Evidence map›Paper›PMID 42418363›Full record

ArticleJMIR formative research2026

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.

Francesca Alfieri, Andrea Ancona, Alessandro Bacci, Simone Zappalà, Jordi Morillas Perez

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Francesca AlfieriU-Care Medical S.r.l., Corso Castelfidardo 30/A, Torino, TO, 10129, Italy.ORCID 0000-0003-4337-4523
Andrea AnconaU-Care Medical S.r.l., Corso Castelfidardo 30/A, Torino, TO, 10129, Italy.ORCID 0000-0001-5114-7502
Alessandro BacciU-Care Medical S.r.l., Corso Castelfidardo 30/A, Torino, TO, 10129, Italy.ORCID 0009-0006-9829-8766
Simone ZappalàU-Care Medical S.r.l., Corso Castelfidardo 30/A, Torino, TO, 10129, Italy.ORCID 0000-0002-8221-2626
Jordi Morillas PerezServicio de Medicina Intensiva, Hospital de Barcelona (SCIAS), Barcelona, Spain.ORCID 0000-0003-0437-1848

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute kidney injury (AKI) is a frequent and serious complication among hospitalized patients, particularly in critical care settings, where its incidence can exceed 50%. AKI is associated with increased mortality, prolonged hospitalization, dialysis dependence, and higher health care costs. Although the KDIGO (Kidney Disease: Improving Global Outcomes) guidelines emphasize supportive care, hemodynamic optimization, and avoidance of nephrotoxins, their implementation remains inconsistent, partly due to the lack of timely risk stratification. Recent advances in artificial intelligence have enhanced early prediction and detection of AKI, offering new opportunities to improve patient outcomes and intensive care unit (ICU) efficiency. The U-Care Renal Platform (UCRP; U-Care Medical S.r.l), a Conformité Européenne (CE)-marked artificial intelligence-powered medical device, integrates directly with the ICU electronic health record to continuously analyze patient data and predict the risk of moderate or severe AKI within 24 hours, providing actionable, guideline-based recommendations. While the predictive performance of UCRP has been validated previously, its real-world impact on clinical and operational outcomes in the ICU remains underexplored. Objective: This single-center uncontrolled before-and-after implementation study aims to evaluate the association between UCRP implementation and selected ICU clinical and operational outcomes in routine practice at SCIAS Hospital, Barcelona. Methods: This study was conducted as a retrospective service evaluation of a workflow-embedded clinical decision support system between March 2023 and March 2025. It included 202 postsurgical adult ICU patients. Outcomes of interest were assessed by comparing preimplementation and postimplementation periods. Months during which the UCRP was inactive were excluded from the analysis (total excluded duration: 10 months; 5 in the preimplementation period and 5 in the postimplementation period). The outcomes included the incidence of moderate-to-severe AKI (KDIGO stages 2 and 3), the use of nephrotoxic medications, the frequency of hypotensive episodes among patients with AKI, and the ICU length of stay. Results: During the postimplementation period, lower rates of moderate-to-severe AKI (9/99, 9.1% vs 12/103, 11.7%), nephrotoxic drug administration, and hypotensive episodes among patients with AKI were observed compared with the preimplementation period. Conclusions: Integration of the UCRP into ICU workflows was associated with differences in selected AKI-related process and intermediate clinical outcomes in this single-center uncontrolled before-and-after implementation study. However, given the study design, causal relationships cannot be established, and the findings should be interpreted as preliminary signals requiring confirmation in larger, controlled, and multicenter studies, including patient-centered outcomes.

Indexed as

Acute Kidney InjuryArtificial IntelligenceDecision Support Systems, ClinicalAgedFemaleHumansIntelligent SystemsIntensive Care UnitsMaleMiddle AgedRetrospective Studiesacute kidney injuryartificial intelligenceclinical decision support systemsimplementation studyintensive care unitsKDIGO guidelinesKidney Disease: Improving Global Outcomesmachine learningrenal function monitoring

Identifiers

PMID42418363
PMCPMC13344320

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Registered trials

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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.