Evidence map›Paper›PMID 39588357›Full record

ReviewClinical kidney journal2024

10 tips on how to use dynamic risk assessment and alerts for AKI.

Danica Quickfall, Ashley M La, Jay L Koyner

Abstract readReview
In one paragraph

Review in Clinical kidney journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Article
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

3 authors.

Danica QuickfallCommittee on Clinical Pharmacology and Pharmacogenomics, Biological Science Division, University of Chicago, Chicago, IL, USA.
Ashley M LaSection of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USA.
Jay L KoynerCommittee on Clinical Pharmacology and Pharmacogenomics, Biological Science Division, University of Chicago, Chicago, IL, USA.ORCID https://orcid.org/0000-0001-6873-8712

Funding

Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney InjuryR01DK126933 · NIDDK · UNIVERSITY OF CHICAGO · PI CHURPEK, MATTHEW MICHAEL, KOYNER, JAY L · 2021 to 2025
$3.4M
NIDDK NIH HHS R01 DK126933
6 · The paper itself

Abstract

Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing the length of hospitalization and minimizing resource utilization. Implementation of dynamic risk scores and incorporation of novel biomarkers show promise for early detection and minimizing progression of AKI. Like any risk assessment tools, these require further external validation in a variety of clinical settings prior to widespread implementation. Additionally, alerts that may minimize exposure to a variety of nephrotoxic medications or prompt early nephrology consultation are shown to reduce the incidence and progression of AKI severity and enhance renal recovery. While dynamic risk scores and alerts are valuable, implementation requires thoughtfulness and should be used in conjunction with the overall clinical picture in certain situations, particularly when considering the initiation of fluid and diuretic administration or renal replacement therapy. Despite the contemporary challenges encountered with alert fatigue, implementing an alert-based bundle to improve AKI care is associated with improved outcomes, even when implementation is incomplete. Lastly, all alert-based interventions should be validated at an institutional level and assessed for their ability to improve institutionally relevant and clinically meaningful outcomes, reduce resource utilization and provide cost-effective interventions.

Indexed as

acute kidney injuryalertsbiomarkersclinical decision supportrenal replacement therapy

Identifiers

PMID39588357
PMCPMC11586629

What OpenQuestion holds

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Registered trials

None linked

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.