Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times A Cluster Randomized Clinical Trial

Juan Carlos Martinez-Gutierrez, Youngran Kim, Sergio Salazar-Marioni, Muhammad Bilal Tariq, Rania Abdelkhaleq, Arash Niktabe, Anjan N. Ballekere, Ananya S. Iyyangar, Mai Le, Hussain Azeem, Charles C. Miller, Jon E. Tyson, Sandi Shaw, Peri Smith, Mallory Cowan, Isabel Gonzales, Louise D. McCullough, Andrew D. Barreto, Luca Giancardo, Sunil A. Sheth

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


IMPORTANCE The benefit of endovascular stroke therapy (EVT) in large vessel occlusion (LVO) ischemic stroke is highly time dependent. Process improvements to accelerate in-hospital workflows are critical. OBJECTIVE To determine whether automated computed tomography (CT) angiogram interpretation coupled with secure group messaging can improve in-hospital EVT workflows. DESIGN, SETTING, AND PARTICIPANTS This cluster randomized stepped-wedge clinical trial took place from January 1, 2021, through February 27, 2022, at 4 comprehensive stroke centers (CSCs) in the greater Houston, Texas, area. All 443 participants with LVOstrokewhopresented through the emergency departmentwere treated with EVT at the 4 CSCs. Exclusion criteria included patients presenting as transfers from an outside hospital (n = 158), in-hospital stroke (n = 39), and patients treated with EVT through randomization in a large core clinical trial (n = 3). INTERVENTION Artificial intelligence (AI)-enabled automated LVO detection from CT angiogram coupled with secure messaging was activated at the 4 CSCs in a random-stepped fashion. Once activated, clinicians and radiologists received real-time alerts to their mobile phones notifying them of possible LVO within minutes of CT imaging completion. MAIN OUTCOMES AND MEASURES Primary outcomewas the effect of AI-enabled LVO detection on door-to-groin (DTG) time and was measured using a mixed-effects linear regression model, which included a random effect for cluster (CSC) and a fixed effect for exposure status (pre-AI vs post-AI). Secondary outcomes included time from hospital arrival to intravenous tissue plasminogen activator (IV tPA) bolus in eligible patients, time from initiation of CT scan to start of EVT, and hospital length of stay. In exploratory analysis, the study team evaluated the impact of AI implementation on 90-day modified Rankin Scale disability outcomes. RESULTS Among 243 patients whomet inclusion criteria, 140were treated during the unexposed period and 103 during the exposed period. Median age for the complete cohortwas 70(IQR, 58-79) years and 122were female (50%). Median National Institutes of Health Stroke Scale score at presentationwas 17 (IQR, 11-22) and the medianDTGpreexposurewas 100 (IQR, 81-116) minutes. In mixed-effects linear regression, implementation of the AI algorithmwas associated with a reduction inDTGtime by 11.2 minutes (95%CI, -18.22 to -4.2). Time from CT scan initiation to EVT start fell by 9.8 minutes (95%CI, -16.9 to -2.6). Therewere no differences in IV tPA treatment times nor hospital length of stay. In multivariable logistic regression adjusted for age, National Institutes of Health Stroke scale score, and the Alberta Stroke ProgramEarly CT Score, therewas no difference in likelihood of functional independence (modified Rankin Scale score, 0-2; odds ratio, 1.3; 95%CI,0.42-4.0). CONCLUSIONS AND RELEVANCE Automated LVO detection coupled with secure mobile phone application-based communication improved in-hospital acute ischemic stroke workflows. Software implementation was associated with clinically meaningful reductions in EVT treatment times.

Original languageEnglish
Pages (from-to)1182-1190
Number of pages9
JournalJAMA Neurology
Issue number11
Publication statusPublished - 13 Nov 2023
Externally publishedYes


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