Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support
© 2022 The Author(s). Published by S. Karger AG, Basel..
BACKGROUND: Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis is used for faster and standardized patient selection. However, there is little information on how such software influences real-world patient management.
AIMS: We evaluated changes in thrombolysis and thrombectomy delivery following implementation of automated analysis at a high volume primary stroke centre.
METHODS: We retrospectively collected data on consecutive stroke patients admitted to a large university stroke centre from two identical 7-month periods in 2017 and 2018 between which the e-Stroke Suite (Brainomix, Oxford, UK) was implemented to analyse non-contrast CT and CT angiography results. Delivery of stroke care was otherwise unchanged. Patients were transferred to a hub for thrombectomy. We collected the number of patients receiving intravenous thrombolysis and/or thrombectomy, the time to treatment; and outcome at 90 days for thrombectomy.
RESULTS: 399 patients from 2017 and 398 from 2018 were included in the study. From 2017 to 2018, thrombolysis rates increased from 11.5% to 18.1% with a similar trend for thrombectomy (2.8-4.8%). There was a trend towards shorter door-to-needle times (44-42 min) and CT-to-groin puncture times (174-145 min). There was a non-significant trend towards improved outcomes with thrombectomy. Qualitatively, physician feedback suggested that e-Stroke Suite increased decision-making confidence and improved patient flow.
CONCLUSIONS: Use of artificial intelligence decision support in a hyperacute stroke pathway facilitates decision-making and can improve rate and time of reperfusion therapies in a hub-and-spoke system of care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Cerebrovascular diseases extra - 12(2022), 1 vom: 01., Seite 28-32 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gunda, Bence [VerfasserIn] |
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| Links: |
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| Themen: |
E-ASPECTS |
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| Anmerkungen: |
Date Completed 04.05.2022 Date Revised 16.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1159/000522423 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2022 The Author(s). Published by S. Karger AG, Basel. | ||
| 520 | |a BACKGROUND: Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis is used for faster and standardized patient selection. However, there is little information on how such software influences real-world patient management | ||
| 520 | |a AIMS: We evaluated changes in thrombolysis and thrombectomy delivery following implementation of automated analysis at a high volume primary stroke centre | ||
| 520 | |a METHODS: We retrospectively collected data on consecutive stroke patients admitted to a large university stroke centre from two identical 7-month periods in 2017 and 2018 between which the e-Stroke Suite (Brainomix, Oxford, UK) was implemented to analyse non-contrast CT and CT angiography results. Delivery of stroke care was otherwise unchanged. Patients were transferred to a hub for thrombectomy. We collected the number of patients receiving intravenous thrombolysis and/or thrombectomy, the time to treatment; and outcome at 90 days for thrombectomy | ||
| 520 | |a RESULTS: 399 patients from 2017 and 398 from 2018 were included in the study. From 2017 to 2018, thrombolysis rates increased from 11.5% to 18.1% with a similar trend for thrombectomy (2.8-4.8%). There was a trend towards shorter door-to-needle times (44-42 min) and CT-to-groin puncture times (174-145 min). There was a non-significant trend towards improved outcomes with thrombectomy. Qualitatively, physician feedback suggested that e-Stroke Suite increased decision-making confidence and improved patient flow | ||
| 520 | |a CONCLUSIONS: Use of artificial intelligence decision support in a hyperacute stroke pathway facilitates decision-making and can improve rate and time of reperfusion therapies in a hub-and-spoke system of care | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Stroke | |
| 650 | 4 | |a Thrombectomy | |
| 650 | 4 | |a Thrombolysis | |
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