Evidence map›Paper›PMID 37725539›Full record

ArticleJMIRx med2022

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study.

Diego Pinheiro, Ryan Hartman, Jing Mai, Erick Romero, Mohammad Soroya, Carmelo Bastos-Filho, Ricardo de Carvalho Lima, Michael Gibson, Imo Ebong, Julie Bidwell and 2 more

Abstract read
In one paragraph

Article in JMIRx med, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Diego PinheiroUnicap-Icam International School, Universidade Católica de Pernambuco, Recife, Brazil.ORCID https://orcid.org/0000-0001-9300-7196
Ryan Hartmansee Acknowledgments, .ORCID https://orcid.org/0000-0001-8044-5461
Jing MaiDepartment of Internal Medicine, Division of Cardiovascular Medicine, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0003-4713-9977
Erick RomeroDepartment of Internal Medicine, Division of Cardiovascular Medicine, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0001-5456-3093
Mohammad SoroyaDepartment of Internal Medicine, Division of Cardiovascular Medicine, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0002-6999-9559
Carmelo Bastos-FilhoPolytechnic School of Pernambuco, University of Pernambuco, Recife, Brazil.ORCID https://orcid.org/0000-0002-0924-5341
Ricardo de Carvalho LimaDivision of Cardiovascular Surgery, University of Pernambuco, Recife, Brazil.ORCID https://orcid.org/0000-0002-1369-0296
Michael GibsonDepartment of Internal Medicine, Division of Cardiovascular Medicine, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0001-8177-4371
Imo EbongDepartment of Internal Medicine, Division of Cardiovascular Medicine, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0001-9687-7541
Julie BidwellFamily Caregiving Institute, Betty Irene Moore School of Nursing, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0003-3995-7689
Miriam NunoDepartment of Public Health Sciences, Division of Biostatistics, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0003-2089-7276
Martin CadeirasDepartment of Internal Medicine, Division of Cardiovascular Medicine, University of California, Davis, Sacramento, CA, United States.ORCID https://orcid.org/0000-0003-4545-2871

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigher-than-expected heart failure (HF) readmissions affect half of US hospitals every year. The Hospital Reduction Readmission Program has reduced risk-adjusted readmissions, but it has also produced unintended consequences. Shared care models have been advocated for HF care, but the association of shared care networks with HF readmissions has never been investigated.

objectiveThis study aims to evaluate the association of shared care networks with 30-day HF excessive readmission rates using a longitudinal observational study.

methodsWe curated publicly available data on hospital discharges and HF excessive readmission ratios from hospitals in California between 2012 and 2017. Shared care areas were delineated as data-driven units of care coordination emerging from discharge networks. The localization index, the proportion of patients who reside in the same shared care area in which they are admitted, was calculated by year. Generalized estimating equations were used to evaluate the association between the localization index and the excessive readmission ratio of hospitals controlling for race/ethnicity and socioeconomic factors.

resultsA total of 300 hospitals in California in a 6-year period were included. The HF excessive readmission ratio was negatively associated with the adjusted localization index (β=-.0474, 95% CI -0.082 to -0.013). The percentage of Black residents within the shared care areas was the only statistically significant covariate (β=.4128, 95% CI 0.302 to 0.524).

conclusionsHigher-than-expected HF readmissions were associated with shared care networks. Control mechanisms such as the Hospital Reduction Readmission Program may need to characterize and reward shared care to guide hospitals toward a more organized HF care system.

Indexed as

catchment areacommunity networkshealthhealth carepatient readmissionquality assuranceregional medical programs

Identifiers

PMID37725539
PMCPMC10414461

What OpenQuestion holds

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LicenceCC BY
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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.