Observational studyCritical care (London, England)2020
Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.
Observational study in Critical care (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses 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
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effect of acute kidney injury care bundles on patient prognosis: a systematic review and meta-analysis.BMC nephrology · 2025Pooled it
- Improvement of composite kidney outcomes by AKI care bundles: a systematic review and meta-analysis.Critical care (London, England) · 2023Pooled it
- Impact of AKI care bundles on kidney and patient outcomes in hospitalized patients: a systematic review and meta-analysis.BMC nephrology · 2021Pooled it
- Impact of an Artificial Intelligence-Powered Clinical Decision Support System for Acute Kidney Injury Prevention in the Intensive Care Unit: Single-Center Uncontrolled Before-and-After Implementation Study.JMIR formative research · 2026Article
- Urine output is an early and strong predictor of acute kidney injury and associated mortality: a systematic literature review of 50 clinical studies.Annals of intensive care · 2024Review
- Fluid balance, biomarkers of renal function and mortality in critically ill patients with AKI diagnosed before, or within 24 h of intensive care unit admission: a prospective study.Journal of nephrology · 2024Article
- Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.Nature reviews. Nephrology · 2023Review
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- From digital health to learning health systems: four approaches to using data for digital health design.Health systems (Basingstoke, England) · 2023Article
- A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- Mortality Prediction in Patients with Severe Acute Kidney Injury Requiring Renal Replacement Therapy.Medicina (Kaunas, Lithuania) · 2021Article
- Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.Critical care (London, England) · 2021Article
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
backgroundAcute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI.
methodsThe study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI.
resultsEach phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05.
conclusionsThis observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.
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.