Evidence map›Paper›PMID 38495365›Full record

ArticleCanadian journal of kidney health and disease2024

Advancing Community Care and Access to Follow-up After Acute Kidney Injury Hospitalization: Design of the AFTER AKI Randomized Controlled Trial.

Meha Bhatt, Eleanor Benterud, Taylor Palechuk, Coralea Bignell, Nasreen Ahmed, Kerry McBrien, Matthew T James, Neesh Pannu

Open access · goldAbstract read
In one paragraph

Article in Canadian journal of kidney health and disease, 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
1.4field-weighted citation impact, top 19% of its field
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, 4 citations in OpenAlex.

  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

8 authors at 2 institutions in 1 country.

Meha BhattDepartment of Medicine, Division of Nephrology, University of Calgary, Alberta, Canada.
Eleanor BenterudDepartment of Medicine, Division of Nephrology, University of Calgary, Alberta, Canada.ORCID https://orcid.org/0000-0001-5653-6558
Taylor PalechukDivision of Nephrology, Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada.
Coralea BignellDepartment of Medicine, Division of Nephrology, University of Calgary, Alberta, Canada.
Nasreen AhmedDivision of Nephrology, Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada.
Kerry McBrienDepartment of Family Medicine, University of Calgary, Alberta, Canada.
Matthew T JamesDepartment of Medicine, Division of Nephrology, University of Calgary, Alberta, Canada.ORCID https://orcid.org/0000-0002-1876-3917
Neesh PannuDivision of Nephrology, Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada.ORCID https://orcid.org/0000-0002-8626-8504
University of Calgary · CAUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute kidney injury (AKI) is a common complication among hospitalized patients with long-term implications including chronic kidney disease (CKD). Although models are available to predict the risk of advanced CKD after AKI, there is limited evidence regarding follow-up for patients with AKI after hospital discharge, resulting in variable follow-up care. A risk-stratified follow-up approach may improve appropriateness and efficiency of management for CKD among patients at risk of declining kidney function following AKI. Objective: The objective was to compare and evaluate the use of a risk-stratified approach to follow-up care vs usual care for patients with AKI after hospital discharge. Design: This study was a pragmatic randomized controlled trial. Setting: This study was conducted in 2 large urban hospitals in Alberta, Canada. Patients: Hospitalized patients with AKI (KDIGO stage 2 or 3) not previously under the care of a nephrologist, expected to survive greater than 90 days being discharged home. Measurements: We will evaluate whether guideline-recommended CKD care processes are initiated within 90 days, including statin use, angiotensin-converting enzyme inhibitor (ACEi)/angiotensin II receptor blocker (ARB) use in those with proteinuria or diabetes, and nephrologist follow-up if sustained eGFR <30 mL/min/1.73 m Methods: Patients with AKI will be enrolled and randomized near the time of hospital discharge. In the intervention group, low risk patients will receive information regarding AKI, medium risk patients will additionally receive follow-up guidance sent to their primary care physician, and high-risk patients will additionally receive follow-up with a nephrologist. Participants in the intervention and usual care group will receive a requisition for urine testing and bloodwork at 90 days following hospital discharge. Telephone follow-up will be conducted for all study participants at 90 days and 1 year after hospital discharge. Bivariate tests of association will be conducted to evaluate group differences at the follow-up time points. Limitations: We expect there may be challenges with recruitment due to the significant co-existence of comorbidity in this population. Conclusions: If the trial shows a positive effect on these processes for kidney care, it will inform larger-scale trial to determine whether this intervention reduces the incidence of long-term clinical adverse events, including CKD progression, cardiovascular events, and mortality following hospitalization with AKI.

Indexed as

AKI (acute kidney injury)clinical decision support systemsclinical research

Identifiers

PMID38495365
PMCPMC10943706
OpenAlexW4392890106

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.