Computer-assisted electrocardiography in structured diagnosis of acute myocardial infarction

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.

An interoperable system for automated diagnosis of cardiac abnormalities from electrocardiogram data

Electrocardiogram (ECG) data are stored and analyzed in different formats, devices, and computer platforms. As a result, ECG data from different monitoring devices cannot be displayed unless the user has access to the proprietary software of each particular device. This research describes an ontology and encoding for representation of ECG data that allows open exchange and display of ECG data in a web browser. The ontology is based on the Health Level Seven (HL7) medical device communication standard. It integrates ECG waveform data, HL7 standard ECG data descriptions, and cardiac diagnosis rules, providing a capability to both represent ECG waveforms as well as perform automated diagnosis of 37 different cardiac abnormalities. The ECG ontology is encoded in XML, thus allowing ECG data from any digital ECG device that maps to it to be displayed in a general-purpose Internet browser. An experiment was conducted to test the interoperability of the system (ability to openly share ECG data without error in a web browser) and also to assess the accuracy of the diagnosis model. Results showed 100% interoperability using 276 ECG data files and 93% accuracy in diagnosis of abnormal cardiac conditions.

Common errors in computer electrocardiogram interpretation

OBJECTIVE

The aim of the study was to determine the frequency and nature of errors in computer electrocardiogram (ECG) reading.

METHODS

The ECGs were collected in the tertiary care VA Hospital from both inpatients and outpatients. They were read by the electrocardiograph built-in computer software, and then reread by two cardiologists. Statistical analysis was performed using sensitivity, specificity, positive and negative predicted value to analyze the data. An error index was formulated as indicator of diagnostic accuracy.

RESULTS

Out of 2072 ECGs, 776 (37.5%) were normal, and 1296 (62.5%) were abnormal. In 9.9% of all ECGs and in 15.9% of abnormal ECGs there were significant disagreements between the computer and cardiologists. The errors in diagnosis of arrhythmia, conduction disorders and electronic pacemakers accounted for 178 cases, or 86.4% of all errors. The rest was represented by misdetection of chamber enlargement (7 cases, 3.4%), misdiagnosis of ischemia and acute myocardial infarction (16 cases, 7.8%), and lead misplacement (5 cases, 2.4%).

CONCLUSIONS

The most frequent errors in computer ECG interpretation are related to arrhythmias, conduction disorders, and electronic pacemakers. Computer ECG diagnosis of life threatening conditions e.g. acute myocardial infarction or high degree AV blocks are frequently not accurate (40.7% and 75.0% errors, respectively). Improvement in the diagnostic algorithms should focus on these areas. Error index is a convenient and informative tool for evaluation of diagnostic accuracy.