Evidence map›Paper›PMID 41973406›Full record

ArticleAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2026

Determining Access for a City-Wide Extracorporeal Cardiopulmonary Resuscitation (ECPR) Initiative Using Geospatial Analysis.

Christiana K Prucnal, Melissa A Meeker, Rebecca E Cash, Erica L Nelson, P Gregg Greenough, Stephen D Hallisey, Annette M Ilg, Christopher Kabrhel, Raghu R Seethala, Paul S Jansson

Abstract read
In one paragraph

Article in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2026. 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. Determining Access for a City-Wide Extracorporeal Cardiopulmonary Resuscitation (ECPR) Initiative Using Geospatial Analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026
    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

10 authors.

Christiana K PrucnalDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Melissa A MeekerDepartment of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0002-6074-8315
Rebecca E CashDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Erica L NelsonDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
P Gregg GreenoughDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Stephen D HalliseyDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Annette M IlgDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Christopher KabrhelDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Raghu R SeethalaDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Paul S JanssonDepartment of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.

Funding

Mass General Brigham
6 · The paper itself

Abstract

backgroundIn select situations, patients experiencing out-of-hospital cardiac arrest (OHCA) may be candidates for extracorporeal cardiopulmonary resuscitation (ECPR). Eligibility criteria for ECPR typically include a maximum time (usually 30 min) from arrest to arrival at an ECPR-capable center, which may exclude populations based on geographic factors.

methodsUsing geospatial modeling, we calculated drive times to ECPR-capable hospitals in Boston utilizing census block group centroid coordinates as proxy sites for OHCA locations. We used a fixed dispatch-to-scene arrival time of 7.4 min, extrapolated from Boston EMS median transport time data. We set conditions at the 50th (24 min), 25th (18 min), and 10th (13 min) percentiles for EMS on-scene time and, for each condition, determined access to ECPR with an arrest to arrival criterion of less than 30 min. We analyzed the effect of high- versus low-traffic conditions and then derived the arrest to arrival time necessary to achieve access for 90% of the city.

resultsThe entire City of Boston was excluded from ECPR with median times and current eligibility criteria. Decreasing time-on-scene to the 25th percentile led to increased access: 16% of block groups with low traffic and 6% of block groups with high traffic. At the 10th percentile for time-on-scene, 55% of block groups had access with low traffic and 28% had access with high traffic. To achieve access for 90% of the city under high-traffic conditions at the 50th percentile for time-on-scene, the criterion for arrest to arrival would need to be extended to 55.8 min.

conclusionsThe current arrest to arrival criterion for ECPR excludes the entire City of Boston using median transportation and on-scene times. Increasing access to ECPR should include efforts to decrease prehospital duration, such as minimizing time-on-scene for potential OHCA cases. Future study should examine potential levers to improve access, such as novel prehospital ECPR delivery models, air-based transport, and liberalized arrest to arrival criteria.

Indexed as

Cardiopulmonary ResuscitationEmergency Medical ServicesExtracorporeal Membrane OxygenationHealth Services AccessibilityOut-of-Hospital Cardiac ArrestBostonHumansSpatial AnalysisTime FactorsTime-to-Treatmentemergency medical services (EMS)extracorporeal cardiopulmonary resuscitation (ECPR)extracorporeal membrane oxygenation (ECMO)geographic information systems (GIS)out of hospital cardiac arrest (OHCA)spatial analysistraffic network patterns

Identifiers

PMID41973406
PMCPMC13075459

What OpenQuestion holds

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LicenceCC BY
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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.