_0008882 and PDE7A, and high expression of miR-155-5p when compared with the controls. Overexpression of circ_0008882 relieved LPS-induced HK2 cell injury. MiR-155-5p was a target of circ_0008882, and miR-155-5p mimic restored circ_0008882 overexpression-mediated effects on LPS-treated HK2 cells. PDE7A was identified as a target gene of miR-155-5p, and PDE7A downregulation almost reverted the improvement impacts induced by the miR-155-5p inhibitor. Conclusions: Overexpression of circ_0008882 impeded LPS-induced HK2 cell injury by modulating miR-155-5p/PDE7A pathway, implying that circ_0008882 might be a possible circRNA-targeted therapy for septic AKI.

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Mortality and serum hepcidin are associated with persistent and transient acute kidney injury in septic patients

Cited by 6 publications

References 0 publications

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

Circ_0008882 Stimulates Pde7a to Suppress Septic Acute Kidney Injury Progression by Sponging Mir-155-5p

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