ReviewCurrent opinion in critical care2015
Automated/integrated real-time clinical decision support in acute kidney injury.
Review in Current opinion in critical care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 25 citations in OpenAlex.
- Derivation and Validation of Pediatric Sepsis-Associated Acute Kidney Injury Subphenotypes With Prognostic Relevance.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Observational
- Shaping the Future of Chronic Kidney Disease Management in Spain: Insights from the CARABELA-CKD Initiative.Journal of clinical medicine · 2025Article
- Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.JAMIA open · 2024Article
- Implementation Strategies for Baby NINJA (Nephrotoxic Injury Negated by Just-in-Time Action) to Prevent Neonatal Medication-Induced Kidney Injury.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2023Article
- Impact of integrated clinical decision support systems in the management of pediatric acute kidney injury: a pilot study.Pediatric research · 2021Article
- Drug-Induced Hospital-Acquired Acute Kidney Injury in China: A Multicenter Cross-Sectional Survey.Kidney diseases (Basel, Switzerland) · 2021Article
- Prediction of Vancomycin Levels Using Cystatin C in Overweight and Obese Patients: a Retrospective Cohort Study of Hospitalized Patients.Antimicrobial agents and chemotherapy · 2020Observational
- Patterns of Cystatin C Uptake and Use Across and Within Hospitals.Mayo Clinic proceedings · 2020Article
- Does Your Program Know Its AKI and CRRT Epidemiology? The Case for a Dashboard.Frontiers in pediatrics · 2020Review
- Predictive Modeling of the Risk of Acute Kidney Injury in Critical Care: A Systematic Investigation of The Class Imbalance Problem.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Improvement of drug prescribing in acute kidney injury with a nephrotoxic drug alert system.European journal of hospital pharmacy : science and practice · 2019Article
- Provider acceptance of an automated electronic alert for acute kidney injury.Clinical kidney journal · 2016Article
- Impact of Daily Electronic Laboratory Alerting on Early Detection and Clinical Documentation of Acute Kidney Injury in Hospital Settings.Academic pathologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
purpose of reviewHealth information technology advancements have resulted in recent increased sophistication of the electronic health record, whereby patient demographic, physiological, and laboratory data can be extracted real-time and integrated into clinical decision support (CDS). RECENT
findingsThe implementation of health information technology advancements into CDS in the renal realm has been focused mainly on assessment of kidney function to guide medication dosing in the setting of reduced function or to reactively detect acute kidney injury (AKI) heralded by an abrupt increase in serum creatinine. More recent work has combined risk stratification algorithms to guide proactive diagnostic or therapeutic intervention to prevent AKI or reduce its severity. SUMMARY: Early, real-time identification and notification to healthcare providers of patients at risk for, or with, acute or chronic kidney disease can drive simple interventions to reduce harm. Similarly, screening patients at risk for AKI with these platforms to alert research personnel will lead to improve study subject recruitment. However, sole reliance on electronic health record generated alerts without active healthcare team integration and assessment represents a major barrier to the realization of the potential of CDS to improve healthcare quality and outcomes.
Indexed as
Identifiers
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.