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TRUVACE RECORD VERSION
record: TRV-2026-1167
version: 2
kind: revised
reason: Direction audit: the problem line described the method, not an outcome; restated as the finding.
timestamp: 2026-10-26T06:38:40.505346Z
status: published
lens: g_space
sector: crime
headline: Radial wall strain for residual risk stratification after percutaneous coronary intervention
dek: Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging. We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (…
gain_title: An AI algorithm enables real-time automated radial wall strain assessment from routine angiography, avoiding complex modelling and costly intracoronary imaging, to flag vulnerable plaques.
problem_title: (none)
trace_subject: (none)
gain_reading: An AI algorithm enables real-time automated radial wall strain assessment from routine angiography, avoiding complex modelling and costly intracoronary imaging, to flag vulnerable plaques.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging.

We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (RWS) from routine angiography. A prespecified cutoff value of maximal RWS (RWS max ) ≥13% was used to define increased plaque vulnerability.
limitation: 
tag: Evidence-backed gain
key_points: Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. | Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging. | We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (RWS) from routine angiography.
rundown: Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging.

We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (RWS) from routine angiography. Aims This post hoc study sought to determine the prognostic value of RWS for predicting future cardiac events in non-target vessels over 5 years, using the high-quality TARGET All Comers randomised trial.
sources:
- peer_reviewed | EuroIntervention | https://doi.org/10.4244/eij-d-26-00126 | 2026-09-21
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SHA-256 hash

974997dc5779126611617af5145db9edd94c312e386ffd113ff64ecc184f3b84

Previous version’s hash

f92bcf28841f3dec319810442f031dc14901b23fbc18153de1c8a2f7c140485c