Evidence map›Paper›PMID 38609346›Full record

ArticleEuropean heart journal. Quality of care & clinical outcomes2024

Impact of the method of calculating 30-day readmission rate after hospitalization for heart failure. Data from the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry.

Samaneh Salimian, Sean A Virani, Thomas M Roston, Ren Jie Robert Yao, Ricky D Turgeon, Justin Ezekowitz, Nathaniel M Hawkins

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Quality of care & clinical outcomes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 9% 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, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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

7 authors at 2 institutions in 1 country.

Samaneh SalimianCentre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver V6T 2A1, Canada.
Sean A ViraniCentre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver V6T 2A1, Canada.
Thomas M RostonCentre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver V6T 2A1, Canada.ORCID 0000-0001-5663-4377
Ren Jie Robert YaoCentre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver V6T 2A1, Canada.
Ricky D TurgeonCentre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver V6T 2A1, Canada.
Justin EzekowitzCanadian Vigour Centre, University of Alberta, Edmonton, Alberta T6G 2E1, Canada.
Nathaniel M HawkinsCentre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver V6T 2A1, Canada.ORCID 0000-0003-1286-2014
University of British Columbia · CAUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThirty-day readmission rate after heart failure (HF) hospitalization is widely used to evaluate healthcare quality. Methodology may substantially influence estimated rates. We assessed the impact of different definitions on HF and all-cause readmission rates.

methodsReadmission rates were examined in 1835 patients discharged following HF hospitalization using 64 unique definitions derived from five methodological factors: (1) International Classification of Diseases-10 codes (broad vs. narrow), (2) index admission selection (single admission only first-in-year vs. random sample; or multiple admissions in year with vs. without 30-day blanking period), (3) variable denominator (number alive at discharge vs. number alive at 30 days), (4) follow-up period start (discharge date vs. day following discharge), and (5) annual reference period (calendar vs. fiscal). The impact of different factors was assessed using linear regression.

resultsThe calculated 30-day readmission rate for HF varied more than two-fold depending solely on the methodological approach (6.5-15.0%). All-cause admission rates exhibited similar variation (18.8-29.9%). The highest rates included all consecutive index admissions (HF 11.1-15.0%, all-cause 24.0-29.9%), and the lowest only one index admission per patient per year (HF 6.5-11.3%, all-cause 18.8-22.7%). When including multiple index admissions and compared with blanking the 30-day post-discharge, not blanking was associated with 2.3% higher readmission rates. Selecting a single admission per year with a first-in-year approach lowered readmission rates by 1.5%, while random-sampling admissions lowered estimates further by 5.2% (P < 0.001).

conclusionCalculated 30-day readmission rates varied more than two-fold by altering methods. Transparent and consistent methods are needed to ensure reproducible and comparable reporting.

Indexed as

Heart FailurePatient ReadmissionRegistriesAcute DiseaseAgedAged, 80 and overBritish ColumbiaFemaleFollow-Up StudiesHospitalizationHumansMaleMiddle AgedTime Factors30-day Readmission rateHeart failurePatient readmissionQuality improvementStandardized reporting

Identifiers

PMID38609346
PMCPMC11398898
OpenAlexW4394789527

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.