Beyond Risk Scores: Concordance of a Multidisciplinary Decision Support Instrument with Real Life Treatment Decisions for Patients with Ruptured Abdominal Aortic Aneurysm

OBJECTIVES: Treatment decisions for patients with ruptured abdominal aortic aneurysm (rAAA) are ethically complex and must be made under time pressure. This prospective observational study assessed how a previously developed decision support instrument (DSI) for rAAA aligns with real life treatment decisions and determined a reference threshold for interpreting its output. The DSI estimates the proportion of experts from the original multidisciplinary expert panel who would be expected to favour operative treatment (OT) for a given patient profile. METHODS: Consecutive rAAA patients were included between March 2024 and January 2026 from two Dutch hospitals. Treating vascular surgeons completed the DSI after the actual treatment decision (OT or palliative care). DSI outputs were analysed in relation to observed treatment decisions. A threshold sweep analysis was performed to identify a contextual reference threshold reflecting the level of DSI estimated expert support at which OT was generally pursued. Post-operative outcomes were assessed in OT patients for clinical context. RESULTS: Fifty patients were included; OT was chosen in 43 patients (86%). DSI estimated expert support was higher for patients receiving OT compared with palliative care (median 98% [interquartile range 97, 99%] vs. 83% [59, 89%]; p < .001). The contextual reference threshold was 93%, with concordance of 94% (95% confidence interval 83.8 - 97.9%). Case analysis highlighted challenges in capturing unresponsiveness due to profound hypovolaemic shock and assessing cardiac burden acutely Among patients surviving OT, relatively high health related quality of life was observed. CONCLUSIONS: The DSI shows concordance with real life treatment decisions for rAAA. A contextual reference threshold above which operative treatment was generally pursued could be empirically derived. Although refinement is needed to better identify certain high risk patients, these findings represent an important first step in the validation pathway and provide proof of concept for behavioural artificial intelligence technology methodology.