Abstract TMP57: User Engagement is Associated With Clinical Impacts of Automated LVO Detection Software

Abstract

Introduction: Machine learning (ML)-based acute ischemic stroke (AIS) large vessel occlusion (LVO) detection algorithms can reduce door to groin time (DTG). Here, we examine whether the dependency of these benefits on end user engagement and interaction. Methods: This analysis was conducted as a pre-planned post hoc analysis of our multicenter, prospective randomized clinical trial (NCT05838456). ML-based LVO detection software was implemented at 4 comprehensive stroke centers in the greater Houston area in a stepped fashion between Jan 2021 and March 2022. Patients were included in this analysis if they underwent EVT for LVO AIS. Exclusion criteria included inter-hospital transfers or inpatient stroke alerts. ML-software utilization was quantified as the number of software interactions including imaging viewing and/or HIPAA-compliant text messaging and was trichotomized at the hospital level into low/medium/high. Primary outcome was the reduction in DTG relation to pre-ML implementation by hospital utilization level. Secondary outcomes included CT to groin puncture time, and sensitivity analyses in subsets of patients who did and did not have an LVO alert sent. Results: Among 243 patients that met inclusion criteria, median age was 70 (IQR 58-79), 50% were female and median NIHSS was 17 (IQR 11-22). There were 2 high-utilization centers interacting with the ML software mean 160 times, one medium center that interacted 64 times and one low center interacting 42 times during the study period. We observed a reduction on DTG of 11 minutes in the high-interaction center (p<0.01) but no significant reduction in DTG in the medium or low-interaction centers. Similarly, time from CT scan initiation to groin puncture fell in the high-utilization centers by 32 minutes (p<0.05) but no significant change in others. Without adjusting for hospital-level software utilization, there was no statistically significant reduction in DTG for patients analyzed by the ML software versus those that were not (p=0.35). Conclusion: The benefit of ML-enhanced LVO AIS workflows are dependent on user engagement.