Observational studyAmerican journal of kidney diseases : the official journal of the National Kidney Foundation2020
Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.
Observational study in American journal of kidney diseases : the official journal of the National Kidney Foundation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Non-linear association between AKI alert detection rate by physicians and medical costs.PloS one · 2025Trial
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Article
- Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study.JMIR medical informatics · 2024Article
- A single-center clinical study of acute kidney injury associated with acute myocardial infarction.International urology and nephrology · 2024Article
- Exploring the role of ferroptosis-related genes as biomarkers in acute kidney injury.PloS one · 2024Article
- A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study.Journal of translational medicine · 2023Article
- Renoprotective Effects of Luteolin: Therapeutic Potential for COVID-19-Associated Acute Kidney Injuries.Biomolecules · 2022Review
- Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset.Journal of translational medicine · 2022Article
- Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction.Healthcare (Basel, Switzerland) · 2021Review
- Renal Recovery after the Implementation of an Electronic Alert and Biomarker-Guided Kidney-Protection Strategy following Major Surgery.Journal of clinical medicine · 2021Article
- Catechins: Therapeutic Perspectives in COVID-19-Associated Acute Kidney Injury.Molecules (Basel, Switzerland) · 2021Review
- SARS-CoV-2 Causes Acute Kidney Injury by Directly Infecting Renal Tubules.Frontiers in cell and developmental biology · 2021Article
- Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.Advances in chronic kidney disease · 2021Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
RATIONALE &
objectiveAcute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY
designProspective observational cohort study. SETTING &
participants2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI ANALYTICAL APPROACH: Descriptive statistics and univariable associations.
resultsAt enrollment, mean predicted probability of AKI LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation.
conclusionsA real-time AKI risk model was successfully integrated into the EHR.
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