ArticleJournal of critical care2023
Accurate and interpretable prediction of ICU-acquired AKI.
Article in Journal of critical care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Prediction of acute kidney injury in mechanically ventilated patients with COVID-19-related septic shock: An exploratory analysis of non-renal organ dysfunction markers.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2026Article
- Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.European journal of clinical investigation · 2026Article
- Development and validation of a prediction model for acute kidney injury following cardiopulmonary bypass surgery.European journal of medical research · 2025Observational
- Development of a machine learning-based prediction model for acute kidney injury associated with respiratory failure in the intensive care unit.Clinical and experimental medicine · 2025Article
- Epidemiological study of hospital acquired acute kidney injury in critically ill and its effect on the survival.Scientific reports · 2024Observational
- Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.BMC nephrology · 2024Review
- PTEN in kidney diseases: a potential therapeutic target in preventing AKI-to-CKD transition.Frontiers in medicine · 2024Review
- Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.Nature reviews. Nephrology · 2023Review
- Exploring the Potential of Chatbots in Critical Care Nephrology.Medicines (Basel, Switzerland) · 2023Review
- Serum magnesium levels and the risk of acute kidney injury in ICU patients with acute pancreatitis: A MIMIC-IV cohort study.Science progressArticle
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Authors and funding
6 authors.
Funding
Abstract
purposeWe developed and validated two parsimonious algorithms to predict the time of diagnosis of any stage of acute kidney injury (any-AKI) or moderate-to-severe AKI in clinically actionable prediction windows. MATERIALS AND
methodsIn this retrospective single-center cohort of adult ICU admissions, we trained two gradient-boosting models: 1) any-AKI model, predicting the risk of any-AKI at least 6 h before diagnosis (50,342 admissions), and 2) moderate-to-severe AKI model, predicting the risk of moderate-to-severe AKI at least 12 h before diagnosis (39,087 admissions). Performance was assessed before disease diagnosis and validated prospectively.
resultsThe models achieved an area under the receiver operating characteristic curve (AUROC) of 0.756 at six hours (any-AKI) and 0.721 at 12 h (moderate-to-severe AKI) prior. Prospectively, both models had high positive predictive values (0.796 and 0.546 for any-AKI and moderate-to-severe AKI models, respectively) and triggered more in patients who developed AKI vs. those who did not (median of 1.82 [IQR 0-4.71] vs. 0 [IQR 0-0.73] and 2.35 [IQR 0.14-4.96] vs. 0 [IQR 0-0.8] triggers per 8 h for any-AKI and moderate-to-severe AKI models, respectively).
conclusionsThe two AKI prediction models have good discriminative performance using common features, which can aid in accurately and informatively monitoring AKI risk in ICU patients.
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