Evidence map›Paper›PMID 29343588›Full record

ArticleStroke2018

Modeling the Impact of Interhospital Transfer Network Design on Stroke Outcomes in a Large City.

Neal S Parikh, Abhinaba Chatterjee, Iván Díaz, Ankur Pandya, Alexander E Merkler, Gino Gialdini, Benjamin R Kummer, Saad A Mir, Michael P Lerario, Matthew E Fink and 2 more

Open access · bronzeAbstract read

PubMed Full text at PMC Publisher

In one paragraph

Article in Stroke, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it

0cells of the map it votes in

10citing papers in PubMed

2.1field-weighted citation impact, top 13% 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

10 citing papers in PubMed, 23 citations in OpenAlex.

Artificial intelligence in disease diagnostics: a comprehensive narrative review of current advances, applications, and future challenges in healthcare.Annals of medicine and surgery (2012) · 2025
Review
Differences in Interfacility Transfer from Emergency Department and Inpatient Services for Inpatient Neurologic Care.The Neurohospitalist · 2024
Article
Geographical Requirements for the Applicability of the Results of the RACECAT Study to Other Stroke Networks.Journal of the American Heart Association · 2023
Article
Intravenous Thrombolysis Is Associated With Better Outcomes in Large-Vessel Occlusion Requiring Endovascular Therapy.Stroke (Hoboken, N.J.) · 2023
Article
RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.Journal of ambient intelligence and humanized computing · 2023
Article
Access to Mechanical Thrombectomy for Ischemic Stroke in the United States.Stroke · 2021
Article
Nonindex Readmission After Ruptured Brain Aneurysm Treatment Is Associated with Higher Morbidity and Repeat Readmission.World neurosurgery · 2019
Article
A Network Approach to Stroke Systems of Care.Circulation. Cardiovascular quality and outcomes · 2019
Review
Decision Analysis Model for Prehospital Triage of Patients With Acute Stroke.Stroke · 2019
Article
Effect of Interhospital Transfer on Endovascular Treatment for Acute Ischemic Stroke.Stroke · 2019
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

12 authors at 3 institutions in 1 country.

Neal S ParikhFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.). ctnu-correspondence@med.cornell.edu.

Abhinaba ChatterjeeFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Iván DíazFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Ankur PandyaFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Alexander E MerklerFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Gino GialdiniFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Benjamin R KummerFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Saad A MirFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Michael P LerarioFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Matthew E FinkFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Babak B NaviFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Hooman KamelFrom the Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, New York, NY (N.S.P., A.C., A.E.M., G.G., B.R.K., S.A.M., M.P.L., M.E.F., B.B.N., H.K.); Department of Neurology (N.S.P., A.E.M., S.A.M., M.E.F., B.B.N., H.K.) and Department of Healthcare Policy and Research (I.D.), Weill Cornell Medicine, New York, NY; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (A.P.); Department of Biomedical Informatics, Columbia University, New York, NY (B.R.K.); and Department of Neurology, NewYork-Presbyterian Queens, Flushing (M.P.L.).

Weill Cornell Medicine · USColumbia University · USCornell University · US

Funding

NEUROEPIDEMIOLOGY TRAINING PROGRAMT32NS007153 · NINDS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI GU, YIAN, WILLEY, JOSHUA ZEBADIAH · 1987 to 2023

$6.2M

Left Atrial abNormality, ThromboEmbolism, and Race: Novel risk factors for stroke (LANTERN)R01NS097443 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI KAMEL, HOOMAN · 2016 to 2019

$1.5M

Cancer And Risk Of Ischemic StrokeK23NS091395 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI NAVI, BABAK BENJAMIN · 2015 to 2019

$945k

Atrial Fibrillation Precursors May Be Novel Stroke Risk FactorsK23NS082367 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI KAMEL, HOOMAN · 2013 to 2017

$937k

NINDS NIH HHS K23 NS082367NINDS NIH HHS K23 NS091395NINDS NIH HHS R01 NS097443NINDS NIH HHS T32 NS007153

6 · The paper itself

Abstract

background and purposeWe sought to model the effects of interhospital transfer network design on endovascular therapy eligibility and clinical outcomes of stroke because of large-vessel occlusion for the residents of a large city.

methodsWe modeled 3 transfer network designs for New York City. In model A, patients were transferred from spoke hospitals to the closest hub hospitals with endovascular capabilities irrespective of hospital affiliation. In model B, which was considered the base case, patients were transferred to the closest affiliated hub hospitals. In model C, patients were transferred to the closest affiliated hospitals, and transfer times were adjusted to reflect full implementation of streamlined transfer protocols. Using Monte Carlo methods, we simulated the distributions of endovascular therapy eligibility and good functional outcomes (modified Rankin Scale score, 0-2) in these models.

resultsIn our models, 200 patients (interquartile range [IQR], 168-227) with a stroke amenable to endovascular therapy present to New York City spoke hospitals each year. Transferring patients to the closest hub hospital irrespective of affiliation (model A) resulted in 4 (IQR, 1-9) additional patients being eligible for endovascular therapy and an additional 1 (IQR, 0-2) patient achieving functional independence. Transferring patients only to affiliated hospitals while simulating full implementation of streamlined transfer protocols (model C) resulted in 17 (IQR, 3-41) additional patients being eligible for endovascular therapy and 3 (IQR, 1-8) additional patients achieving functional independence.

conclusionsOptimizing acute stroke transfer networks resulted in clinically small changes in population-level stroke outcomes in a dense, urban area.

Indexed as

Brain IschemiaHospitalsHumansPatient TransferStrokeThrombolytic TherapyTime FactorsTreatment Outcomeemergency medical servicespatient transferstroketriage

Identifiers

PMID29343588

DOI10.1161/STROKEAHA.117.018166

PMCPMC5780257

OpenAlexW2794147915

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

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.