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Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit.

ABSTRACT: Intradialytic hypotension often complicates haemodialysis for patients with acute kidney injury (AKI) and may impact renal recovery. Sodium modelling is sometimes used as prophylaxis against intradialytic hypotension in the chronic haemodialysis population, but there is little evidence for its use among critically ill patients with AKI.A retrospective cohort with AKI requiring intermittent haemodialysis in the intensive care unit from 2001 to 2008 was used to study the association of prophylactic sodium modelling and multiple outcomes. Outcomes included a composite of in-hospital death or dialysis dependence at hospital discharge, as well as intradialytic hypotension, ultrafiltration goal achievement and net ultrafiltration volume. Associations were estimated using logistic regression, mixed linear models and generalized estimating equations adjusting for demographic and clinical characteristics.One hundred and ninety-one individuals who underwent 892 sessions were identified; sodium modelling was prescribed in 27.1% of the sessions. In adjusted analyses, sodium modelling was not significantly associated with intradialytic hypotension (P?=?0.67) or with the ultrafiltration goal achievement (P?=?0.06). Sodium modelling during the first dialysis session was numerically associated with lower risk for the composite of in-hospital death or dialysis dependence: adjusted odds ratio (95% confidence interval) 0.39 (0.15-1.02; P?=?0.06); however, this association did not reach statistical significance.We did not observe statistically significant associations between sodium modelling and improved outcomes among AKI patients receiving intermittent dialysis in the intensive care unit. However, suggestive findings warrant further study.

SUBMITTER: Lynch KE

PROVIDER: S-EPMC4875881 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit.

Lynch Katherine E KE Ghassemi Fatimah F Flythe Jennifer E JE Feng Mengling M Ghassemi Marzyeh M Celi Leo Anthony LA Brunelli Steven M SM

Nephrology (Carlton, Vic.) 20161001 10

<h4>Aim</h4>Intradialytic hypotension often complicates haemodialysis for patients with acute kidney injury (AKI) and may impact renal recovery. Sodium modelling is sometimes used as prophylaxis against intradialytic hypotension in the chronic haemodialysis population, but there is little evidence for its use among critically ill patients with AKI.<h4>Methods</h4>A retrospective cohort with AKI requiring intermittent haemodialysis in the intensive care unit from 2001 to 2008 was used to study th ...[more]

PMID: 26590371

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Project description:BackgroundIntradialytic hypotension (IDH) is a common complication of haemodialysis (HD), and a risk factor of cardiovascular morbidity and death. Several clinical studies suggested that reduction of dialysate temperature, such as fixed reduction of dialysate temperature or isothermal dialysate using a biofeedback system, might improve the IDH rate.ObjectivesThis review aimed to evaluate the benefits and harms of dialysate temperature reduction for IDH among patients with chronic kidney disease requiring HD, compared with standard dialysate temperature.Search methodsWe searched Cochrane Kidney and Transplant's Specialised Register up to 14 May 2019 through contact with the Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, EMBASE, conference proceedings, the International Clinical Trials Register (ICTRP) Search Portal, and ClinicalTrials.gov.Selection criteriaAll randomised controlled trials (RCTs), cross-over RCTs, cluster RCTs and quasi-RCTs were included in the review.Data collection and analysisTwo authors independently extracted information including participants, interventions, outcomes, methods of the study, and risks of bias. We used a random-effects model to perform quantitative synthesis of the evidence. We assessed the risks of bias for each study using the Cochrane 'Risk of bias' tool. We assessed the certainty of evidence using Grades of Recommendation, Assessment, Development and Evaluation (GRADE).Main resultsWe included 25 studies (712 participants). Three studies were parallel RCTs and the others were cross-over RCTs. Nineteen studies compared fixed reduction of dialysate temperature (below 36°C) and standard dialysate temperature (37°C to 37.5°C). Most studies were of unclear or high risk of bias. Compared with standard dialysate, it is uncertain whether fixed reduction of dialysate temperature improves IDH rate (8 studies, 153 participants: rate ratio 0.52, 95% CI 0.34 to 0.80; very low certainty evidence); however, it might increase the discomfort rate compared with standard dialysate (4 studies, 161 participants: rate ratio 8.31, 95% CI 1.86 to 37.12; very low certainty evidence). There were no reported dropouts due to adverse events. No study reported death, acute coronary syndrome or stroke.Three studies compared isothermal dialysate and thermoneutral dialysate. Isothermal dialysate might improve the IDH rate compared with thermoneutral dialysate (2 studies, 133 participants: rate ratio 0.68, 95% CI 0.60 to 0.76; I2 = 0%; very low certainty evidence). There were no reports of discomfort rate (1 study) or dropouts due to adverse events (2 studies). No study reported death, acute coronary syndrome or stroke.Authors' conclusionsReduction of dialysate temperature may prevent IDH, but the conclusion is uncertain. Larger studies that measure important outcomes for HD patients are required to assess the effect of reduction of dialysate temperature. Six ongoing studies may provide much-needed high quality evidence in the future.

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.

Dataset Information

Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit.

Publications

Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit.

Similar Datasets

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