ReviewCanadian journal of kidney health and disease2016
Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.
Review in Canadian journal of kidney health and disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical prediction models for acute kidney injury.Revista Brasileira de terapia intensiva · 2020Pooled it
- Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data.PloS one · 2018Trial
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.Nature communications · 2026Article
- External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.Critical care explorations · 2026Article
- Development and Validation of Machine Learning Models to Identify Emergency Department Patients at Increased Risk of New or Progressive Acute Kidney Injury.Journal of the American College of Emergency Physicians open · 2026Article
- Anti-Infective-Associated AKI: A Narrative Review of the Epidemiology, Mechanisms, Risk Factors, Biomarkers, Clinical Course, Monitoring, Prevention, and Therapeutic Strategies.Antibiotics (Basel, Switzerland) · 2025Review
- Prediction of acute kidney injury after noncardiac surgery in intensive care unit by machine learning.Annals of medicine and surgery (2012) · 2025Article
- Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.Journal of medical Internet research · 2025Article
- Development and validation of a real-time risk prediction model for acute kidney injury in hospitalized pediatric patients.World journal of pediatrics : WJP · 2025Article
- The challenges for developing prognostic prediction models for acute kidney injury in hospitalized children: A systematic review.Pediatric investigation · 2025Review
- Development and validation of a real-time prediction model for acute kidney injury in hospitalized patients.Nature communications · 2025Article
- Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.Renal failure · 2024Article
- Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study.JMIR medical informatics · 2024Article
- Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.Journal of biomedical informatics · 2024Article
- Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.medRxiv : the preprint server for health sciences · 2024Article
- Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study.EClinicalMedicine · 2024Article
- Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients.PloS one · 2024Article
- A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study.JMIR formative research · 2023Article
- Exploring the Potential of Chatbots in Critical Care Nephrology.Medicines (Basel, Switzerland) · 2023Review
- Prediction differences and implications of acute kidney injury with and without urine output criteria in adult critically ill patients.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.
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Registered trials
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.