Evidence map›Paper›PMID 41542123›Full record

ArticleCJC open2025

Heart Failure Readmission Risk Factors: A Modified Delphi Panel Study.

Natalie Wiebe, Cathy A Eastwood, Seungwon Lee, Elliot A Martin, Robin L Walker, Alexander Ah-Chi Leung, Jonathan Howlett, Hude Quan

Abstract read
In one paragraph

Article in CJC open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

8 authors.

Natalie WiebeCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Cathy A EastwoodCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Seungwon LeeCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Elliot A MartinCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Robin L WalkerCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Alexander Ah-Chi LeungLibin Cardiovascular Institute of Alberta, Calgary, Alberta, Canada.
Jonathan HowlettLibin Cardiovascular Institute of Alberta, Calgary, Alberta, Canada.
Hude QuanCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Heart failure (HF) readmission rates have been a significant concern for healthcare systems globally. Accurate predictive models are essential to identify patients at high readmission risk and implement timely interventions. Current models often lack comprehensive variables that reflect both clinical and patient and/or caregiver perspectives. We aimed to develop a consensus-driven approach to identify essential variables for inclusion in HF hospital readmission risk prediction algorithms. Methods: A Delphi panel comprised of clinicians and patient and/or caregiver partners was assembled. The Delphi panelists were recruited from the province of Alberta, Canada. The panel consisted of 13 individuals, including 9 healthcare providers and 4 patients and/or caregivers. The review panel was provided with a list of variables from a previously completed systematic literature review. Three rounds were conducted. The panel also determined the directionality of the association. Results: A total of 99 variables were identified through literature and physician input. Panelists reached a consensus on 61 variables, which were deemed to be associated with the risk of readmission for any cause within 30 days of discharge after HF hospitalization. Clinician ratings on consensus were consistently higher than those of nonclinicians. Conclusions: This study successfully identified 61 variables associated with HF readmission risk through a modified Delphi process, incorporating both clinician and patient and/or caregiver perspectives. These findings provide a foundation for future research and the development of more comprehensive and accurate risk prediction models. Including diverse stakeholder input highlights the importance of integrating medical expertise and patient experiences in improving HF management and reducing readmission rates.

Indexed as

heart failurehospital readmissionmodified delphiprediction algorithmrisk factors

Identifiers

PMID41542123
PMCPMC12800856

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.