ReviewNature reviews. Nephrology2023
Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.
Review in Nature reviews. Nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Electronic Alert Systems for Patients With Acute Kidney Injury: A Systematic Review and Meta-Analysis.JAMA network open · 2024Pooled it
- Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Article
- Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.Intensive care medicine experimental · 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
- 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
- Sodium bicarbonate therapy for metabolic acidosis in children stratified by acute kidney injury status and chloride levels: a retrospective cohort study.Translational pediatrics · 2026Article
- Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP score.Scientific reports · 2026Article
- Acute kidney injury over the past decade: from definition evolution to pathogenesis insights and innovative therapeutic strategies.Cellular and molecular life sciences : CMLS · 2026Review
- Accent related errors in clinical speech transcription and a LLM-based remedy.NPJ digital medicine · 2026Article
- The global epidemiology of acute kidney injury: challenges and opportunities.Nature reviews. Nephrology · 2026Review
- Explainable machine learning-based 28-day mortality prediction model for elderly patients with acute kidney injury.BMC nephrology · 2026Article
- Forecasting ICU Acute Kidney Injury with Actionable Lead Time Using Interpretable Machine Learning: Development and Multi-Center Validation.Journal of clinical medicine · 2026Article
- Review
- Acute kidney injury in hospitalized patients with real-life analysis of incidence and clinical impact in Italian hospitals (the SIN-AKI study).Scientific reports · 2025Article
- Acute Kidney Injury Provider and Survivor Education: Current and Emerging Tools.Advances in kidney disease and health · 2025Review
- ADVANCE: a biomedical informatics approach to investigate acute kidney injury in infants.Pediatric research · 2025Article
- Development and validation of a real-time prediction model for acute kidney injury in hospitalized patients.Nature communications · 2025Article
- Article
- Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning.Renal failure · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
29 authors at 20 institutions in 5 countries.
Funding
Abstract
Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.
Indexed as
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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.