ArticleJournal of nephrology2021
A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.
Article in Journal of nephrology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Construction and evaluation of a risk prediction model for pulmonary infection-associated acute kidney injury in intensive care units.Clinical and translational science · 2023Trial
- DCBM-Tri: a dual-channel bilinear mapping triplet model for early recognition of acute kidney injury in imbalanced cohorts.Scientific reports · 2026Article
- Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.Intensive care medicine experimental · 2026Article
- Development and validation of clinical prediction models for personalized renal function monitoring in people with heart failure in primary care: the RENAL-HF study protocol.European heart journal. Digital health · 2026Article
- Impact of 24-hour urine output trajectories on 28-day mortality in people with urinary tract infection: based on the MIMIC-IV database.BMC infectious diseases · 2026Article
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Review
- Toward the standardization of big datasets of urine output for AKI analysis: a multicenter validation study.Scientific reports · 2025Article
- Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.Scientific reports · 2025Observational
- Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives.BMC nursing · 2024Article
- Effects of 24-hour urine-output trajectories on the risk of acute kidney injury in critically ill patients with cirrhosis: a retrospective cohort analysis.Renal failure · 2024Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.Journal of medical systems · 2024Review
- Machine-learning model for predicting oliguria in critically ill patients.Scientific reports · 2024Article
- Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions.
methodsThe aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model.
resultsThe deep learning model defined an area under the curve (AUC) of 0.89 (± 0.01), sensitivity = 0.8 and specificity = 0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12 h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI.
conclusionIn conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12 h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated in the ICU setting to better manage, and potentially prevent, AKI episodes.
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