Duceau B, Alsac JM, Bellenfant F, et al. - As acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions that necessitate emergency specialist management, researchers sought to develop a prediction algorithm to aid prehospital triage of AAS. A prospective collection of details of consecutive patients enrolled in a regional specialist aortic network was done. They developed two prediction algorithms for AAS based on logistic regression and an ensemble machine learning method called SuperLearner. They included data from 976 hospital admissions between February 2010 and June 2017; of these, 609 (62·4%) had AAS. Overtriage rate of 52·3% and undertriage rate of 16·1% were reported. Outcomes support the machine learning prediction model as clinically valuable in prehospital triage of patients with suspected AAS given its good performance in discriminating AAS.