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.

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.

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

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).

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).

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.

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).

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.

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).

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.

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.

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.

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.

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.

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.

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.

In this territory-wide study, a rule-based PHECG algorithm demonstrated good sensitivity and fair specificity for the diagnosis of STEMI.

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.

All authors have disclosed no conflicts of interest.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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.

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