1
|
Swenne CA, Ter Haar CC. Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients. J Electrocardiol 2024; 82:34-41. [PMID: 38006762 DOI: 10.1016/j.jelectrocard.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/14/2023] [Accepted: 10/23/2023] [Indexed: 11/27/2023]
Abstract
Non-traumatic chest pain is a frequent reason for an urgent ambulance visit of a patient by the emergency medical services (EMS). Chest pain (or chest pain-equivalent symptoms) can be innocent, but it can also signal an acute form of severe pathology that may require prompt intervention. One of these pathologies is cardiac ischemia, resulting from a disbalance between blood supply and demand. One cause of a diminished blood supply to the heart is acute coronary syndrome (ACS, i.e., cardiac ischemia caused by a reduced blood supply to myocardial tissue due to plaque instability and thrombus formation in a coronary artery). ACS is dangerous due to the unpredictable process that drives the supply problem and the high chance of fast hemodynamic deterioration (i.e., cardiogenic shock, ventricular fibrillation). This is why an ECG is made at first medical contact in most chest pain patients to include or exclude ischemia as the cause of their complaints. For speedy and adequate triaging and treatment, immediate assessment of this prehospital ECG is necessary, still during the ambulance ride. Human diagnostic efforts supported by automated interpretation algorithms seek to answer questions regarding the urgency level, the decision if and towards which healthcare facility the patient should be transported, and the indicated acute treatment and further diagnostics after arrival in the healthcare facility. In the case of an ACS, a catheter intervention room may be activated during the ambulance ride to facilitate the earliest possible in-hospital treatment. Prehospital ECG assessment and the subsequent triaging decisions are complex because chest pain is not uniquely associated with ACS. The differential diagnosis includes other cardiac, pulmonary, vascular, gastrointestinal, orthopedic, and psychological conditions. Some of these conditions may also involve ECG abnormalities. In practice, only a limited fraction (order of magnitude 10%) of the patients who are urgently transported to the hospital because of chest pain are ACS patients. Given the relatively low prevalence of ACS in this patient mix, the specificity of the diagnostic ECG algorithms should be relatively high to prevent overtreatment and overflow of intervention facilities. On the other hand, only a sufficiently high sensitivity warrants adequate therapy when needed. Here, we review how the prehospital ECG can contribute to identifying the presence of myocardial ischemia in chest pain patients. We discuss the various mechanisms of myocardial ischemia and infarction, the typical patient mix of chest pain patients, the shortcomings of the ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) ECG criteria to detect a completely occluded culprit artery, the OMI ECG criteria (including the STEMI-equivalent ECG patterns) in detecting completely occluded culprit arteries, and the promise of neural networks in recognizing ECG patterns that represent complete occlusions. We also discuss the relevance of detecting any ACS/ischemia, not necessarily caused by a total occlusion, in the prehospital ECG. In addition, we discuss how serial prehospital ECGs can contribute to ischemia diagnosis. Finally, we discuss the diagnostic contribution of a serial comparison of the prehospital ECG with a previously made nonischemic ECG of the patient.
Collapse
Affiliation(s)
- Cees A Swenne
- Cardiology Department, Leiden University Medical Center, Leiden, the Netherlands.
| | - C Cato Ter Haar
- Cardiology Department, Amsterdam University Medical Center, Amsterdam, the Netherlands
| |
Collapse
|
2
|
Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform 2023; 180:105274. [PMID: 37944275 DOI: 10.1016/j.ijmedinf.2023.105274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Abstract
Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies. METHODS The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms. FINDINGS AND DISCUSSION The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction. CONCLUSION Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.
Collapse
Affiliation(s)
| | - Sven Tomforde
- Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany
| |
Collapse
|
3
|
Wu YH, Li AH, Chen TC, Liu JK, Tsai KC, Ho MP. Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. J Electrocardiol 2023; 81:60-65. [PMID: 37572584 DOI: 10.1016/j.jelectrocard.2023.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 08/14/2023]
Abstract
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.
Collapse
Affiliation(s)
- Yuan-Hui Wu
- Department of Emergency Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan; School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan.
| | - Ai-Hsien Li
- Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Tsan-Chi Chen
- Department of Medical Research, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
| | - Jen-Kuei Liu
- Department of Emergency Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Kuang-Chau Tsai
- Department of Emergency Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
| | - Min-Po Ho
- Department of Emergency Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
| |
Collapse
|
4
|
Al-Zaiti S, Macleod R, Dam PV, Smith SW, Birnbaum Y. Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities. J Electrocardiol 2022; 74:65-72. [PMID: 36027675 DOI: 10.1016/j.jelectrocard.2022.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/13/2022]
Abstract
Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.
Collapse
Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Robert Macleod
- Department of Biomedical Engineering, University of Utah, Salt Lake, UT, USA
| | - Peter Van Dam
- Department of Cardiology, University Medical Center Utrecht, the Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare and University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
| |
Collapse
|
5
|
Kawano K, Yufu K, Shimomura T, Sato H, Ishii Y, Yonezu K, Saito S, Kondo H, Akioka H, Shinohara T, Teshima Y, Sakamoto T, Takahashi N. Prehospital 12-Lead Electrocardiography System in Oita Assisted Transport of "True" Acute Coronary Syndrome Patients to Optimal Institutes. Circ J 2022; 86:1481-1487. [PMID: 35944978 DOI: 10.1253/circj.cj-22-0178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
BACKGROUND Mobile cloud electrocardiography (C-ECG) can reduce the door-to-balloon time of acute coronary syndrome (ACS) patients, so we hypothesized it would also assist in transporting ACS-suspected patients to the optimal institutes.Methods and Results: Initially, 10 fire departments in Oita had 10 ambulances equipped with C-ECG. Ambulance crews recorded a 12-lead ECG from the patient at the first point of contact and transmitted them to 18 hospitals (13 institutions (PCII) with 24-h availability for percutaneous coronary intervention (PCI) and 5 regional core hospitals (RCH) without 24-h PCI) for analysis by a cardiologist. During 41 months, 476 ECGs suspected to be ACS were transmitted and analyzed. Of these, 24 ECGs transmitted to PCII were judged as not requiring PCI, and the patients were directly transported to a RCH (PCII-RCH); 35 ECGs sent to a RCH were judged as requiring PCI, and the patients were directly transported to a PCII (RCH-PCII). The prevalence of cardiovascular disease was significantly higher in the RCH-PCII group than in the PCII-RCH group (P<0.01). There was no significant difference in the door-to-balloon time between the RCH-PCII and the group in which the C-ECG was sent to a PCII and the patients were transported directly to PCII (PCII-PCII) (49±14 vs. 59±20 min, P=0.14). CONCLUSIONS Prehospital 12-lead ECG can assist in transporting ACS-suspect patients to the optimal treatment facility.
Collapse
Affiliation(s)
- Kyoko Kawano
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Kunio Yufu
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | | | - Hiroki Sato
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Yumi Ishii
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Keisuke Yonezu
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Shotaro Saito
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Hidekazu Kondo
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Hidefumi Akioka
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Tetsuji Shinohara
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Yasushi Teshima
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Teruo Sakamoto
- Advanced Trauma Emergency and Critical Care Center, Oita University Hospital
| | - Naohiko Takahashi
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| |
Collapse
|
6
|
Peace A, Al-Zaiti SS, Finlay D, McGilligan V, Bond R. Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation. J Electrocardiol 2022; 73:157-161. [PMID: 35853754 DOI: 10.1016/j.jelectrocard.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/29/2022] [Accepted: 07/01/2022] [Indexed: 11/26/2022]
Abstract
In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making.
Collapse
Affiliation(s)
- Aaron Peace
- Clinical Translational Research and Innovation Centre, Northern Ireland, UK
| | | | | | | | | |
Collapse
|