The Comparison of Physician to Computer Interpreted Electrocardiograms on ST-elevation Myocardial Infarction Door-to-balloon Times

Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction

INTRODUCTION

Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI). This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.

MATERIAL AND METHODS

In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Both the Philips DXL 16-Lead ECG. Algorithm and on-duty emergency physicians interpreted each standard 12‑lead ECG. The sensitivity and specificity of computer interpretation and physician overread ECGs for the definite diagnosis of STEMI were calculated and compared.

RESULTS

Among the 9340 patients included in the final analysis, 133 were definitively diagnosed with STEMI. When "computer-reported infarct or injury" was used as the indicator, the sensitivity was 87.2% (95% CI 80.3% to 92.4%) and the specificity was 86.2% (95% CI 85.5% to 86.9%). When "physician-overread STEMI" was used as the indicator, the sensitivity was 88.0% (95% CI 81.2% to 93.0%) and the specificity was 99.9% (95% CI 99.8% to 99.9%). The area under the receiver operating characteristic curve for physician-overread STEMI and computer-reported infarct or injury were 0.939 (95% CI 0.907 to 0.972) and 0.867 (95% CI 0.834 to 0.900), respectively.

CONCLUSIONS

This study reveals that while the sensitivity of the computer in recognizing ECG signs of STEMI is similar to that of physicians, physician overread of ECGs is more specific and, therefore, more accurate than CIE.

Does the Electrocardiogram Machine Interpretation Affect the Ability to Accurately Diagnose ST-Elevation Myocardial Infarction by Emergency Physicians?

INTRODUCTION

An ST-elevation myocardial infarction (STEMI) can portend significant morbidity and mortality to the patient and therefore must be rapidly diagnosed by an emergency medicine (EM) physician. The primary aim of this study is to determine whether EM physicians are more or less likely to accurately diagnose STEMI on an electrocardiogram (ECG) if they are blinded to the ECG machine interpretation as opposed to if they are provided the ECG machine interpretation.

METHODS

We performed a retrospective chart review of adult patients over 18 years of age admitted to our large, urban tertiary care center with a diagnosis of STEMI from January 1, 2016, to December 31, 2017. From these patients' charts, we selected 31 ECGs to create a quiz that was presented twice to a group of emergency physicians. The first quiz contained the 31 ECGs without the computer interpretations revealed. The second quiz, presented to the same physicians 2 weeks later, contained the same set of ECGs with the computer interpretations revealed. Physicians were asked "Based on the ECG above, is there a blocked coronary artery present causing a STEMI?"

RESULTS

Twenty-five EM physicians completed two 31-question ECG quizzes for a total of 1550 ECG interpretations. On the first quiz with computer interpretations blinded, the overall sensitivity in identifying a "true STEMI" was 67.2% with an overall accuracy of 65.6%. On the second quiz in which the ECG machine interpretation was revealed, the overall sensitivity was 66.4% with an accuracy of 65.8 % in correctly identifying a STEMI. The differences in sensitivity and accuracy were not statistically significant.

CONCLUSION

This study demonstrated no significant difference in physicians blinded versus those unblinded to computer interpretations of possible STEMI.

Analysis of the accuracy of automatic electrocardiogram interpretation in ST-segment elevation myocardial infarction

Objective

This study aimed to analyze the association between the culprit artery and the diagnostic accuracy of automatic electrocardiogram (ECG) interpretation in patients with ST-segment elevation myocardial infarction (STEMI).

Methods

This single-centered, retrospective cohort study included adult patients with STEMI who visited the emergency department between January 2017 and December 2020. The primary endpoint was the association between the culprit artery occlusion and the misinterpretation of ECG, evaluated by the chi-square test or Fisher exact test.

Results

The rate of misinterpretation of the automated ECG for patients with STEMI was 26.5% (31/117 patients). There was no significant correlation between the ST segment change in the four involved leads (anteroseptal, lateral, inferior, and aVR) and the misinterpretation of ECG (all P > 0.05). Single culprit artery occlusion significantly affected the misinterpretation of ECG compared with multiple culprit artery occlusion (single vs. multiple, 27/86 [31.3%] vs. 4/31 [12.9%], P = 0.045). There was no association between culprit artery and the misinterpretation of ECG (P = 0.132).

Conclusion

Single culprit artery occlusion might increase misinterpretation of ECG compared with multiple culprit artery occlusions in the automatic interpretation of STEMI.

Interventions targeted at reducing diagnostic error: systematic review

BACKGROUND

Incorrect, delayed and missed diagnoses can contribute to significant adverse health outcomes. Intervention options have proliferated in recent years necessitating an update to McDonald et al's 2013 systematic review of interventions to reduce diagnostic error.

OBJECTIVES

(1) To describe the types of published interventions for reducing diagnostic error that have been evaluated in terms of an objective patient outcome; (2) to assess the risk of bias in the included interventions and perform a sensitivity analysis of the findings; and (3) to determine the effectiveness of included interventions with respect to their intervention type.

METHODS

MEDLINE, CINAHL and the Cochrane Database of Systematic Reviews were searched from 1 January 2012 to 31 December 2019. Publications were included if they delivered patient-related outcomes relating to diagnostic accuracy, management outcomes and/or morbidity and mortality. The interventions in each included study were categorised and analysed using the six intervention types described by McDonald et al (technique, technology-based system interventions, educational interventions, personnel changes, structured process changes and additional review methods).

RESULTS

Twenty studies met the inclusion criteria. Eighteen of the 20 included studies (including three randomised controlled trials (RCTs)) demonstrated improvements in objective patient outcomes following the intervention. These three RCTs individually evaluated a technique-based intervention, a technology-based system intervention and a structured process change. The inclusion or exclusion of two higher risk of bias studies did not affect the results.

CONCLUSION

Technique-based interventions, technology-based system interventions and structured process changes have been the most studied interventions over the time period of this review and hence are seen to be effective in reducing diagnostic error. However, more high-quality RCTs are required, particularly evaluating educational interventions and personnel changes, to demonstrate the value of these interventions in diverse settings.

Sharing and Teaching Electrocardiograms to Minimize Infarction (STEMI): reducing diagnostic time for acute coronary occlusion in the emergency department

BACKGROUND

Limits to ST-Elevation Myocardial Infarction (STEMI) criteria may lead to prolonged diagnostic time for acute coronary occlusion. We aimed to reduce ECG-to-Activation (ETA) time through audit and feedback on STEMI-equivalents and subtle occlusions, without increasing Code STEMIs without culprit lesions.

METHODS

This multi-centre, quality improvement initiative reviewed all Code STEMI patients from the emergency department (ED) over a one-year baseline and one-year intervention period. We measured ETA time, from the first ED ECG to the time a Code STEMI was activated. Our intervention strategy involved a grand rounds presentation and an internal website presenting weekly local challenging cases, along with literature on STEMI-equivalents and subtle occlusions. Our outcome measure was ETA time for culprit lesions, our process measure was website views/visits, and our balancing measure was the percentage of Code STEMIs without culprit lesions.

RESULTS

There were 51 culprit lesions in the baseline period, and 64 in the intervention period. Median ETA declined from 28.0 min (95% confidence interval [CI] 15.0-45.0) to 8.0 min (95%CI 6.0-15.0). The website garnered 70.4 views/week and 27.7 visitors/week in a group of 80 physicians. There was no change in percentage of Code STEMIs without culprit lesions: 28.2% (95%CI 17.8-38.6) to 20.0% (95%CI 11.2-28.8%). Conclusions Our novel weekly web-based feedback to all emergency physicians was associated with a reduction in ETA time by 20 min, without increasing Code STEMIs without culprit lesions. Local ECG audit and feedback, guided by ETA as a quality metric for acute coronary occlusion, could be replicated in other settings to improve care.

Human-Computer Agreement of Electrocardiogram Interpretation for Patients Referred to and Declined for Primary Percutaneous Coronary Intervention: Retrospective Data Analysis Study

Background

When a patient is suspected of having an acute myocardial infarction, they are accepted or declined for primary percutaneous coronary intervention partly based on clinical assessment of their 12-lead electrocardiogram (ECG) and ST-elevation myocardial infarction criteria.

Objective

We retrospectively determined the agreement rate between human (specialists called activator nurses) and computer interpretations of ECGs of patients who were declined for primary percutaneous coronary intervention.

Methods

Various features of patients who were referred for primary percutaneous coronary intervention were analyzed. Both the human and computer ECG interpretations were simplified to either “suggesting” or “not suggesting” acute myocardial infarction to avoid analysis of complex heterogeneous and synonymous diagnostic terms. Analyses, to measure agreement, and logistic regression, to determine if these ECG interpretations (and other variables such as patient age, chest pain) could predict patient mortality, were carried out.

Results

Of a total of 1464 patients referred to and declined for primary percutaneous coronary intervention, 722 (49.3%) computer diagnoses suggested acute myocardial infarction, whereas 634 (43.3%) of the human interpretations suggested acute myocardial infarction (P<.001). The human and computer agreed that there was a possible acute myocardial infarction for 342 out of 1464 (23.3%) patients. However, there was a higher rate of human–computer agreement for patients not having acute myocardial infarctions (450/1464, 30.7%). The overall agreement rate was 54.1% (792/1464). Cohen κ showed poor agreement (κ=0.08, P=.001). Only the age (odds ratio [OR] 1.07, 95% CI 1.05-1.09) and chest pain (OR 0.59, 95% CI 0.39-0.89) independent variables were statistically significant (P=.008) in predicting mortality after 30 days and 1 year. The odds for mortality within 1 year of referral were lower in patients with chest pain compared to those patients without chest pain. A referral being out of hours was a trending variable (OR 1.41, 95% CI 0.95-2.11, P=.09) for predicting the odds of 1-year mortality.

Conclusions

Mortality in patients who were declined for primary percutaneous coronary intervention was higher than the reported mortality for ST-elevation myocardial infarction patients at 1 year. Agreement between computerized and human ECG interpretation is poor, perhaps leading to a high rate of inappropriate referrals. Work is needed to improve computer and human decision making when reading ECGs to ensure that patients are referred to the correct treatment facility for time-critical therapy.

A comprehensive artificial intelligence–enabled electrocardiogram interpretation program

Background

Automated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence–enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists.

Methods

We developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist’s final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients ≥18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n = 1,749,654), validation (n = 249,951), and testing (n = 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm’s performance to the cardiologist’s interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves.

Results

The model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of ≥0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity ≥95% for all codes.

Conclusions

An AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.

Comparing physicians and experienced advanced practice practitioners on the interpretation of electrocardiograms in the emergency department

BACKGROUND

Many patients present to emergency departments (ED) in U.S. for evaluation of acute coronary syndrome, and a rapid electrocardiogram (ECG) and interpretation are imperative for initial triage. A growing number of advanced practice practitioners (APP) (e.g. physician assistants, nurse practitioners) are assisting patient care in the ED.

PURPOSE

This study aims to compare the interpretation of ECGs by experienced APPs, each having 10 or more years of experience, with resident physicians and attending physicians.

PATIENTS AND METHODS

99 ED providers were stratified into attendings, residents at varying levels, and APPs were tested to interpret 36 ECGs from a database of ECGs initially interpreted to be ST elevation myocardial infarctions, of which 24 were determined to have a culprit lesion by coronary intervention.

RESULTS

Attending physicians were the most sensitive (0.86, 95% CI of 0.80 to 0.92) and specific (0.69, 95% Cl of 0.60 to 0.79) at interpreting ECGs, but APPs and physicians in their first year of practice out of residency were almost equally as sensitive [(0.82, 95% CI of 0.76 to 0.88) and (0.82, 95% CI of 0.76 to 0.88)] and specific [(0.62, 95% cl of 0.52 to 0.73) and (0.65, 95% Cl of 0.56 to 0.75)].

CONCLUSION

This study suggests the possibility of changing ED workflow where experienced APPs can be responsible for initial screening of an ECG, thus allowing fewer interruptions for ED physicians.

Interpretations of and management actions following electrocardiograms in symptomatic patients in primary care: a retrospective dossier study

BACKGROUND

The electrocardiogram (ECG) has become a popular tool in primary care. The clinical value of the ECG depends on the appropriateness of the indication and the interpretation skills of the general practitioner (GP).

OBJECTIVES

To describe the use of electrocardiography in primary care and to assess the performance of GPs in interpreting ECGs and making subsequent management decisions.

METHODS

Three hundred ECGs, recorded during daily practice in symptomatic patients by 14 GPs who regularly perform electrocardiography, were selected. Corresponding data of the indications, interpretations and subsequent management actions were extracted from the associated medical records. A panel consisting of an expert GP and a cardiologist reviewed the ECGs and specified their agreement with the findings and actions of the study GPs.

RESULTS

The most common indications were suspicion of a rhythm abnormality (43.7%), ischaemic heart disease (42.7%) and patient reassurance (14.3%). The study GPs interpreted 53.3% of the ECGs as showing no (new or acute) abnormality, whereas supraventricular rhythm disorders (12.3%), conduction disorders (7.7%) and repolarisation disorders (7.0%) were the most frequently reported pathological findings. Overall, the expert panel disagreed with the interpretations of the study GPs in 16.2% of cases, and with the GPs' management actions in 11.7%. Learning goals for GPs performing electrocardiography could be formulated for acute coronary syndrome, rhythm disorders, pulmonary embolism, reassurance, left ventricular hypertrophy and premature ventricular complexes.

CONCLUSION

GPs who feel competent in electrocardiography performed well in the opinion of the expert panel. We formulated various learning objectives for GPs performing electrocardiography.

Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation

BACKGROUND

Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and ST-T-U waves. We compared the accuracy of the first version of Cardiologs® DNN algorithm to the Mortara/Veritas® conventional algorithm in emergency department (ED) ECGs.

METHODS

Individual ECG diagnoses were prospectively mapped to one of 16 pre-specified groups of ECG diagnoses, which were further classified as "major" ECG abnormality or not. Automated interpretations were compared to blinded experts'. The primary outcome was the performance of the algorithms in finding at least one "major" abnormality. The secondary outcome was the proportion of all ECGs for which all groups were identified, with no false negative or false positive groups ("accurate ECG interpretation"). Additionally, we measured sensitivity and positive predictive value (PPV) for any abnormal group.

RESULTS

Cardiologs® vs. Veritas® accuracy for finding a major abnormality was 92.2% vs. 87.2% (p < 0.0001), with comparable sensitivity (88.7% vs. 92.0%, p = 0.086), improved specificity (94.0% vs. 84.7%, p < 0.0001) and improved positive predictive value (PPV 88.2% vs. 75.4%, p < 0.0001). Cardiologs® had accurate ECG interpretation for 72.0% (95% CI: 69.6-74.2) of ECGs vs. 59.8% (57.3-62.3) for Veritas® (P < 0.0001). Sensitivity for any abnormal group for Cardiologs® and Veritas®, respectively, was 69.6% (95CI 66.7-72.3) vs. 68.3% (95CI 65.3-71.1) (NS). Positive Predictive Value was 74.0% (71.1-76.7) for Cardiologs® vs. 56.5% (53.7-59.3) for Veritas® (P < 0.0001).

CONCLUSION

Cardiologs' DNN was more accurate and specific in identifying ECGs with at least one major abnormal group. It had a significantly higher rate of accurate ECG interpretation, with similar sensitivity and higher PPV.

e-Transmission of ECGs for expert consultation results in improved triage and treatment of patients with acute ischaemic chest pain by ambulance paramedics

AIMS

In pre-hospital settings handled by paramedics, identification of patients with myocardial infarction (MI) remains challenging when automated electrocardiogram (ECG) interpretation is inconclusive. We aimed to identify those patients and to get them on the right track to primary percutaneous coronary intervention (PCI).

METHODS AND RESULTS

In the Rotterdam-Rijnmond region, automated ECG devices on all ambulances were supplemented with a modem, enabling transmission of ECGs for online expert interpretation. The diagnostic protocol for acute chest pain was modified and monitored for 1 year. Patients with an ECG that met the criteria for ST-elevation myocardial infarction (STEMI) were immediately transported to a PCI hospital. ECGs that did not meet the STEMI criteria, but showed total ST deviation ≥800 µv were transmitted for online interpretation by the ECG expert. Online supervision was offered as a service if ECGs showed conduction disorders, or had an otherwise 'suspicious' pattern according to the ambulance paramedics. We enrolled 1,076 patients with acute ischaemic chest pain who did not meet the automated STEMI criteria. Their mean age was 63 years; 64% were men. After online consultation, 735 (68%) patients were directly transported to a PCI hospital for further treatment. PCI within 90 min was performed in 115 patients.

CONCLUSION

During a 1-year evaluation of the modified pre-hospital triage protocol for patients with acute ischaemic chest pain, over 100 acute MI patients with an initially inconclusive ECG received primary PCI within 90 min. Because of these results, we decided to continue the operation of the modified protocol.

Pre-hospital detection of acute ischemic stroke secondary to emergent large vessel occlusion: lessons learned from electrocardiogram and acute myocardial infarction

Currently, there is no device capable of detecting acute ischemic stroke (AIS) secondary to emergent large vessel occlusion (ELVO) in the pre-hospital setting. The inability to reliably identify patients that would benefit from primary treatment with endovascular thrombectomy remains an important limitation to optimizing emergency medical services (EMS) triage models and time-to-treatment. Several clinical grading scales that rely solely on clinical examination have been proposed and have demonstrated only moderate predictive ability for ELVO. Consequently, a technology capable of detecting ELVO in the pre-hospital setting would be of great benefit. An analogous scenario existed decades ago, in which pre-hospital detection of acute myocardial infarction (AMI) was unreliable until the emergence of the 12-lead ECG and its adoption by EMS providers. This review details the implementation of pre-hospital ECG (PHECG) for the detection of AMI and explores how early experience with PHECG may be applied to ELVO detection devices, once they become available.

New Insights Into the Use of the 12-Lead Electrocardiogram for Diagnosing Acute Myocardial Infarction in the Emergency Department

The 12-lead electrocardiogram (ECG) remains the most immediately accessible and widely used initial diagnostic tool for guiding management in patients with suspected myocardial infarction (MI). Although the development of high-sensitivity cardiac troponin assays has improved the rule-in and rule-out and risk stratification of acute MI without ST elevation, the immediate management of the subset of acute MI with acute coronary occlusion depends on integrating clinical presentation and ECG findings. Careful interpretation of the ECG might yield subtle features suggestive of ischemia that might facilitate more rapid triage of patients with subtle acute coronary occlusion or, conversely, in identification of ST-elevation MI mimics (pseudo ST-elevation MI patterns). Our goal in this review article is to consider recent advances in the use of the ECG to diagnose coronary occlusion MIs, including the application of rules that allow MI to be diagnosed on the basis of atypical ECG manifestations. Such rules include the modified Sgarbossa criteria allowing identification of acute MI in left bundle branch block or ventricular pacing, the 3- and 4-variable formula to differentiate normal ST elevation (formerly called early repolarization) from subtle ECG signs of left anterior descending coronary artery occlusion, the differentiation of ST elevation of left ventricular aneurysm from that of acute anterior MI, and the use of lead aVL in the recognition of inferior MI. Improved use of the ECG is essential to improving the diagnosis and appropriate early management of acute coronary occlusion MIs, which will lead to improved outcomes for patients who present with acute coronary syndrome.

A new 4-variable formula to differentiate normal variant ST segment elevation in V2-V4 (early repolarization) from subtle left anterior descending coronary occlusion - Adding QRS amplitude of V2 improves the model

INTRODUCTION

Precordial normal variant ST elevation (NV-STE), previously often called "early repolarization," may be difficult to differentiate from subtle ischemic STE due to left anterior descending (LAD) occlusion. We previously derived and validated a logistic regression formula that was far superior to STE alone for differentiating the two entities on the ECG. The tool uses R-wave amplitude in lead V4 (RAV4), ST elevation at 60 ms after the J-point in lead V3 (STE60V3) and the computerized Bazett-corrected QT interval (QTc-B). The 3-variable formula is: 1.196 x STE60V3 + 0.059 × QTc-B - 0.326 × RAV4 with a value ≥23.4 likely to be acute myocardial infarction (AMI).

HYPOTHESIS

Adding QRS voltage in V2 (QRSV2) would improve the accuracy of the formula.

METHODS

355 consecutive cases of proven LAD occlusion were reviewed, and those that were obvious ST elevation myocardial infarction were excluded. Exclusion was based on one straight or convex ST segment in V2-V6, 1 millimeter of summed inferior ST depression, any anterior ST depression, Q-waves, "terminal QRS distortion," or any ST elevation >5 mm. The NV-STE group comprised emergency department patients with chest pain who ruled out for AMI by serial troponins, had a cardiologist ECG read of "NV-STE," and had at least 1 mm of STE in V2 and V3. R-wave amplitude in lead V4 (RAV4), ST elevation at 60 ms after the J-point in lead V3 (STE60V3) and the computerized Bazett-corrected QT interval (QTc-B) had previously been measured in all ECGs; physicians blinded to outcome then measured QRSV2 in all ECGs. A 4-variable formula was derived to more accurately classify LAD occlusion vs. NV-STE and optimize area under the curve (AUC) and compared with the previous 3-variable formula.

RESULTS

There were 143 subtle LAD occlusions and 171 NV-STE. A low QRSV2 added diagnostic utility. The derived 4-variable formula is: 0.052*QTc-B - 0.151*QRSV2 - 0.268*RV4 + 1.062*STE60V3. The 3-variable formula had an AUC of 0.9538 vs. 0.9686 for the 4-variable formula (p = 0.0092). At the same specificity as the 3-variable formula [90.6%, at which cutpoint (≥23.4), 123 of 143 MI were correctly classified for 86% sensitivity], the sensitivity of the new formula at cutpoint ≥17.75 is 90.2%, with 129/143 correctly classified MI, identifying an additional 6 cases. The cutpoint with the highest accuracy (92.0%) was at a cutoff value ≥18.2, with 88.8% sensitivity, 94.7% specificity, and a positive and negative likelihood ratio of 16.9 (95% CI: 8.9-32) and 0.12 (95% CI: 0.07-0.19). At this cutpoint, it correctly classified an additional 11 cases (289 of 315, vs. 278 of 315): 127/143 for MI (an additional 4 cases) and 162/171 for NV-STE (an additional 7 cases).

CONCLUSION

On the ECG, a 4-variable formula was derived which adds QRSV2; it differentiates subtle LAD occlusion from NV-STE better than the 3-variable formula. At a value ≥18.2, the formula (0.052*QTc-B - 0.151*QRSV2 - 0.268*RV4 + 1.062*STE60V3) was very accurate, sensitive, and specific, with excellent positive and negative likelihood ratios. This formula needs to be validated.