Evidence map›Paper›PMID 26102251›Full record

ArticleCritical care medicine2015

County-Level Effects of Prehospital Regionalization of Critically Ill Patients: A Simulation Study.

Christopher W Seymour, Osama Alotaik, David J Wallace, Ahmed E Elhabashy, Jagpreet Chhatwal, Thomas D Rea, Derek C Angus, Graham Nichol, Jeremy M Kahn

Abstract read
In one paragraph

Article in Critical care medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Understanding Emergency Care Delivery Through Computer Simulation Modeling.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2018
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Christopher W Seymour1Department of Critical Care and Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. 2Clinical Research, Investigation, and Systems Modeling of Acute Illness Center, Department of Critical Care, University of Pittsburgh, Pittsburgh, PA. 3Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA. 4Production Engineering Department, Alexandria University, Alexandria, Egypt. 5Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX. 6Department of Medicine, University of Washington School of Medicine, Seattle, WA. 7King County MedicOne, King County Emergency Medical Services, Seattle, WA. 8Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. 9University of Washington-Harborview Center for Prehospital Emergency Care, Seattle, WA.
Osama Alotaik
David J Wallace
Ahmed E Elhabashy
Jagpreet Chhatwal
Thomas D Rea
Derek C Angus
Graham Nichol
Jeremy M Kahn

Funding

University of Pittsburgh Clinical and Translational Science InstitutionKL2TR000146 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2012 to 2015
$8.7M
Pittsburgh Career Development Program in Emergency Medicine ResearchK12HL109068 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CALLAWAY, CLIFTON W., YEALY, DONALD M · 2011 to 2015
$3.1M
Pre-hospital identification of high-risk sepsisK23GM104022 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SEYMOUR, CHRISTOPHER WARREN · 2013 to 2016
$674k
NCATS NIH HHS KL2 TR000146NCATS NIH HHS KL2TR000146NHLBI NIH HHS K12 HL109068NHLBI NIH HHS K12-HL109068NIGMS NIH HHS 1K23GM104022-01NIGMS NIH HHS K23 GM104022
6 · The paper itself

Abstract

objectiveRegionalization may improve critical care delivery, yet stakeholders cite concerns about its feasibility. We sought to determine the operational effects of prehospital regionalization of nontrauma, nonarrest critical illness.

settingKing County, Washington.

designDiscrete event simulation study. PATIENTS: All 2006 hospital discharge data, linked to all adult, eligible patients transported by county emergency medical services agencies.

interventionsWe simulated active triage of high-risk patients to designated referral centers using a validated prehospital risk score; we studied three regionalization scenarios: 1) up triage, 2) up and down triage, and 3) up and down triage after reducing ICU beds by 25%. We determined the effect on patient routing, ICU occupancy at referral and nonreferral hospitals, and emergency medical services transport times. MEASUREMENTS AND MAIN

resultsA total of 119,117 patients were hospitalized at 11 nonreferral centers and 76,817 patients were hospitalized at three referral centers. Among 20,835 emergency medical services patients, 7,817 patients (43%) were eligible for up triage and 10,242 patients (57%) were eligible for down triage. At baseline, mean daily ICU bed occupancy was 61% referral and 47% at nonreferral hospitals. Up triage increased referral ICU occupancy to 68%, up and down triage to 64%, and up and down triage with bed reduction to 74%. Mean daily nonreferral ICU occupancy did not exceed 60%. Total emergency medical services transport time increased by less than 3% with up and down triage.

conclusionsRegionalization based on prehospital triage of the critically ill can allocate high-risk patients to referral hospitals without adversely affecting ICU occupancy or prehospital travel time.

Indexed as

AdultAgedBed OccupancyComputer SimulationCritical IllnessFemaleHumansMaleMiddle AgedPatient DischargeReferral and ConsultationRegional Health PlanningRisk AssessmentTime FactorsTransportation of PatientsTrauma Centers

Identifiers

PMID26102251
PMCPMC5083852

What OpenQuestion holds

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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.