ArticleClinical journal of the American Society of Nephrology : CJASN2011
Model to predict mortality in critically ill adults with acute kidney injury.
Article in Clinical journal of the American Society of Nephrology : CJASN, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.PloS one · 2017Pooled it
- Development and validation of a nomogram to predict the risk of renal replacement therapy among acute kidney injury patients in intensive care unit.Clinical and experimental nephrology · 2023Trial
- Article
- Uremic Toxins and Mortality in Patients With Acute Kidney Injury Prior to and During Continuous Kidney Replacement Therapy.Kidney medicine · 2026Article
- Organ Crosstalk During Injury: Mechanisms of Lung-Kidney Interaction in Critical Illness.Comprehensive Physiology · 2025Review
- Prognostic value of the advanced lung cancer inflammation index for 28 day mortality in sepsis associated acute kidney injury.Scientific reports · 2025Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Acute kidney injury triggers hypoxemia by lung intravascular neutrophil retention that reduces capillary blood flow.The Journal of clinical investigation · 2025Article
- Development and validation of a prediction model for 90-day mortality among critically ill patients with AKI undergoing CRRT.Journal of nephrology · 2025Article
- Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.BMC medical informatics and decision making · 2024Article
- Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy.The Journal of international medical research · 2024Article
- Development and validation of a model for predicting in-hospital mortality in patients with sepsis-associated kidney injury receiving renal replacement therapy: a retrospective cohort study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2024Article
- Predicting outcomes of acute kidney injury in critically ill patients using machine learning.Scientific reports · 2023Observational
- Risk Factors for Death Among Veterans Following Acute Kidney Injury.The American journal of medicine · 2023Article
- Article
- Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.Frontiers in medicine · 2023Article
- Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.Frontiers in immunology · 2023Article
- Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms.Annals of translational medicine · 2022Article
- Predicting Mortality Using Machine Learning Algorithms in Patients Who Require Renal Replacement Therapy in the Critical Care Unit.Journal of clinical medicine · 2022Article
- Dissipating the Fog at the Crossroad: Predicting Survival after the Initiation of Kidney Replacement Therapy.Kidney360 · 2022Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesAcute kidney injury (AKI) requiring dialysis is associated with high mortality. Most prognostic tools used to describe case complexity and to project patient outcome lack predictive accuracy when applied in patients with AKI. In this study, we developed an AKI-specific predictive model for 60-day mortality and compared the model to the performance of two generic (Sequential Organ Failure Assessment [SOFA] and Acute Physiology and Chronic Health Evaluation II [APACHE II]) scores, and a disease specific (Cleveland Clinic [CCF]) score. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data from 1122 subjects enrolled in the Veterans Affairs/National Institutes of Health Acute Renal Failure Trial Network study; a multicenter randomized trial of intensive versus less intensive renal support in critically ill patients with AKI conducted between November 2003 and July 2007 at 27 VA- and university-affiliated centers.
resultsThe 60-day mortality was 53%. Twenty-one independent predictors of 60-day mortality were identified. The logistic regression model exhibited good discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.85 (0.83 to 0.88), and a derived integer risk score yielded a value of 0.80 (0.77 to 0.83). Existing scoring systems, including APACHE II, SOFA, and CCF, when applied to our cohort, showed relatively poor discrimination, reflected by areas under the ROC curve of 0.68 (0.64 to 0.71), 0.69 (0.66 to 0.73), and 0.65 (0.62 to 0.69), respectively.
conclusionsOur new risk model outperformed existing generic and disease-specific scoring systems in predicting 60-day mortality in critically ill patients with AKI. The current model requires external validation before it can be applied to other patient populations.
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