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

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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

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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:BackgroundInnovative care models are needed to address gaps in kidney care follow-up among acute kidney injury (AKI) survivors. We developed the multidisciplinary AKI in Care Transitions (ACT) program, which embeds post-AKI care in patients' primary care clinic.ObjectiveThe objective of this randomized pilot trial is to test the feasibility and acceptability of the ACT program and study protocol, including recruitment and retention, procedures, and outcome measures.MethodsThe study will be conducted at Mayo Clinic in Rochester, Minnesota, a tertiary care center with a local primary care practice. Individuals who are included have stage 3 AKI during their hospitalization, do not require dialysis at discharge, have a local primary care provider, and are discharged to their home. Patients unable or unwilling to provide informed consent and recipients of any transplant within 100 days of enrollment are excluded. Consented patients are randomized to receive the intervention (ie, ACT program) or usual care. The ACT program intervention includes predischarge kidney health education from nurses and coordinated postdischarge laboratory monitoring (serum creatinine and urine protein assessment) and follow-up with a primary care provider and pharmacist within 14 days. The usual care group receives no specific study-related intervention, and any aspects of AKI care are at the direction of the treating team. This study will examine the feasibility of the ACT program, including recruitment, randomization and retention in a trial setting, and intervention fidelity. The feasibility and acceptability of participating in the ACT program will also be examined in qualitative interviews with patients and staff and through surveys. Qualitative interviews will be deductively and inductively coded and themes compared across data types. Observations of clinical encounters will be examined for discussion and care plans related to kidney health. Descriptive analyses will summarize quantitative measures of the feasibility and acceptability of ACT. Participants' knowledge about kidney health, quality of life, and process outcomes (eg, type and timing of laboratory assessments) will be described for both groups. Clinical outcomes (eg, unplanned rehospitalization) up to 12 months will be compared with Cox proportional hazards models.ResultsThis study received funding from the Agency for Health Care Research and Quality on April 21, 2021, and was approved by the Institutional Review Board on December 14, 2021. As of March 14, 2023, seventeen participants each have been enrolled in the intervention and usual care groups.ConclusionsFeasible and generalizable AKI survivor care delivery models are needed to improve care processes and health outcomes. This pilot trial will test the ACT program, which uses a multidisciplinary model focused on primary care to address this gap.Trial registrationClinicalTrials.gov NCT05184894; https://www.clinicaltrials.gov/ct2/show/NCT05184894.International registered report identifier (irrid)DERR1-10.2196/48109.

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

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