Prehospital triage of acute aortic syndrome using a machine learning algorithm

Abstract

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 < 0·001) in the training cohort, and 0·67 (0·60 to 0·74) and 0·73 (0·66 to 0·79) in the validation cohort (P = 0·038). The logistic regression score was associated with undertriage and overtriage rates of 33·7 (bootstrapped 95 per cent c.i. 29·3 to 38·3) and 7·2 (4·8 to 9·8) per cent respectively, whereas the SL yielded undertriage and overtriage rates of 1·0 (0·3 to 2·0) and 30·2 (25·8 to 34·8) per cent respectively. Conclusion A machine learning prediction model performed well in discriminating AAS and could be clinically useful in prehospital triage of patients with suspected AAS.

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

“…Although machine learning-based prediction algorithms have shown promising results with high accuracy in other fields, including stroke and acute aortic syndrome 9 , 10 , to the best of our knowledge, only one study has reported the efficacy of a machine learning-based prediction model for the prehospital onset of ACS using only 12-lead ECG 11 . In contrast, in our study, we built the models on the basis of 3-lead ECG monitoring, as well as vital signs and symptoms, which can be easily obtained without special equipment and technical training in a prehospital setting.…”

Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms

“…A machine learning prediction model has been shown to be more accurate than prior models using LR in prehospital triage in the patients with acute aortic syndrome (AAS). 40 And this study has proved that machine learning methods are more accurate than LR in the prediction of ICU admission. Specifically, our proposed model could be used to identify patients who will be admitted to ICU postoperatively before the operation, and improve the allocation of limited ICU resources reasonably.…”

Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches

“…Of the nine studies included in the review, common prehospital data elements used in ML models targeting early detection of time-sensitive conditions or prediction of patient outcomes include the following: (1) demographics, such as age and gender; chief complaints 15 16 20 21 22 24 25 ; (2) prehospital level of consciousness such as Glasgow coma scale (GCS) 19 20 22 25 ; (3) prehospital vital signs recorded in the ambulance or in the field 16 21 22 ; (4) prehospital actions, characterized as intubation, cardiopulmonary resuscitation (CPR), and chest decompression 16 18 21 22 ; or (5) prehospital situational assessments, described as mechanism of injury, witnessed arrest or CPR initiation, and presence of speech deficit and facial and limb weakness prior to arrival to ED. 15 18 21 22 Table 1 summarizes studies using prehospital data for clinical decision-making in ED based on their targets and modeling approaches.…”

“…ML has been found to be superior in performance in terms of accuracy, specificity, and sensitivity compared with statistically calculated screening tools for sepsis prediction. 16 20 21 24 32 38 Based on current and previous definitions of sepsis, the early detection of sepsis has been linked with the following screening tools: SIRS, the modified Early Warning Scores (MEWS), and SOFA and qSOFA scores. 6 39 However, SIRS, MEWS, SOFA, and qSOFA scores when compared with ML models have performed poorly in identification of sepsis and in predicting inpatient mortality from sepsis in ED.…”

“…For instance, Duceau et al used 32 prehospital and postarrival variables that included electrocardiogram (ECG), computed tomography (CT) scan, and transthoracic echocardiogram to predict acute aortic syndromes (AAS) and assess the performance of undertriage and overtriage. 24 Use of data elements that take a longer time to result such as diagnostic tests provides accuracy in performance but may delay the time to predict. 41 Contrarily, only using insufficient prehospital data, one model may be able to deliver prediction at arrival time but with potential cost of reduced performance.…”