Literature DB >> 30451541

Organizing stroke systems in the field for patients with suspected large vessel occlusion acute stroke.

Mohammed A Almekhlafi1,2,3,4, Jessalyn K Holodinsky2,4, Michael D Hill1,2,3,4, Noreen Kamal1,2, Mayank Goyal1,3.   

Abstract

Introduction: The dawn of endovascular stroke therapy has reshaped stroke care. Eligible patients need to be rushed to capable centers for intervention. This may entail bypassing closer hospitals that could confirm the diagnosis, administer thrombolytic therapy, then transfer patients for intervention. This has created a set of challenges: identifying endovascular candidates in the field, determining the best transport destination, and getting patients there quickly. Areas covered: This review provides a context for these emerging challenges. Current and emerging clinical prediction instruments for large vessel occlusion (LVO) are reviewed. The workflow in the thrombolysis-only primary stroke centers is reviewed, and interventions aimed at minimizing delays are highlighted. Innovations using mathematical modeling and devices for detection of LVO are reviewed. Expert commentary: More patients are expected to receive endovascular therapy as we push the boundaries for time and imaging criteria. Advances in detection and decision-making aids will improve the speed of treatment. Some patients will arrive at thrombolysis-only centers. This need to be triaged, diagnosed, treated, and transported promptly. Therefore, education of practitioners in these centers is paramount. Creating and facilitating infrastructure for imaging acquisition and sharing in such centers will reflect better care for stroke patients overall.

Entities:  

Keywords:  Endovascular; emergency; innovation; modeling; triage

Mesh:

Year:  2018        PMID: 30451541     DOI: 10.1080/14779072.2019.1550717

Source DB:  PubMed          Journal:  Expert Rev Cardiovasc Ther        ISSN: 1477-9072


  2 in total

1.  Implementation of a Prehospital Stroke Triage System Using Symptom Severity and Teleconsultation in the Stockholm Stroke Triage Study.

Authors:  Michael V Mazya; Annika Berglund; Niaz Ahmed; Mia von Euler; Staffan Holmin; Ann-Charlotte Laska; Jan M Mathé; Christina Sjöstrand; Einar E Eriksson
Journal:  JAMA Neurol       Date:  2020-06-01       Impact factor: 18.302

2.  Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML).

Authors:  Kazutaka Uchida; Junichi Kouno; Shinichi Yoshimura; Norito Kinjo; Fumihiro Sakakibara; Hayato Araki; Takeshi Morimoto
Journal:  Transl Stroke Res       Date:  2021-08-14       Impact factor: 6.800

  2 in total

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