ArticleIntensive care medicine2017
AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin.
Article in Intensive care medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00512122 (Impact of Early Parenteral Nutrition Completing Enteral Nutrition in Adult Critically Ill Patients), which is not on this map. Cited by 86 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Impact of Early Parenteral Nutrition Completing Enteral Nutrition in Adult Critically Ill Patients
Who cites it
86 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Clinical prediction models for acute kidney injury.Revista Brasileira de terapia intensiva · 2020Pooled it
- Development and validation of the creatinine clearance predictor machine learning models in critically ill adults.Critical care (London, England) · 2023Trial
- Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data.PloS one · 2018Trial
- External validation and comparative performance of four risk scores for acute kidney injury in critically ill adults.Renal failure · 2026Article
- Beyond binary AKI classification: development and external validation of a distributional model predicting serum creatinine and urine output trajectories in ICU patients.Critical care (London, England) · 2026Article
- 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
- The global epidemiology of acute kidney injury: challenges and opportunities.Nature reviews. Nephrology · 2026Review
- A validated model for early prediction of group A streptococcal aetiology in necrotising soft tissue infections using minimal patient data.BMC medicine · 2026Article
- Prediction of severe sepsis-associated acute kidney injury incorporating immune-inflammatory profiles: development and validation of a machine learning model in a multicenter prospective cohort study.Frontiers in immunology · 2026Article
- Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort study.EClinicalMedicine · 2025Article
- Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.Journal of medical Internet research · 2025Article
- Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI.Clinical journal of the American Society of Nephrology : CJASN · 2025Article
- An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.BMC medical informatics and decision making · 2025Article
- Leveraging artificial intelligence for early detection and prediction of acute kidney injury in clinical practice.Frontiers in physiology · 2025Article
- 10 tips on how to use dynamic risk assessment and alerts for AKI.Clinical kidney journal · 2024Review
- Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study.Biomedicines · 2024Article
- When to start renal replacement therapy in acute kidney injury: What are we waiting for?Journal of intensive medicine · 2024Review
- [Recent developments in acute kidney injury : Definition, biomarkers, subphenotypes, and management].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
26 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeEarly diagnosis of acute kidney injury (AKI) remains a major challenge. We developed and validated AKI prediction models in adult ICU patients and made these models available via an online prognostic calculator. We compared predictive performance against serum neutrophil gelatinase-associated lipocalin (NGAL) levels at ICU admission.
methodsAnalysis of the large multicenter EPaNIC database. Model development (n = 2123) and validation (n = 2367) were based on clinical information available (1) before and (2) upon ICU admission, (3) after 1 day in ICU and (4) including additional monitoring data from the first 24 h. The primary outcome was a comparison of the predictive performance between models and NGAL for the development of any AKI (AKI-123) and AKI stages 2 or 3 (AKI-23) during the first week of ICU stay.
resultsValidation cohort prevalence was 29% for AKI-123 and 15% for AKI-23. The AKI-123 model before ICU admission included age, baseline serum creatinine, diabetes and type of admission (medical/surgical, emergency/planned) and had an AUC of 0.75 (95% CI 0.75-0.75). The AKI-23 model additionally included height and weight (AUC 0.77 (95% CI 0.77-0.77)). Performance consistently improved with progressive data availability to AUCs of 0.82 (95% CI 0.82-0.82) for AKI-123 and 0.84 (95% CI 0.83-0.84) for AKI-23 after 24 h. NGAL was less discriminant with AUCs of 0.74 (95% CI 0.74-0.74) for AKI-123 and 0.79 (95% CI 0.79-0.79) for AKI-23.
conclusionsAKI can be predicted early with models that only use routinely collected clinical information and outperform NGAL measured at ICU admission. The AKI-123 models are available at http://akipredictor.com/ . Trial registration Clinical Trials.gov NCT00512122.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.