Dataset Information

Frequency of TGF- ? and IFN- ? genotype as risk factors for acute kidney injury and death in intensive care unit patients.

ABSTRACT: Genetic variations in TGF-? and IFN-? may interfere with proinflammatory cytokine production and, consequently, may be involved with inflammatory diseases, as acute kidney injury (AKI). We considered that genetic polymorphisms of these cytokines may have a crucial role in the outcome of critically ill patients. To investigate whether the genetic polymorphisms of rs1800470 (codon 10 T/C), rs1800471 (codon 25 C/G) from the TGF-?, and rs2430561 (+874 T/A) from IFN-? may be a risk factor for ICU patients to the development of AKI and/or death. In a prospective nested case-control study, were included 139 ICU patients who developed AKI, 164 ICU patients without AKI, and 244 healthy individuals. We observed a higher frequency to T/A genotype for IFN-? (intermediate producer phenotype) and higher frequency of TT GG and TC GG genotype (high producer) for TGF-? polymorphism in overall population. However, these polymorphisms have not been shown as a predictor of risk for AKI and death. We found an increased prevalence of high and intermediate producer phenotypes from TGF-? and IFN-?, respectively, in patients in ICU setting. However, the studied genetic polymorphism of the TGF-? and IFN-? was not associated as a risk factor for AKI or death in our population.

SUBMITTER: Grabulosa CC

PROVIDER: S-EPMC4132405 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Frequency of TGF- β and IFN- γ genotype as risk factors for acute kidney injury and death in intensive care unit patients.

Grabulosa Caren Cristina CC Batista Marcelo Costa MC Cendoroglo Miguel M Quinto Beata Marie Redublo BM Narciso Roberto R Monte Julio Cesar JC Durão Marcelino M Rizzo Luiz Vicente LV Santos Oscar Fernando Pavão OF Dalboni Maria Aparecida MA

BioMed research international 20140723

Genetic variations in TGF-β and IFN-γ may interfere with proinflammatory cytokine production and, consequently, may be involved with inflammatory diseases, as acute kidney injury (AKI). We considered that genetic polymorphisms of these cytokines may have a crucial role in the outcome of critically ill patients. To investigate whether the genetic polymorphisms of rs1800470 (codon 10 T/C), rs1800471 (codon 25 C/G) from the TGF-β, and rs2430561 (+874 T/A) from IFN-γ may be a risk factor for ICU pat ...[more]

PMID: 25147823

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Project description:BackgroundAcute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.ObjectiveThe primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.MethodsWe developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.ResultsThe developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.ConclusionsNAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

Project description:BackgroundAcute kidney injury (AKI) is a common complication among SARS-CoV-2-positive patients who undergo hospitalization. Abundant evidence exists concerning the epidemiology of AKI in patients hospitalized in the ICU for COVID-19 but limited data are available about the occurrence of AKI in SARS-CoV-2-positive patients being hospitalized in a non-ICU setting.Aim and methodsWe have carried out a retrospective study to evaluate frequency and risk factors for AKI among patients consecutively admitted at a third-level university hospital starting from February 2020 (the beginning of the first wave of the SARS-CoV-2 pandemic); all patients were hospitalized outside the ICU.ResultsA total of 387 SARS-CoV-2-positive patients were included in the current study; 372 (96.1%) had SARS-CoV-2-related pneumonia. In-hospital AKI onset was recorded in 119 (30.7%) patients, mainly with AKI stage 1 (n = 74, 62.2%); eighteen (4.6%) patients reported AKI stage 3 and six (1.5%) patients had HD-dependent AKI. There were 235 (60.7%) patients with severe COVID-19, and this was more common in patients developing AKI, 94.5% (86/119) vs. 86.1% (149/268), p = 0.02. Multivariate regression model (n = 144 patients) reported an independent and significant relationship between AKI occurrence and greater levels of ferritin (p = 0.036), IL-6 (p = 0.032), and azotemia at admission (p = 0.0001). A total of 69 (17.8%) SARS-CoV-2-positive patients died and strong predictors of in-hospital death resulted from age (p < 0.0001), serum ferritin (p < 0.0001) and white blood cells (p < 0.001). According to multivariable analysis (n = 163 patients), there was a consistent link between in-hospital death and AKI stage (1) (p = 0.021) and -stage (2) (p = 0.009). Our results support the notion that AKI occurs frequently among hospitalized COVID-19 patients even in a non-ICU setting and plays a pivotal role in the mortality of this population. Further studies are ongoing in order to clearly establish the frequency of AKI in patients with COVID-19; the mechanisms underlying kidney injury in this population are an area of active investigation. These data provide solid evidence to support close monitoring of COVID-19 patients for the development of AKI and measures taken to prevent this.

Dataset Information

Frequency of TGF- ? and IFN- ? genotype as risk factors for acute kidney injury and death in intensive care unit patients.

Publications

Frequency of TGF- β and IFN- γ genotype as risk factors for acute kidney injury and death in intensive care unit patients.

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