Deans' stroke musings: Machine Learning–Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Friday, March 29, 2024

Machine Learning–Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers

But still not fast enough to get to 100% recovery and you're not even measuring that!

Survivors want to know how many got to 100% recovery! If you're not measuring that you'll never get there, and just to make sure I'd have you all fired for incompetently not even understanding the only goal in stroke for survivors is 100% recovery!

“What's measured, improves.” So said management legend and author Peter F. Drucker

Machine Learning–Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers

Ngoc Mai Le,

Ananya S. Iyyangar,

Youngran Kim,

Mohammad Rauf Chaudhry,

Sergio Salazar‐Marioni,

Originally published29 Mar 2024https://doi.org/10.1161/SVIN.123.001119Stroke: Vascular and Interventional Neurology. 2024;0:e001119

Abstract

BACKGROUND

Accelerating door‐in‐door‐out (DIDO) times at primary stroke centers (PSCs) for patients with large vessel occlusion (LVO) acute ischemic stroke transferred for possible endovascular stroke therapy (EVT) is important to optimize outcomes. Here, we assess whether automated LVO detection coupled with secure communication at non‐EVT performing PSCs improves DIDO time and increases the proportion of patients receiving EVT after transfer.

METHODS

From our prospectively collected multicenter registry, we identified patients with LVO acute ischemic stroke that presented to one of 7 PSCs in the Greater Houston area from January 1, 2021, to February 27, 2022. Noncontrast computed tomography and computed tomographic angiography were performed in all patients at the time of presentation, per standard of care. A machine learning (artificial intelligence [AI]) algorithm trained to detect LVO (Viz.AI) from computed tomographic angiography was implemented at all 7 hospitals. The primary outcome of the study was DIDO at the PSCs and was determined using multivariable linear regression adjusted for sex and on/off hours. Secondary outcomes included likelihood of receiving EVT post‐transfer.

RESULTS

Among 115 patients who met inclusion criteria, 80 were evaluated pre‐AI and 35 post‐AI. The most common occlusion locations were middle cerebral artery (51.3%) and internal carotid artery (25.2%). There were no substantial differences in demographics or presentation characteristics between the 2 groups. Median time from onset to PSC arrival was 117 minutes (interquartile range, 54–521 minutes). In univariable analysis, patients evaluated at the PSCs after AI implementation had a shorter DIDO time (median difference, 77 minutes; P<0.001). In multivariable linear regression, patients evaluated with automated LVO detection AI software were associated with a 106‐minute (95% CI, −165 to −48 minutes) reduction in DIDO time but no difference in likelihood of EVT post‐transfer (odd ratio, 2.13 [95% CI, 0.88–5.13).

CONCLUSION

Implementation of a machine learning method for automated LVO detection coupled with secure communication resulted in a substantial decrease in DIDO time at non‐EVT performing PSCs.

Adapted from Biomedical Image Analysis (AI vs Traditional Techniques), by BioRender.com (2023). Retrieved from https://app.biorender.com/biorender‐templates .