Hong Kong Med J 2024 Aug;30(4):271–80 | Epub 25 Jul 2024
© Hong Kong Academy of Medicine. CC BY-NC-ND 4.0
 
ORIGINAL ARTICLE  CME
Diagnostic accuracy of a prehospital electrocardiogram rule-based algorithm for ST-elevation myocardial infarction: results from a population-wide project
Joanne HY Lai, MB, BS, FHKAM (Emergency Medicine)1; CT Lui, MB, BS, FHKAM (Emergency Medicine)1; Total WT Chan, MB, ChB, FHKAM (Emergency Medicine)2; Ben CP Wong, MB, ChB, FHKAM (Emergency Medicine)2; Matthew SH Tsui, MB, BS, FHKAM (Emergency Medicine)3; Ben KA Wan, MB, BS, FHKAM (Emergency Medicine)4,5; KL Mok, MB, BS, FHKAM (Emergency Medicine)4,5
1 Department of Accident and Emergency, Tuen Mun Hospital, Hong Kong SAR, China
2 Department of Accident and Emergency, Tin Shui Wai Hospital, Hong Kong SAR, China
3 Department of Accident and Emergency, Queen Mary Hospital, Hong Kong SAR, China
4 Department of Accident and Emergency, Ruttonjee & Tang Shiu Kin Hospitals, Hong Kong SAR, China
5 Fire Services Department, Hong Kong SAR, China
 
Corresponding author: Dr Joanne HY Lai (joannelaihy@fellow.hkam.hk)
 
 Full paper in PDF
 
Abstract
Introduction: This study reviewed the diagnostic accuracy of the prehospital electrocardiogram (PHECG) rule-based algorithm for ST-elevation myocardial infarction (STEMI) universally utilised in Hong Kong.
 
Methods: This prospective observational study was linked to a population-wide project. We analysed 2210 PHECGs performed on patients who presented to the emergency medical service (EMS) with chest pain from 1 October to 31 December 2021. The diagnostic accuracy of the adopted rule-based algorithm, the Hannover Electrocardiogram System, was evaluated using the adjudicated blinded rating by two investigators as the primary reference standard. Diagnostic accuracy was also evaluated using the attending emergency physician’s diagnosis and the diagnosis on hospital discharge as secondary reference standards.
 
Results: The prevalence of STEMI was 5.1% (95% confidence interval [CI]=4.2%-6.1%). Using the adjudicated blinded rating by investigators as the reference standard, the rule-based PHECG algorithm had a sensitivity of 94.6% (95% CI=88.2%-97.8%), specificity of 87.9% (95% CI=86.4%-89.2%), positive predictive value of 29.4% (95% CI=24.8%-34.4%), and negative predictive value of 99.7% (95% CI=99.3%-99.9%) [all P<0.05].
 
Conclusion: The rule-based PHECG algorithm that is widely used in Hong Kong demonstrated high sensitivity and fair specificity for the diagnosis of STEMI.
 
 
New knowledge added by this study
  • The prehospital electrocardiogram (PHECG) diagnostic algorithm universally utilised in Hong Kong had high sensitivity for diagnosing ST-elevation myocardial infarction (STEMI) in a population-wide cohort of patients with chest pain.
  • One in eight ECGs showed false-positive results for STEMI; the leading causes were early repolarisation, left bundle branch block, and extreme tachycardia.
  • Evolving ECG patterns, subtle ST-segment elevation, and STEMI equivalents were responsible for false-negative diagnoses.
Implications for clinical practice or policy
  • Primary diversion of STEMI patients to centres capable of primary percutaneous coronary intervention should not be implemented solely based on the algorithm’s ECG diagnosis.
  • ST-elevation myocardial infarction can be reasonably excluded by the PHECG diagnostic algorithm.
  • Physicians should be aware of STEMI equivalents that are not identified by the algorithm.
 
 
Introduction
Heart disease is the third leading cause of death in Hong Kong. In 2019, an average of approximately 10.2 people died from coronary heart disease each day.1 International guidelines recommend prehospital 12-lead electrocardiogram (ECG) for the assessment of patients with suspected acute coronary syndrome who present to emergency medical services (EMS).2 3 Prehospital triage with direct transfer to the cardiac catheterisation laboratory for primary percutaneous coronary intervention is a strategy adopted by various healthcare systems to reduce reperfusion time in patients with ST-elevation myocardial infarction (STEMI).4 5 Previous studies have investigated the diagnostic performances of prehospital electrocardiograms (PHECGs) for STEMI by various automated algorithms,6 7 8 9 10 trained onsite EMS personnel,11 12 and emergency department (ED) physicians remotely interpreting the tele-transmitted ECGs13; the findings have implications for policymakers involved in planning systems of care to minimise inappropriate resource mobilisation.
 
In Hong Kong, the Hospital Authority, the local public healthcare service, and Hong Kong Fire Services Department, the primary EMS provider, jointly launched the Prehospital 12-Lead Electrocardiogram for Chest Pain Protocol on 1 February 2021. The Protocol covers the catchment areas of all EDs in Hong Kong and serves a population of 7.41 million. This study utilised data from a territory-wide audit of the Protocol to determine the diagnostic performance of the PHECG algorithm for STEMI.
 
Methods
Study design and setting
This prospective observational study analysed data from the territory-wide audit project regarding the Prehospital 12-Lead Electrocardiogram for Chest Pain Protocol, led by the Hong Kong Hospital Authority Coordinating Committee in Accident and Emergency. The Protocol was designed to include all patients with complaints of chest pain, excluding those <12 years of age; in cardiac arrest; with unmanageable airway or breathing; a Glasgow Coma Scale score of ≤13; a first systolic blood pressure of <90 mm Hg; a respiratory rate of <10 or >29 breaths per minute; or refusal or inability to give consent.
 
Ambulances were equipped with 12-lead ECG machines capable of automatic algorithm-based diagnosis. The selected machine model was a corpuls3 Monitor and Defibrillator (GS Elektromedizinische Geräte G Stemple GmbH, Kaufering, Germany), with the telemedicine application corpuls.mission (GS Elektromedizinische Geräte G Stemple GmbH, Kaufering, Germany). The selected ECG algorithm was the ECG diagnostic algorithm of the Hannover ECG System (Corscience GmbH & Co KG, Erlangen, Germany).
 
Upon encountering a patient who met the Protocol’s criteria, the ambulance personnel performed a 12-lead ECG on scene or in the stationary ambulance compartment. The ECG was immediately analysed by the computer algorithm and classified as ‘STEMI’, ‘Not STEMI’, or ‘N/A’ (not interpretable due to suboptimal ECG quality). Additional ECGs were performed as necessary to improve quality. The ECG(s) were tele-transmitted to the ED serving the particular catchment area for reading and interpretation by the ED attending physician. If the ECG was classified as ‘STEMI’ by the computer algorithm, the EMS personnel also directly called to alert the ED. The ED prepared the resuscitation room for patient arrival if the ECG was classified as STEMI by the ED physician.
 
Study population and data collection
This study adhered to the STARD (Standards for Reporting of Diagnostic Accuracy Studies) 2015 reporting guideline. Patients with PHECGs performed in accordance with the Protocol throughout Hong Kong were prospectively recruited from 1 October to 31 December 2021.
 
Prehospital ECGs performed and tele-transmitted during the study period were obtained from corpuls.mission’s online database and matched to clinical data from the Clinical Data Analysis and Reporting System and Accident and Emergency Information System (Information Technology and Health Informatics Division, Hospital Authority, Hong Kong). Electrocardiograms without matching patient data and those classified as ‘N/A’ by the algorithm were excluded from the analysis.
 
Three reference standards were used to investigate the diagnostic accuracy of the computer algorithm. The first reference standard, the primary outcome, was adjudicated blinded rating of the ECG. Each ECG was de-identified and independently interpreted as ‘STEMI’, ‘Not STEMI’ or ‘Not interpretable’ by two investigators: an emergency physician with ≥5 years of experience in emergency medicine practice and a specialist in Emergency Medicine. Electrocardiograms for which there was disagreement between the interpretations of the two blinded raters were classified according to the blinded interpretation of an adjudicator (a second Emergency Medicine specialist). The diagnosis of STEMI was based on the Fourth Universal Definition of Myocardial Infarction14 and the modified Sgarbossa criteria for left bundle branch block or ventricular paced rhythm.15 16 ST-elevation myocardial infarction mimics17 and STEMI equivalents, according to the 2022 ACC Expert Consensus Decision Pathway on the Evaluation and Disposition of Acute Chest Pain in the Emergency Department,18 were regarded as ‘Not STEMI’. ‘Not interpretable’ ECGs were those with substantial motion artefacts, wavering baseline, or disconnected lead(s); these ECGs were excluded from the analysis.
 
The second reference standard was the ED attending physician’s diagnosis, which considered the patient’s clinical condition, along with additional ECGs and other investigations performed upon arrival in the ED. Patients without ECGs performed in the ED were excluded from the analysis.
 
The third reference standard was the diagnosis on hospital discharge from the index admission. We excluded patients who died in the ED without an established diagnosis, who developed STEMI after admission, or were discharged with acknowledgement of medical advice and no definitive diagnosis.
 
Interrater agreement analysis was performed in three dimensions, namely, between the two blinded raters, between the adjudicated blinded rating and the ED diagnosis, and between the adjudicated blinded rating and the diagnosis on hospital discharge. If there was disagreement between the adjudicated blinded rating and the ED diagnosis, the prehospital and ED ECGs were reviewed by the principal investigator to differentiate between dynamic change or true disagreement. Dynamic change was defined as the lack of ST-segment elevation and ECG criteria fulfilment on the initial PHECG, with subsequent evidence on serial ECG performed in the ED.
 
False-positive and false-negative ECGs were reviewed and classified by the principal investigator. The following categories of ECG morphology were determined based on criteria described in existing literature: Brugada pattern,19 early repolarisation,20 left bundle branch block or paced rhythm not matching STEMI criteria,15 16 left ventricular hypertrophy,21 pericarditis,22 and ventricular ectopics.23
 
Statistical analysis
Continuous variables were presented as mean ± standard deviation and were analysed with the independent t test. Categorical variables were reported as absolute frequencies and percentages and were analysed with the Chi squared test or Fisher’s exact test. Interrater agreement regarding ECG diagnosis was analysed using Cohen’s kappa. Sensitivity, specificity, and predictive values were derived from 2 × 2 contingency tables and analysed with the Chi squared test.
 
The threshold for statistical significance was regarded as P<0.05. All statistical analyses were performed using SPSS software (Windows version 26.0; IBM Corp, Armonk [NY], US).
 
Results
Baseline characteristics
During the study period, 2801 PHECGs were performed, one for each patient who presented with chest pain. Of these ECGs, 2437 were matched to electronic patient records. After the exclusion of 103 ECGs classified as ‘N/A’ by the computer algorithm, 2334 ECGs were included in the analysis (Fig 1).
 

Figure 1. Patient selection for diagnostic accuracy analysis
 
The characteristics of the study population are presented in Table 1. Overall, 62.9% of the patients were men. The mean age of male patients, female patients, and both sexes were 63.9 years, 74.1 years, and 67.7 years, respectively. In total, 83.6% of patients were placed on stretchers upon arrival at the ED. Furthermore, 8.2% of patients were institutionalised in residential homes. Of the ECGs, 42.4% were performed during 0800 to 1559 hours, 35.4% were performed during 1600 to 2359 hours, and 22.3% were performed during 0000 to 0759 hours. A total of 405 (17.4%) PHECGs were classified as STEMI by the algorithm.
 

Table 1. Characteristics of the study population
 
Primary outcome
The primary outcome was diagnostic accuracy based on the adjudicated blinded rating. The prevalence of STEMI was 5.1% (Table 2). There was good interrater observed agreement (96.9%) between the two blinded ECG assessors. Cohen’s kappa was 0.84 (95% confidence interval [CI]=0.81-0.88; P<0.05) [Table 3]. The algorithm had a sensitivity of 94.6% (95% CI=88.2%-97.8%), specificity of 87.9% (95% CI=86.4%-89.2%), positive predictive value of 29.4% (95% CI=24.8%-34.4%), negative predictive value of 99.7% (95% CI=99.3%-99.9%), positive likelihood ratio of 7.8 (95% CI=6.9-8.8), and negative likelihood ratio of 0.06 (95% CI=0.03-0.13) [all P<0.05] (Table 2).
 

Table 2. Diagnostic performance of the prehospital electrocardiogram algorithm according to respective reference standards
 

Table 3. Analysis of interrater agreement
 
Secondary outcomes
Secondary outcomes were the algorithm’s diagnostic accuracy with reference to the ED attending physician’s diagnosis and to the diagnosis on hospital discharge.
 
Substantial agreement was observed between the diagnosis based on the adjudicated blinded rating and these two reference standards. Discrepancies in agreement between the adjudicated blinded rating of ECGs and these two reference standards reflected the presence of dynamic ECG changes. Observed agreement between the adjudicated blinded rating and ED physician’s diagnosis was 97.1%, with Cohen’s kappa of 0.69. Excluding patients with dynamic ECG changes in the ED, the observed agreement was 98.2% and Cohen’s kappa was 0.78. Observed agreement between the adjudicated blinded rating and final discharge diagnosis was 97.5%, with Cohen’s kappa of 0.74. Excluding patients with dynamic ECG changes in the ED, the observed agreement was 98.4% and Cohen’s kappa was 0.80 (Table 3). The diagnostic performance based on the three reference standards and the analysis of interrater agreement are summarised in Tables 2 and 3, respectively.
 
Characteristics of false-positive electrocardiograms
The 255 false-positive ECGs with the adjudicated blinded rating as the reference standard were reviewed and characterised as shown in Figure 2. The leading causes were early repolarisation (n=97; 38.0%), left bundle branch block (n=40; 15.7%), and tachycardia of >140 beats per minute (n=34; 13.3%). Excluding ECGs with suboptimal quality (classified as ‘N/A’ by the algorithm and ‘Not interpretable’ according to adjudicated blinded rating), false-positive ECGs due to artefacts constituted 8.6% (n=22).
 

Figure 2. Electrocardiogram features of false-positive electrocardiograms with the adjudicated blinded rating as the reference standard (n=255)
 
Characteristics of false-negative electrocardiograms
Using the diagnosis on hospital discharge as the reference standard, 22 STEMI cases were missed by the algorithm (Fig 3). Thirteen (59.1%) of the false-negative ECGs were due to the development of dynamic ECD changes in the ED; four (18.2%) of these had subtle ST-segment elevation. ST-segment elevation in lead augmented vector right and the STEMI equivalent morphology of de Winter’s T wave were noted in two (9.1%) ECGs each. One ECG was classified as ‘Not interpretable’ according to the adjudicated blinded rating because of substantial artefacts.
 

Figure 3. Electrocardiogram features of false-negative electrocardiograms with diagnosis on hospital discharge as the reference standard (n=22)
 
Discussion
Implications on prehospital care systems for ST-elevation myocardial infarction
This prospective observational study examined the diagnostic performance of a rule-based PHECG algorithm universally utilised in Hong Kong, based on three levels of reference standards. The primary outcome, adjudicated blinded rating, closely reflects diagnostic performance without the addition of patient clinical history and presentation or any other diagnostic aids. The American Heart Association recommends three levels of PHECG diagnosis, namely, EMS interpretation, computerised algorithm diagnosis, and ECG transmission for remote interpretation.24 However, in healthcare systems such as the Hospital Authority in Hong Kong, EMS are trained to perform but not interpret PHECGs. Thus, it is important to understand reliance on the computerised algorithm using the benchmark of physician-based remote interpretation; these data can guide the establishment and improvement of care systems.
 
One in eight of the PHECGs in this study showed false-positive results. Considering the fair specificity and positive predictive value of only 29.4% for the automated ECG diagnostic programme, this high false-positive rate reflected limitations in guiding prehospital treatment and streamlining care systems (eg, prehospital diversion to percutaneous coronary intervention–capable centres or prehospital triage for direct transfer to a cardiac catheterisation laboratory). A hybrid two-step ECG interpretation model, involving a physician’s remote (ie, telemedicine-based) interpretation of ECGs that are classified as STEMI by the computerised algorithm, could be adopted to minimise overactivation and ensure prudent use of healthcare resources. Nonetheless, the algorithm exhibited good sensitivity in terms of identifying STEMI patients. Its high negative predictive value allowed STEMI to be reasonably excluded based on ECG results. Although remote PHECG interpretation is considered relatively accurate, it generally results in STEMI misdiagnosis rates of 6% to 8%.13 Therefore, we included secondary outcomes, namely, the algorithm’s diagnostic performance based on the ED attending physician’s diagnosis and the final discharge diagnosis; we assessed interrater agreement between these reference standards. We adopted an operational approach focused on the ‘appropriateness of cardiac catheterisation laboratory activation’, rather than a strictly patient-centred approach based on primary percutaneous coronary intervention findings or cardiac biomarkers.
 
Diagnostic performance varies across electrocardiogram machine models and algorithms
The inclusion of three reference standards was intended to address the heterogeneous estimates of PHECG diagnostic performance for STEMI in existing literature. Prior studies have been based on various reference standards, including blinded physician rating,25 ED attending physician’s diagnosis,6 26 hospital discharge diagnosis,7 and the appropriateness of coronary angiography activation.8 The results have varied according to STEMI prevalence in the study population, as well as the reference standard, ECG machine, and computerised algorithm. Using ED clinical diagnosis as the reference standard, a single-centre pilot study in Hong Kong by Cheung et al6 utilising the X Series Monitor/Defibrillator and Inovise 12L Interpretive Algorithm (Zoll Medical Corporation, Chelmsford [MA], US) demonstrated a low sensitivity (53.8%) and high specificity (99.6%). Bhalla et al26 utilised LIFEPAK 12 monitors (Physio-Control, Redmond [WA], US) equipped with a Marquette 12SL ECG analysis programme (General Electric Company, Fairfield [CT], US) to evaluate PHECGs from 100 STEMI patients and 100 control participants; they found a similarly low sensitivity (58%) and very high specificity (100%). Bosson et al7 examined ECGs obtained with the LIFEPAK 15 monitor (Physio-Control, Inc, Minneapolis [MN], US) and analysed using the University of Glasgow 12-Lead ECG Analysis Programme (version 27); their results showed 92.8% sensitivity and 98.7% specificity, based on the reference standard of appropriateness for emergency coronary angiography.7 The prevalence of STEMI was much lower in their study than in our study (1.4%7 vs 5.1% [Table 2]) because their dataset also included PHECGs performed for symptoms other than chest pain. Using the same ECG machine model as the aforementioned study,7 Fakhri et al8 tested an automated analysis method with a high-specificity STEMI configuration. In a carefully selected STEMI population, the sensitivity and specificity were 69.8% and 51.5%, respectively, based on discharge diagnosis.8 A meta-analysis conducted by Tanaka et al27 suggested that computer-assisted ECG interpretation had a high pooled specificity (95.4%; 95% CI=87.3%-98.4%) with an acceptable estimated number of false-positive results, whereas the pooled sensitivity was relatively low (85.4%; 95% CI=74.1%-92.3%), for identifying STEMI on PHECG. All of these studies utilised ECG machines and diagnostic algorithms that differed from our method, emphasising that diagnostic performance varies across models; evaluations of specific ECG machines and algorithms should be conducted by individual healthcare systems to suit their operational needs.
 
Major patterns of false-positive and falsenegative electrocardiograms
The Hannover ECG System algorithm utilised in our study was one of nine computer programmes investigated in the international Common Standards for Quantitative Electrocardiography Diagnostic Study,28 using clinical diagnosis as the reference standard. This statistics-based algorithm exhibited one of the highest sensitivities (79.0%) for detecting myocardial infarction compared with all algorithms combined (72.2%); its sensitivity also was similar to that of the combined independent ratings of eight cardiologists (80.3%). However, its ability to correctly classify normal ECGs (86.6%) was lower than that of the combined ratings of cardiologists (97.1%) and the combined algorithms (96.7%). Our findings are consistent with the results of the Common Standards for Quantitative Electrocardiography Diagnostic Study. The presence of artefacts contributed to 8.6% of false-positive ECGs; this rate could be improved by enhancing ECG technique. The major patterns of misdiagnosis were early repolarisation (38.0%), left bundle branch block (15.7%), and tachycardia of >140 beats per minute (13.3%) [Fig 2]. Artefacts on ECG were responsible for the largest proportion of false-positive ECGs8; they contributed a smaller proportion in our dataset because we excluded ECGs considered ‘Not interpretable’ by the algorithm or blinded raters. Early repolarisation remained a leading cause of false-positive ECGs, and existing consensus papers on early repolarisation may help guide future algorithm development.20 29 Further collaboration with the software provider to optimise the algorithm may enhance its accuracy.
 
Among cases of STEMI missed by the algorithm using diagnosis on hospital discharge as the reference standard, more than half were caused by ECG changes after patient arrival in the ED. False-negative ECGs due to subtle ST-segment elevation represented only 3.45% of all STEMI patients. Remote physician interpretation of these PHECGs would likely be equivocal and uncertain. It presumably would not be beneficial to adjust the algorithm to correct this margin of error, considering the potential for additional false-positives. However, it might be useful to refine the algorithm for enhanced detection of STEMI equivalents, which were missed in the current cohort.
 
The rise of artificial intelligence
Although the diagnostic limitations of rule-based algorithms are recognised, Zhao et al9 described an artificial intelligence diagnostic algorithm that showed promising results (96.8% sensitivity and 99% specificity) using coronary angiography findings as the reference standard. The potential role of artificial intelligence in PHECG diagnosis merits further exploration to increase accuracy.
 
Paradigm shift in classifying myocardial infarction
Meyers et al30 proposed a new paradigm of occlusion myocardial infarction (OMI) vs non-OMI, which they compared with the conventional STEMI vs non-STEMI paradigm. Occlusion myocardial infarction refers to type 1 myocardial infarction that involves acute total or near-total occlusion of a major epicardial coronary vessel with insufficient collateral circulation, causing acute infarction. Meyers et al30 showed that 38% of OMI patients did not meet ECG-based STEMI criteria, as stated in the 4th Universal Definition of Myocardial Infarction.14 Compared with OMI patients who met STEMI criteria, patients not meeting the criteria experienced significant delays in cardiac catheterisation but exhibited similar adverse outcome profiles. These findings highlight the need to re-evaluate classification strategies for acute coronary syndrome, with a focus on rapidly recognising this underserved and poorly understood subgroup of patients who would benefit from emergent reperfusion therapy. Future research should emphasise identifying ECG features of OMI beyond the STEMI criteria.
 
Limitations
First, 13% of PHECGs were not matched to electronic patient records, resulting in the loss of data for interpretation. Second, during adjudicated blinded rating of the ECGs, STEMI equivalents were not included in the definition of STEMI because the algorithm was not designed to include these characteristics. This exclusion differs from real-world scenarios in which the recognition of STEMI equivalents would prompt ED physicians to implement STEMI management. Third, this study evaluated a single rule-based algorithm combined with a single ECG machine model utilised by a single urban EMS service provider serving a predominantly ethnic Chinese population. Fourth, intraobserver variability was not assessed for each ECG reviewer. Finally, ECGs considered ‘Not interpretable’ by ECG reviewers due to substantial artefacts were excluded from data analysis, which might lead to underestimation regarding the contributions of artefacts to false positivity.
 
Conclusion
In this territory-wide study, a rule-based PHECG algorithm demonstrated good sensitivity and fair specificity for the diagnosis of STEMI.
 
Author contributions
Concept or design: JHY Lai, CT Lui.
Acquisition of data: JHY Lai, TWT Chan, BCP Wong.
Analysis or interpretation of data: JHY Lai, CT Lui.
Drafting of the manuscript: JHY Lai.
Critical revision of the manuscript for important intellectual content: CT Lui, TWT Chan, BCP Wong, MSH Tsui, BKA Wan, KL Mok.
 
All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.
 
Conflicts of interest
All authors have disclosed no conflicts of interest.
 
Funding/support
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
 
Ethics approval
The research was approved by the New Territories West Cluster Research Ethics Committee of Hospital Authority, Hong Kong (Ref No.: NTWC/REC/21097). The requirement for patient consent was waived by the Committee because the study was conducted within a preexisting prehospital clinical service.
 
References
1. HealthyHK, Hong Kong SAR Government. Coronary heart diseases. 2021. Available from: https://www.healthyhk.gov.hk/phisweb/en/chart_detail/24/. Accessed 3 Dec 2022.
2. O’Gara PT, Kushner FG, Ascheim DD, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;61:485-510. Crossref
3. Ibánez B, James S, Agewall S, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation [in English, Spanish]. Rev Esp Cardiol (Engl Ed) 2017;70:1082. Crossref
4. Kontos MC, Gunderson MR, Zegre-Hemsey JK, et al. Prehospital activation of hospital resources (PreAct) ST-segment-elevation myocardial infarction (STEMI): a standardized approach to prehospital activation and direct to the catheterization laboratory for STEMI recommendations from the American Heart Association’s mission: lifeline program. J Am Heart Assoc 2020;9:e011963. Crossref
5. Brunetti ND, De Gennaro L, Correale M, et al. Prehospital electrocardiogram triage with telemedicine near halves time to treatment in STEMI: a meta-analysis and meta-regression analysis of non-randomized studies. Int J Cardiol 2017;232:5-11. Crossref
6. Cheung KS, Leung LP, Siu YC, et al. Prehospital 12-lead electrocardiogram for patients with chest pain: a pilot study. Hong Kong Med J 2018;24:484-91. Crossref
7. Bosson N, Sanko S, Stickney RE, et al. Causes of prehospital misinterpretations of ST elevation myocardial infarction. Prehosp Emerg Care 2017;21:283-90. Crossref
8. Fakhri Y, Andersson H, Gregg RE, et al. Diagnostic performance of a new ECG algorithm for reducing false positive cases in patients suspected acute coronary syndrome. J Electrocardiol 2021;69:60-4. Crossref
9. Zhao Y, Xiong J, Hou Y, et al. Early detection of ST-segment elevated myocardial infarction by artificial intelligence with 12-lead electrocardiogram. Int J Cardiol 2020;317:223-30. Crossref
10. Goebel M, Vaida F, Kahn C, Donofrio JJ. A novel algorithm for improving the diagnostic accuracy of prehospital STelevation myocardial infarction. Prehosp Disaster Med 2019;34:489-96. Crossref
11. Le May MR, Dionne R, Maloney J, et al. Diagnostic performance and potential clinical impact of advanced care paramedic interpretation of ST-segment elevation myocardial infarction in the field. CJEM 2006;8:401-7. Crossref
12. Ducas RA, Wassef AW, Jassal DS, et al. To transmit or not to transmit: how good are emergency medical personnel in detecting STEMI in patients with chest pain? Can J Cardiol 2012;28:432-7. Crossref
13. Tanguay A, Lebon J, Brassard E, Hébert D, Bégin F. Diagnostic accuracy of prehospital electrocardiograms interpreted remotely by emergency physicians in myocardial infarction patients. Am J Emerg Med 2019;37:1242-7. Crossref
14. Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction (2018). J Am Coll Cardiol 2018;72:2231-64. Crossref
15. Meyers HP, Limkakeng AT Jr, Jaffa EJ, et al. Validation of the modified Sgarbossa criteria for acute coronary occlusion in the setting of left bundle branch block: a retrospective case-control study. Am Heart J 2015;170:1255-64. Crossref
16. Smith SW, Dodd KW, Henry TD, Dvorak DM, Pearce LA. Diagnosis of ST-elevation myocardial infarction in the presence of left bundle branch block with the ST-elevation to S-wave ratio in a modified Sgarbossa rule. Ann Emerg Med 2012;60:766-76. Crossref
17. Wang K, Asinger RW, Marriott HJ. ST-segment elevation in conditions other than acute myocardial infarction. N Engl J Med 2003;349:2128-35. Crossref
18. Writing Committee; Kontos MC, de Lemos JA, et al. 2022 ACC Expert Consensus Decision Pathway on the evaluation and disposition of acute chest pain in the emergency department: a report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol 2022;80:1925-60. Crossref
19. Wilde AA, Antzelevitch C, Borggrefe M, et al. Proposed diagnostic criteria for the Brugada syndrome: consensus report. Circulation 2002;106:2514-9. Crossref
20. Patton KK, Ellinor PT, Ezekowitz M, et al. Electrocardiographic early repolarization: a scientific statement from the American Heart Association. Circulation 2016;133:1520-9. Crossref
21. Armstrong EJ, Kulkarni AR, Bhave PD, et al. Electrocardiographic criteria for ST-elevation myocardial infarction in patients with left ventricular hypertrophy. Am J Cardiol 2012;110:977-83. Crossref
22. Bischof JE, Worrall C, Thompson P, Marti D, Smith SW. ST depression in lead aVL differentiates inferior ST-elevation myocardial infarction from pericarditis. Am J Emerg Med 2016;34:149-54. Crossref
23. Mond HG, Haqqani HM. The electrocardiographic footprints of ventricular ectopy. Heart Lung Circ 2020;29:988-99. Crossref
24. Ting HH, Krumholz HM, Bradley EH, et al. Implementation and integration of prehospital ECGs into systems of care for acute coronary syndrome: a scientific statement from the American Heart Association Interdisciplinary Council on Quality of Care and Outcomes Research, Emergency Cardiovascular Care Committee, Council on Cardiovascular Nursing, and Council on Clinical Cardiology. Circulation 2008;118:1066-79. Crossref
25. Wilson RE, Kado HS, Percy RF, et al. An algorithm for identification of ST-elevation myocardial infarction patients by emergency medicine services. Am J Emerg Med 2013;31:1098-102. Crossref
26. Bhalla MC, Mencl F, Gist MA, Wilber S, Zalewski J. Prehospital electrocardiographic computer identification of ST-segment elevation myocardial infarction. Prehosp Emerg Care 2013;17:211-6. Crossref
27. Tanaka A, Matsuo K, Kikuchi M, et al. Systematic review and meta-analysis of diagnostic accuracy to identify ST-segment elevation myocardial infarction on interpretations of prehospital electrocardiograms. Circ Rep 2022;4:289-97. Crossref
28. Willems JL, Abreu-Lima C, Arnaud P, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med 1991;325:1767-73. Crossref
29. Macfarlane PW, Antzelevitch C, Haissaguerre M, et al. The early repolarization pattern: a consensus paper. J Am Coll Cardiol 2015;66:470-7. Crossref
30. Meyers HP, Bracey A, Lee D, et al. Comparison of the ST-elevation myocardial infarction (STEMI) vs. NSTEMI and occlusion MI (OMI) vs. NOMI paradigms of acute MI. J Emerg Med 2021;60:273-84. Crossref