AKI and Genetics: Evolving Concepts in the Genetics of Acute Kidney Injury: Implications for Pediatric AKI.
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ABSTRACT:
SUBMITTER: Lee-Son K
PROVIDER: S-EPMC4918705 | biostudies-literature | 2016 Mar
REPOSITORIES: biostudies-literature
Publications
AKI and Genetics: Evolving Concepts in the Genetics of Acute Kidney Injury: Implications for Pediatric AKI.
Lee-Son Kathy K Jetton Jennifer G JG
Journal of pediatric genetics 20150813 1
In spite of recent advances in the field of acute kidney injury (AKI) research, morbidity and mortality remain high for AKI sufferers. The study of genetic influences in AKI pathways is an evolving field with potential for improving outcomes through the identification of risk and protective factors at the individual level that may in turn allow for the development of rational therapeutic interventions. Studies of single nucleotide polymorphisms, individual susceptibility to nephrotoxic medicatio ...[more]
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Project description:BackgroundAcute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.MethodsEHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication".ResultsThe model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI.ConclusionsAs the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.
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2013-09-05 | E-GEOD-44925 | biostudies-arrayexpress
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Project description:PTBA has been published to increase renal tubular cell proliferation, increased survival, and increased renal functional recovery in fish and various models of murine models of acute kidney injury. Immunohistological analyses suggested increased cell proliferation is accompanied by increased epithelial-to-mesenchymal transition in the RTECs. In order to elucidate pathways responsible for the increased repair response after compound treatment, larval zebrafish were given AKI and treated with PTBA analogue, UPHD25 or DMSO. Results suggests that epithelial-related genes were downregulated while mesenchymal-related genes were upregulated with injury and compound treatment. Results further validate our immunohistological finding that our compound increase post-AKI repair by increasing EMT in renal tubular cells.
2019-03-25 | GSE126418 | GEO
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Project description:Acute kidney injury (AKI) is a significant global health concern. The primary causes of AKI include ischemia, sepsis and nephrotoxicity. The unraveled interface between nervous system and immune response with specific focus on pain pathways is generating a huge interest in reference to AKI. The nervous system though static executes functions by nerve fibers throughout the body. Neuronal peptides released by nerves effect the immune response to mediate the hemodynamic system critical to the functioning of kidney. Pain is the outcome of cellular cross talk between nervous and immune systems. The widespread release of neuropeptides, neurotransmitters and immune cells contribute to bidirectional neuroimmune cross talks for pain manifestation. Recently, we have reported pain pathway genes that may pave the way to better understand such processes during AKI. An auxiliary understanding of the functions and communications in these systems will lead to novel approaches in pain management and treatment through the pathological state, specifically during acute kidney injury.
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