Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.
10.47102/annals-acadmedsg.2024232
- Author:
Ziwei LIN
1
;
Tar Choon AW
2
;
Laurel JACKSON
3
;
Cheryl Shumin KOW
4
;
Gillian MURTAGH
3
;
Siang Jin Terrance CHUA
5
;
Arthur Mark RICHARDS
6
;
Swee Han LIM
7
Author Information
1. Department of Emergency Medicine, Sengkang General Hospital, Singapore.
2. Department of Laboratory Medicine, Changi General Hospital, Singapore.
3. Core Diagnostics, Abbott Laboratories, Lake Forest, Illinois, US.
4. Department of General Surgery, Singapore General Hospital, Singapore.
5. Department of Cardiology, National Heart Centre Singapore, Singapore.
6. Christchurch Heart Institute, Department of Medicine, University of Ontago, Christchurch, New Zealand.
7. Department of Emergency Medicine, Singapore General Hospital, Singapore.
- Publication Type:Journal Article
- Keywords:
cardiology;
emergency medicine;
machine learning;
myocardial infarction;
troponin behaviour
- MeSH:
Humans;
Male;
Female;
Emergency Service, Hospital;
Middle Aged;
Electrocardiography;
Machine Learning;
Singapore;
Chest Pain/blood*;
Troponin I/blood*;
Myocardial Infarction/blood*;
Risk Assessment/methods*;
Aged;
Algorithms;
Acute Coronary Syndrome/blood*;
Adult;
Sensitivity and Specificity
- From:Annals of the Academy of Medicine, Singapore
2025;54(4):219-226
- CountrySingapore
- Language:English
-
Abstract:
INTRODUCTION:Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction.
METHOD:Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI.
RESULTS:There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV.
CONCLUSION:The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
