Abstract
Introduction Automated machine learning (ML)âbased large vessel occlusion (LVO) detection algorithms have been shown to improve inâhospital workflow metrics including door to groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized. Methods This analysis was conducted as a preâplanned post hoc analysis of our multicenter, prospective randomized clinical trial (NCT05838456). MLâbased 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. Patients were excluded if they presented as interâhospital transfers or inpatient stroke alerts, as these workflows diverge considerably from ERâbased ones. MLâsoftware utilization quantified as the number of software interactions including imaging viewing and/or HIPAAâcompliant text messaging and was trichotomized at the hospital level into low/medium/high. Primary outcome was the reduction in DTG relation to preâML 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 58â79), 50% were female and median NIHSS was 17 (IQR 11â22). MLâsoftware utilization varied considerably with highâutilization centers (n=2) interacting with mean 160 times, medium (n=1) 64 times and low center (n=1) 42 times during the study period. We observed a reduction on DTG of 11 minutes in the highâinteraction center (p<0.01) but no significant reduction in DTG in the medium or lowâinteraction centers. Similarly, time from CT scan initiation to groin puncture fell in the highâutilization centers by 32 minutes (p<0.05) but no significant change in others. Without adjusting for hospitalâlevel 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 MLâbased workflow improvements are dependent on care team adoption and utilization.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.