Evidence map›Paper›PMID 31727058›Full record

ArticleBMC medical informatics and decision making2019

Post-acute care referral in United States of America: a multiregional study of factors associated with referral destination in a cohort of patients with coronary artery bypass graft or valve replacement.

Ineen Sultana, Madhav Erraguntla, Hye-Chung Kum, Dursun Delen, Mark Lawley

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 15% 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Systematic review of prediction models for postacute care destination decision-making.Journal of the American Medical Informatics Association : JAMIA · 2021
    Pooled it
  3. Review
  4. Article
  5. Article
  6. Article
  7. Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.Journal of diabetes science and technology · 2021
    Article
  8. Observational
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

5 authors at 2 institutions in 1 country.

Ineen SultanaDepartment of Industrial and System Engineering, Texas A&M University, College Station, TX, USA. ineensultana@tamu.edu.ORCID 0000-0001-7448-5643
Madhav ErraguntlaDepartment of Industrial and System Engineering, Texas A&M University, College Station, TX, USA.
Hye-Chung KumDepartment of Industrial and System Engineering, Texas A&M University, College Station, TX, USA.
Dursun DelenDepartment of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, USA.
Mark LawleyDepartment of Industrial and System Engineering, Texas A&M University, College Station, TX, USA.
Texas A&M University System · USOklahoma State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of post-acute care (PAC) for cardiovascular conditions is highly variable across geographical regions. Although PAC benefits include lower readmission rates, better clinical outcomes, and lower mortality, referral patterns vary widely, raising concerns about substandard care and inflated costs. The objective of this study is to identify factors associated with PAC referral decisions at acute care discharge.

methodsThis study is a retrospective Electronic Health Records (EHR) based review of a cohort of patients with coronary artery bypass graft (CABG) and valve replacement (VR). EHR records were extracted from the Cerner Health-Facts Data warehouse and covered 49 hospitals in the United States of America (U.S.) from January 2010 to December 2015. Multinomial logistic regression was used to identify associations of 29 variables comprising patient characteristics, hospital profiles, and patient conditions at discharge.

resultsThe cohort had 14,224 patients with mean age 63.5 years, with 10,234 (71.9%) male and 11,946 (84%) Caucasian, with 5827 (40.96%) being discharged to home without additional care (Home), 5226 (36.74%) to home health care (HHC), 1721 (12.10%) to skilled nursing facilities (SNF), 1168 (8.22%) to inpatient rehabilitation facilities (IRF), 164 (1.15%) to long term care hospitals (LTCH), and 118 (0.83%) to other locations. Census division, hospital size, teaching hospital status, gender, age, marital status, length of stay, and Charlson comorbidity index were identified as highly significant variables (p- values < 0.001) that influence the PAC referral decision. Overall model accuracy was 62.6%, and multiclass Area Under the Curve (AUC) values were for Home: 0.72; HHC: 0.72; SNF: 0.58; IRF: 0.53; LTCH: 0.52, and others: 0.46.

conclusionsCensus location of the acute care hospital was highly associated with PAC referral practices, as was hospital capacity, with larger hospitals referring patients to PAC at a greater rate than smaller hospitals. Race and gender were also statistically significant, with Asians, Hispanics, and Native Americans being less likely to be referred to PAC compared to Caucasians, and female patients being more likely to be referred than males. Additional analysis indicated that PAC referral practices are also influenced by the mix of PAC services offered in each region.

Indexed as

Coronary Artery BypassHeart Valve Prosthesis ImplantationPatient DischargeReferral and ConsultationSubacute CareAgedCohort StudiesFemaleHeart DiseasesHome Care ServicesHospitalsHumansLogistic ModelsMaleMiddle AgedSkilled Nursing FacilitiesCoronary artery bypassLength of stayPatient dischargePost-acute careReferral

Identifiers

PMID31727058
PMCPMC6854767
OpenAlexW2987237387

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.