Prehospital triage of acute aortic syndrome using a machine learning algorithm

Background Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS. Methods Details of consecutive patients enrolled in a regiona...

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Bibliographic Details
Published in:British journal of surgery 2020-07, Vol.107 (8), p.995-1003
Main Authors: Duceau, B., Alsac, J.‐M., Bellenfant, F., Mailloux, A., Champigneulle, B., Favé, G., Neuschwander, A., El Batti, S., Cholley, B., Achouh, P., Pirracchio, R.
Format: Article
Language:English
Subjects:
Acute Disease
Aged
Algorithms
Aortic Diseases - diagnosis
Aortic Diseases - mortality
Aortic Diseases - therapy
Clinical Decision Rules
Emergency Medical Services - methods
Female
Hospital Mortality
Humans
Logistic Models
Machine Learning
Male
Middle Aged
Prognosis
Prospective Studies
Reproducibility of Results
Syndrome
Triage - methods
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Summary:Background Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS. Methods Details of consecutive patients enrolled in a regional specialist aortic network were collected prospectively. Two prediction algorithms for AAS based on logistic regression and an ensemble machine learning method called SuperLearner (SL) were developed. Undertriage was defined as the proportion of patients with AAS not transported to the specialist aortic centre, and overtriage as the proportion of patients with alternative diagnoses but transported to the specialist aortic centre. Results Data for 976 hospital admissions between February 2010 and June 2017 were included; 609 (62·4 per cent) had AAS. Overtriage and undertriage rates were 52·3 and 16·1 per cent respectively. The population was divided into a training cohort (743 patients) and a validation cohort (233). The area under the receiver operating characteristic (ROC) curve values for the logistic regression score and the SL were 0·68 (95 per cent c.i. 0·64 to 0·72) and 0·87 (0·84 to 0·89) respectively (P 
ISSN:0007-1323
1365-2168
DOI:10.1002/bjs.11442