Evidence map›Paper›PMID 33360394›Full record

ArticleThe American journal of emergency medicine2021

Factors associated with Interhospital transfers of emergency general surgery patients from emergency departments.

Sara Fernandes-Taylor, Dou-Yan Yang, Jessica Schumacher, Fiona Ljumani, Baruch S Fertel, Angela Ingraham

Abstract read
In one paragraph

Article in The American journal of emergency medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Coordinating care across hospitals: how integration shapes patient mobility.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
    Article
  3. Article
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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

6 authors.

Sara Fernandes-TaylorDepartment of Surgery, University of Wisconsin-Madison, Madison, WI, United States of America. Electronic address: fernandes-taylor@surgery.wisc.edu.
Dou-Yan YangDepartment of Surgery, University of Wisconsin-Madison, Madison, WI, United States of America.
Jessica SchumacherDepartment of Surgery, University of Wisconsin-Madison, Madison, WI, United States of America.
Fiona LjumaniDepartment of Surgery, University of Wisconsin-Madison, Madison, WI, United States of America.
Baruch S FertelEmergency Services Institute & Enterprise Quality and Safety Cleveland Clinic, Cleveland OH, United States of America.
Angela IngrahamDepartment of Surgery, University of Wisconsin-Madison, Madison, WI, United States of America.

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
A Critical Assessment of and Opportunities for Improvement in the Interhospital Transfer of Emergency General Surgery patientsK08HS025224 · AHRQ · UNIVERSITY OF WISCONSIN-MADISON · PI INGRAHAM, ANGELA M · 2018 to 2022
$788k
AHRQ HHS K08 HS025224NCATS NIH HHS UL1 TR002373
6 · The paper itself

Abstract

backgroundEmergency general surgery (EGS) conditions account for over 3 million or 7.1% of hospitalizations per year in the US. Patients are increasingly transferred from community emergency departments (EDs) to larger centers for care, and a growing demand for treating EGS conditions mandates a better understanding of how ED clinicians transfer patients. We identify patient, clinical, and organizational characteristics associated with interhospital transfers of EGS patients originating from EDs in the United States.

methodWe analyze data from the Agency for Healthcare Research and Quality Nationwide Emergency Department Sample (NEDS) for the years 2010-2014. Patient-level sociodemographic characteristics, clinical factors, and hospital-level factors were examined as predictors of transfer from the ED to another acute care hospital. Multivariable logistic regression analysis includes patient and hospital characteristics as predictors of transfer from an ED to another acute care hospital.

resultsOf 47,442,892 ED encounters (weighted) between 2008 and 2014, 1.9% resulted in a transfer. Multivariable analysis indicates that men (Odds ratio (OR) 1.18 95% Confidence Interval (95% CI) 1.16-1.21) and older patients (OR 1.02 (95% CI 1.02-1.02)) were more likely to be transferred. Relative to patients with private health insurance, patients covered by Medicare (OR 1.09 (95% CI 1.03-1.15) or other insurance (OR 1.34 (95% CI 1.07-1.66)) had a higher odds of transfer. Odds of transfer increased with a greater number of comorbid conditions compared to patients with an EGS diagnosis alone. EGS diagnoses predicting transfer included resuscitation (OR 36.72 (95% CI 30.48-44.22)), cardiothoracic conditions (OR 8.47 (95% CI 7.44-9.63)), intestinal obstruction (OR 4.49 (95% CI 4.00-5.04)), and conditions of the upper gastrointestinal tract (OR 2.82 (95% CI 2.53-3.15)). Relative to Level I or II trauma centers, hospitals with a trauma designation III or IV had a 1.81 greater odds of transfer. Transfers were most likely to originate at rural hospitals (OR 1.69 (95% CI 1.43-2.00)) relative to urban non-teaching hospitals.

conclusionMedically complex and older patients who present at small, rural hospitals are more likely to be transferred. Future research on the unique needs of rural hospitals and timely transfer of EGS patients who require specialty surgical care have the potential to significantly improve outcomes and reduce costs.

Indexed as

Emergency Service, HospitalGeneral SurgeryAdultAgedFemaleHumansMaleMiddle AgedPatient TransferUnited StatesEmergency departmentEmergency general surgeryHandoffsInterhospital transfer

Identifiers

PMID33360394
PMCPMC8074541

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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.