ReviewJournal of clinical medicine2020
Artificial Intelligence in Acute Kidney Injury Risk Prediction.
Review in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05851222 (Predicting Acute Kidney Injury in Infants Exposed to Nephrotoxic Drugs), which is not on this map. Cited by 33 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.
Predicting Acute Kidney Injury in Infants Exposed to Nephrotoxic Drugs: A Big Data Approach to Predict NEOnatal Acute Kidney Injury in Newborns expoSed to nephroTOxic Drugs. (NeoAKI STOP)
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Inhibiting ANP32E protects against acute kidney injury by regulating autophagy via the AMPK pathway.iScience · 2025Article
- Smart transplants: emerging role of nanotechnology and big data in kidney and islet transplantation, a frontier in precision medicine.Frontiers in immunology · 2025Review
- New approaches to acute kidney injury.Clinical kidney journal · 2024Article
- Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.Renal failure · 2024Article
- Unveiling the Future of Postoperative Outcomes Prediction: The Role of Machine Learning and Trust in Healthcare.Journal of medical systems · 2024Article
- Ten misconceptions regarding decision-making in critical care.World journal of critical care medicine · 2024Review
- [Artificial intelligence and acute kidney injury].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges.Function (Oxford, England) · 2024Review
- 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
- The effect of perioperative AKI on surgical outcomes.Anesthesiology and perioperative science · 2023Article
- Clinical Characteristics and Outcomes of Drug-Induced Acute Kidney Injury Cases.Kidney international reports · 2023Article
- Addressing the challenges of AI-based telemedicine: Best practices and lessons learned.Journal of education and health promotion · 2023Review
- Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal.Clinical kidney journal · 2022Article
- A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients.Journal of clinical medicine · 2022Article
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
- Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.BMC medical informatics and decision making · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.
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What OpenQuestion holds
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.