The implementation of artificial intelligence significantly reduces door-in-door-out times in a primary care center prior to transfer

Abstract

Introduction

Viz LVO artificial intelligence (AI) software utilizes AI-powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. This analysis was performed to determine whether AI software can reduce the door-in-door-out (DIDO) time interval within the primary care center (PSC) prior to transfer to the comprehensive care center (CSC).

Methods

We compared the DIDO time interval for all LVO transfer patients from a single-spoke PSC to our CSC prior to (February 2017 to November 2018) and after (November 2018 to June 2020) incorporating AI. Using a stroke database at a CSC, demographics, DIDO time at PSC, modified Rankin Scale (mRS) at 90-days, mortality rate at discharge, length of stay (LOS), and intracranial hemorrhage rates were examined.

Results

There were a total of 63 patients during the study period (average age 69.99 ± 13.72, 55.56% female). We analyzed 28 patients pre-AI (average age 71.64 ± 12.28, 46.4% female), and 35 patients post-AI (average age 68.67 ± 14.88, 62.9% female). After implementing the AI software, the mean DIDO time interval within the PSC significantly improved by 102.3 min (226.7 versus 124.4 min; p = 0.0374).

Conclusion

The incorporation of the AI software was associated with a significant improvement in DIDO times within the PSC as well as CTA to door-out time in the PSC. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

Keywords

stroke thrombectomy thrombolysis artificial intelligence

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References

Campbell

Mitchell

Kleinig

, et al.; investigators E-I. Endovascular therapy for ischemic stroke with perfusion-imaging selection. N Engl J Med 2015; 372: 1009–1018.

Goyal

Demchuk

Menon

, et al.; investigators ET. Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med 2015; 372: 1019–1030.

Berkhemer

Fransen

Beumer

, et al. Investigators MC. A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med 2015; 372: 11–20.

Saver

Goyal

Bonafe

, et al.; investigators SP. Stent-retriever thrombectomy after intravenous t-pa vs T-pa alone in stroke. N Engl J Med 2015; 372: 2285–2295.

Jovin

Chamorro

Cobo

, et al.; investigators RT. Thrombectomy within 8 h after symptom onset in ischemic stroke. N Engl J Med 2015; 372: 2296−2306.

Mokin

Kass-Hout

, et al. Intravenous thrombolysis and endovascular therapy for acute ischemic stroke with internal carotid artery occlusion: a systematic review of clinical outcomes. Stroke 2012; 43: 2362–2368.

Saver

. Time is brain—quantified. Stroke 2006; 37: 263–266.

Albers

Marks

Kemp

, et al. Thrombectomy for stroke at 6 to 16 h with selection by perfusion imaging. N Engl J Med 2018; 378: 70818.

Nogueira

Jadhav

Haussen

, et al. Thrombectomy 6 to 24 h after stroke with a mismatch between deficit and infarct. N Engl J Med 2018; 378: 11–21.

10.

Goyal

Menon

van Zwam

, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomized trials. Lancet 2016; 387: 1723–1731.

11.

Malhotra

Gornbein

Saver

. Ischemic strokes due to large-vessel occlusions contribute disproportionately to stroke-related dependence and death: a review. Front Neurol 2017; 8: 651.

12.

Murray

Unberath

Hager

, et al. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review. J Neurointerv Surg 2020; 12: 156–164.

13.

Hassan

Ringheanu

Rabah

, et al. Early experience utilizes artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model. Interv Neuroradiol 2020; 26: 615–622.

14.

Rodrigues

Barreira

Bouslama

, et al. Automated large artery occlusion detection in stroke: a single-center validation study of an artificial intelligence algorithm. Cerebrovasc Dis 2022; 51(2): 259–264.

15.

Fonarow

Smith

Saver

, et al. Improving door-to-needle times in acute ischemic stroke. The design and rationale for the American heart association/American stroke association’s target: stroke initiative. Stroke 2011; 42: 2983–2989.

16.

Tilley

Lyden

Brott

, et al. Total quality improvement method for reduction of delays between emergency department admission and treatment of acute ischemic stroke. The national institute of neurological disorders and stroke rt-PA stroke study group. Arch Neurol 1997; 54: 1466–1474.

17.

Goyal

Jadhav

Bonafe

, et al. Analysis of workflow and time to treatment and the effects on outcome in endovascular treatment of acute ischemic stroke: results from the SWIFT PRIME randomized controlled trial. Radiology 2016; 279: 888–897.

18.

Low

Andrew

, et al. Deconstruction of interhospital transfer workflow in large vessel occlusion: real-world data in the thrombectomy era. Stroke. 2017; 48: 1976–1979.