{"id":"c34de73d3938","type":"article","url":"https://hartvaat.nl/2026/03/30/adapt-cec-adaptief-ai-algoritme-voor-cardiovasculaire-event-adjudicatie-in-klini/","title":"ADAPT-CEC: adaptief AI-algoritme voor cardiovasculaire event-adjudicatie in klinische trials","title_en":"","category":"algemeen","category_label":"Algemeen","professions":["cardioloog"],"tags":[],"journal":"Circulation","doi":"https://www.ahajournals.org/doi/abs/10.1161/CIRCULATIONAHA.126.080072","source_url":"https://doi.org/https://www.ahajournals.org/doi/abs/10.1161/CIRCULATIONAHA.126.080072","authors":["Sreekanth Vemulapalli"],"significance":6,"published":"2026-04-28","source_date":"2026-03-30","image":"","kennis":["https://hartvaat.nl/kennis/cardiometabool/inflammatie-en-atherosclerose/","https://hartvaat.nl/kennis/farmacologie/sglt2-remmers-cardiovasculaire-trials/"],"congress":"","summary_en":"Clinical endpoint classification (CEC) is the gold standard in CV trials but adds time and cost. ADAPT-CEC was developed on ODYSSEY OUTCOMES (MI, stroke, HF) and externally validated on EUCLID (MI, stroke, bleeding, CV death). It matches GPT-4 on F1 and offers a hybrid mode in which only the 30% of events with the lowest AI certainty go to human adjudication. Material implications for trial design: substantial time and cost savings without loss of validity.","created":"2026-07-03T10:32:49Z","updated":"2026-07-03T13:31:44Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Klinische endpointadjudicatie (CEC) is de gouden standaard in CV-trials maar kostbaar en tijdrovend. ADAPT-CEC werd ontwikkeld op ODYSSEY OUTCOMES (MI, stroke, HF) en extern gevalideerd op EUCLID (MI, stroke, bloeding, CV-dood). Het model evenaart GPT-4 in F1-score en biedt een hybride modus waarin alleen de 30% onzekerste events menselijke adjudicatie krijgen. Relevant voor trialdesign: substantiële tijd- en kostenreductie zonder verlies aan validiteit.","abstract_original":"Background: Clinical endpoint classification (CEC) is the gold standard for cardiovascular endpoint measurement in clinical trials, but adds time and cost. We developed and validated an artificial intelligence (AI) algorithm (ADAPT-CEC) that adjudicates multiple cardiovascular endpoints and adapts to new definitions.Methods: ADAPT-CEC was derived on myocardial infarction (MI), stroke, and heart failure from the ODYSSEY OUTCOMES trial and externally validated on MI, stroke, bleeding and CV death from the EUCLID trial after adaptation with 20 EUCLID suspected events per endpoint. ADAPT-CEC was compared via F1 score with direct generative pretrained transformer (GPT) 4.0 adjudication and a hybrid approach where the 30% of suspected events with the lowest AI prediction certainty used human adjudication. The EUCLID primary endpoint of CV death, MI, or stroke was re-estimated for all three adjudication strategies.Results: Amongst 13,885 suspected EUCLID primary endpoint events, ADAPT CEC, hybrid, and GPT 4.0 strategies correctly classified 86.4%, 95.6%, and 76.3% of all endpoints and 99.4%, 99.6%, and 99.8% of all non-endpoints compared with human adjudication, respectively. Hybrid adjudication F1 metrics were the highest [CV death (0.94, 95% CI 0.92 – 0.96), MI (0.80, 95% CI 0.77 – 0.82), stroke (0.82; 95% CI 0.78 – 0.86), bleeding (0.83, 95% CI 0.82 – 0.85)]. ADAPT-CEC F1 metrics were lower for CV death, MI, and stroke but similar to GPT 4.0 while bleeding (0.78, 95% CI 0.77 – 0.79) was superior to GPT 4.0. The EUCLID primary treatment effect was similar by human adjudication (HR 1.02, 95% CI 0.93 – 1.13), hybrid (HR 1.04; 95% CI 0.94 – 1.15) ADAPT-CEC (HR 0.98, 95% CI 0.88 – 1.09) and GPT 4.0 (1.06, 95% CI 0.95 – 1.19) adjudication.Conclusions: After brief adaptation, a single trial derived AI algorithm can adjudicate similar (MI and stroke) and new endpoints (CV death and bleeding) in a second trial and replicate the EUCLID primary outcome treatment effect. A hybrid approach with humans adjudicating those suspected events with the lowest 30% of ADAPT-CEC prediction certainty was superior to ADAPT-CEC alone or GPT 4.0 alone and replicated the EUCLID primary outcome treatment effect. Prospective studies of adaptive AI adjudication are needed to determine future trial implementation."}