Dataset Information

Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care.

ABSTRACT:

Background

Outcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting.

Methods

This observational cohort study included 7491 people referred for nephrology clinic care in British Columbia in 2003-09 followed to 2016. Predictors were previous Kidney Disease: Improving Global Outcomes-based AKI, age, sex, proteinuria, estimated glomerular filtration rate (eGFR) and renal diagnosis. Outcomes were 5-year kidney failure and death. We developed cause-specific Cox models (AKI versus no AKI) for kidney failure and death, stratified by eGFR (ResultsAKI was associated with increased kidney failure (33.1% versus 26.3%) and death (23.8% versus 16.8%) (P ?ConclusionsAKI carries a poorer long-term prognosis among those already under nephrology care. AKI may not alter kidney failure risk predictions, but the use of prediction models without appreciating the full impact of AKI, including increased mortality, would be simplistic. People with kidney diseases have risks beyond simply kidney failure. This complexity and variability of outcomes of individuals is important.

SUBMITTER: Sawhney S

PROVIDER: S-EPMC7203563 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care.

Sawhney Simon S Beaulieu Monica M Black Corri C Djurdjev Ognjenka O Espino-Hernandez Gabriela G Marks Angharad A McLernon David J DJ Sheriff Zainab Z Levin Adeera A

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 20200501 5

<h4>Background</h4>Outcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting.<h4>Methods</h4>This observational cohort study included 7491 people referred for nephrology clinic care in British Columbia in 2003-09 followed to 2016. P ...[more]

PMID: 30325464

Similar Datasets

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:BackgroundChronic kidney disease-associated pruritus (CKD-aP) is associated with an increased risk of depression, poor sleep and reduced health-related quality of life. Two phase III studies (KALM-1 and KALM-2) of difelikefalin showed reduced CKD-aP severity and improved itch-related health-related quality of life in patients with moderate and severe CKD-aP receiving haemodialysis for kidney failure.ObjectiveWe aimed to estimate the cost effectiveness of difelikefalin for patients with CKD-aP receiving haemodialysis for kidney failure compared to standard care from a UK National Health Service perspective.MethodsA cohort model was developed with four health states representing levels of pruritus intensity over time, based on the KALM trials augmented with longer term CKD-aP severity data from another haemodialysis trial (SHAREHD) for standard care. Utilities were estimated from a mapping study of 5-D Itch to EQ-5D-5L in 487 patients receiving haemodialysis, costs were estimated based on resource use alongside the SHAREHD and 2018 unit costs, and inflated to 2021 costs. Costs and quality-adjusted life-years were discounted at 3.5% per annum. A de novo economic model was developed in Microsoft Excel with scenario analyses performed using a range of assumptions.ResultsIn the base-case analysis over a time horizon of 64 weeks, using a placeholder cost of £75 per 28-days for difelikefalin, the incremental cost-effectiveness ratio of difelikefalin compared with standard care was £19,558/quality-adjusted life-year (QALY). Scenario analyses resulted in incremental cost-effectiveness ratios that ranged from £10,154/QALY (severe only) to £16,957/QALY (5-year horizon) for difelikefalin compared to standard care. Probabilistic sensitivity analyses suggested difelikefalin has a 48.6% probability of being cost effective at a threshold of £20,000/QALY and a 57.2% probability of being cost effective at a threshold of £30,000/QALY.ConclusionsThe cost effectiveness of difelikefalin in a range of scenarios could make it an important pharmacotherapy to address the high burden of disease and unmet need for treatments associated with CKD-aP in the UK.

Dataset Information

Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care.

Background

Methods

Publications

Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets