Archives of Academic Emergency Medicine. 2022; 10(1): e5 OR I G I N A L RE S E A RC H Remote Analysis and Transmission System of Electrocar- diogram in Prehospital Setting; a Diagnostic Accuracy Study Elmira Almukhambetova1, Murat Almukhambetov1, Abdugani Musayev1∗, Ainur Yeshmanova1, Vildan Indershiyev1, Zhadira Kalhodzhaeva1 1. Department of Emergency and First Aid, Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan. Received: November 2021; Accepted: November 2021; Published online: 1 January 2022 Abstract: Introduction: One of the trends in the development of medical technologies is considered to be telemedicine. This study aimed to evaluate the accuracy of a remote electrocardiogram (ECG) analysis and transmission system in prehospital setting. Methods: In this cross-sectional study, the data of 19,265 ECGs was gathered from emergency medical service (EMS) database of Almaty city, Kazakhstan, from 2015 to 2019. All ECGs were recorded in the prehospital setting by a paramedic, using "Poly-Spectrum" ECG recording device. Subsequently, all ECGs were sent to the cardiologist for interpretation and the findings were compared between software and cardiologist. Results: 19,265 ECGs were registered. The average time from taking ECGs to receiving an expert’s conclusion was 9.2 ± 2.5 minutes. The medical teams were called in 17.9% of cases after paramedic ECG record- ing; however, in the rest of the cases there was no need to call those teams. Using the device reduced the number of visits of specialist teams. The overall sensitivity, specificity, and accuracy of ECG analysis device in diagno- sis of ECG abnormalities were 83.8% (95%CI: 82.6 – 84.9), 95.5% (95%CI: 95.1 – 95.8), and 93.3% (95%CI: 92.9 – 93.7), respectively. Conclusion: The findings of this study showed the 93.3% accuracy of automatic ECG analysis device in interpretation of ECG abnormalities in prehospital setting compared with the cardiologist interpreta- tions. Using the device causes a decrease in the number of cardiologist visits needed as well as reduction in cost and elapsed time. Keywords: Cardiovascular system; cardiovascular diseases; diagnosis; quality of health care; health services administration Cite this article as: Almukhambetova E, Almukhambetov M, Musayev A, Yeshmanova A, Indershiyev V, Kalhodzhaeva Z. Remote Analysis and Transmission System of Electrocardiogram in Prehospital Setting; a Diagnostic Accuracy Study. Arch Acad Emerg Med. 2022; 10(1): e5. https://doi.org/10.22037/aaem.v10i1.1399. 1. Introduction One of the trends in development of medical technologies is considered to be telemedicine, the main goal of which is to create conditions to make the consultation of highly quali- fied experts easily accessible to ordinary citizens (1, 2). Considering the high prevalence and burden of cardiovascu- lar diseases, the importance of a simple and accessible elec- trocardiography (ECG) analysis tools in prehospital settings is clear (3, 4). Thanks to the development of computer tech- nologies, communication networks and the internet have ∗Corresponding Author: Abdugani Musayev; Department of Emergency and First Aid, Asfendiyarov Kazakh National Medical University, Almaty, Kaza- khstan. Mail Index: 050038. Nurkent microdistrict, house number 41, flat number 38. Tel: +77772509406, E-mail: musaev.dr56@gmail.com, ORCID: http://orcid.org/0000-0001-7782-6255. made it possible to register an ECG anywhere and share it over long distances (5, 6). The first experiments of ECG transmission over a significant distance took place at the beginning of the 20th century (7). In 1905, W. Einthoven transmitted an ECG at a distance of about 1.5 kilometers (8). The method of remote analysis and transmission of ECG began to spread in the 1960s with the emergence of technical capabilities that made it possible to achieve the sufficient quality of ECG reception (9-12). In some cases, the description and interpretation of ECGs cause difficulties for paramedics (13, 14). Calling a special- ized medical team to assist the paramedics in deciphering "difficult-to-analyze" ECG is economically and temporally unjustifiable. Using the remote analysis and transmission systems of ECG at prehospital settings could be helpful in this regard. This study aimed to evaluate the accuracy of a remote analysis and transmission system of ECG in the prehospital This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem E. Almukhambetova et al. 2 setting. 2. Methods 2.1. Study design and setting In this diagnostic accuracy study, the data of 19,265 ECGs of adult patients (aged ≥18 years) was gathered from EMS database of Almaty city, Kazakhstan, from 2015 to 2019. All ECGs were recorded in the prehospital setting by a paramedic and using the "Poly-Spectrum" ECG recording device, and then remotely transmitted to the cardiologists. The findings of the automatically obtained analysis from the system were compared with the conclusions made by 19 ex- perienced physicians working in the cardiology center (from 6 to > 30 years). The doctor previewed the ECGs and fur- ther excluded unnecessary artifacts performed by automatic analysis to achieve high accuracy of the final results. Con- sequently, system and specialist reports were compared with each other. 2.2. About the system The system for remote analysis and transmission of ECG in- cluded the following parts: - 12- lead ECG registration, as well as transmitting devices that allow paramedic teams to share ECGs immediately after recording and monitor the patient’s condition during trans- portation to a medical institution. - receiving and transmitting devices suitable for recording ECGs placed in the cardiology remote consultation point, on the emergency medical service (EMS) station and the admis- sion department of the city cardiology center. These devices help specialists to consult people and reach a syndromic con- clusion in a couple of seconds online and by phone. Moreover, specialists analyzed the ECGs in complicated clin- ical cases such as emergency hospitalization and throm- bolytic therapy. They also provided advisory support and ac- curate recommendations to health professionals who trans- mitted the ECGs in order to monitor the patient at the pre- hospital setting. Indications for using the mentioned system were as follows: - The presence of clinical manifestations of acute coronary syndrome (unstable angina, heart attack) - Acutely formed life-threatening condition or hemodynamic disruption - Tachy/brady dysrhythmia in case of being unable to analyze ECGs on the scene 2.3. Data gathering In this diagnostic accuracy study, the data of 19,265 ECGs of adult patients (aged ≥18 years) was gathered from EMS database of Almaty city, Kazakhstan. All ECGs were recorded in prehospital setting by a paramedic and using the "Poly- Spectrum" ECG recording device and then remotely trans- mitted to the cardiologists. Consequently, they analyzed the ECGs to make the conclusion more exact. The results of the ECG analysis and recommendations were stored and regis- tered in the surveillance log. 2.4. Statistical analysis Sensitivity, specificity, and accuracy were evaluated in order to establish the diagnostic capabilities of the tests. Assess- ment and presentation of these indicators were carried out by calculating the 95% confidence interval using the statisti- cal analysis package SPSS 13.0 for Windows. 2.5. Ethical considerations The research corresponded to Declaration of Helsinki, devel- oped by the World Medical Association. Permission or ap- proval of the ethics committee was not required, because the publication describes a retrospective study, only a statistical analysis of available patient data was carried out. 3. Results 19,265 ECGs were registered and stored in the database dur- ing the overall period of application. The average time from taking ECGs to receiving an expert’s conclusion was 9.2 ± 2.5 minutes. Figure 1 shows a sample of transmitted ECG, diag- nosis, and recommendation of specialist. The medical teams were called in 17.9% of cases after paramedic ECG record- ing; however, in the rest of the cases calling those teams was not required. The introduction of devices for record- ing and transmitting ECGs had its economic effect by reduc- ing the number of visits of specialist teams. Owing to that, 52,542,432 tenge (140000 $) was saved from being lost in vain only by 2016. 3.1. Accuracy of ECG interpretation device According to the specialists’ interpretations, ECGs were ana- lyzed to be normal in 16,992 (88.2%) patients (53% male) and had at least one abnormality in 7,086 (36.8%) cases. Table one shows the frequency of ECG abnormalities and screen- ing performance characteristics of the device in diagnosis of each abnormality. The overall sensitivity, specificity, and ac- curacy of ECG analysis device in diagnosis of ECG abnormal- ities were 83.8% (95%CI: 82.6 – 84.9), 95.5% (95%CI: 95.1 – 95.8), and 93.3% (95%CI: 92.9 – 93.7), respectively. 4. Discussion Based on the findings of the present study, the sensitivity, specificity, and overall accuracy of the automatic ECG anal- ysis in the prehospital setting were 83.8%, 95.5%, and 93.3%, respectively. Taking into consideration a review of previous studies on var- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2022; 10(1): e5 Figure 1: Example of a 73 years old patient’s ECG recorded by a paramedic in 12 leads and sent to a specialist. Interpretation of ECG by "Poly- Spectrum" recording device, which was confirmed by a specialist: Sinus regular rhythm with a heart rate of 105 beats per minute; Tachycardia; Deviation of the electrical axis of the heart to the left; Probably a lower myocardial infarction, the stage of scarring; Possible anterior myocardial infarction, subacute stage; and Left ventricular hypertrophy (LVH). The specialist recommended comparing it with previous ECGs and contrasting them with the symptoms and calling the intensive care team. Table 1: Sensitivity and specificity of the electrocardiogram (ECG) analysis device in diagnosis of different ECG abnormalities compared with cardiologist’s interpretation Abnormality N (%) TP FP FN TN Sensitivity Specificity Accuracy Atrial fibrillation 1387 (7.2) 1311 607 76 17878 94,5 (93,2; 95,6) 96,7 (96,5; 97,0) 96,6 (96,3; 96,8) Atrial flutter 139 (0.7) 108 56 31 19126 77,7 (70,1; 83,8) 99,7 (99,6; 99,8) 99,6 (99,5; 99,6) LVH 4527 (23.5) 3678 1353 849 14738 81,3 (80,1; 82,4) 91,6 (91,2; 92,0) 89,3 (88,9; 89,7) AV block 559 (2.9) 473 195 86 18706 84,6 (81,4; 87,4) 98,9 (98,8; 99,1) 89,3 (88,9; 89,7) Extra-systole 1888 (9.8) 1659 935 229 17377 87,9 (86,3; 89,3) 94,9 (94,6; 95,2) 94,2 (93,9; 94,6) RBBB 1522 (7.9) 1397 848 125 17743 91,8 (90,3; 93,1) 95,4 (95,13; 95,73) 95,2 (94,9; 95,5) LBBB 2639 (13.7) 2411 1258 228 16626 91,4 (90,2; 92,4) 92,9 (92,6; 93,3) 92,8 (92,4; 93,1) Ischemia 1291 (6.7) 1168 489 123 17974 90,5 88,8; 92,0) 97,4 (97,1; 97,6) 96,9 (96,7; 97,1) Lower MI 167 (6.8) 148 84 19 19098 88,6 82,9; 92,6) 99,6 (99,5; 99,6) 99,5 (99,4; 99,6) Anterior-lateral MI 153 (0.9) 138 78 15 19112 90,2 (84,5; 94,0) 99,6 (99,5; 99,7) 99,5 (99,4; 99,6) Sinus rhythm 16992 (88.2) 17817 283 782 1448 95,8 (95,5; 96,1) 83,7 (81,8; 85,3) 94,8 (94,5; 95,1) Normal 12179 (63.2) 7439 2920 4740 11826 61,1 (60,2; 61,9) 80,2 (79,6; 80,8) 71,6 (71,0; 72,1) All measures are presented with 95% confidence interval. N: number; FP: false positive; FN: false negative; TP: true positive; TN: true negative; LVH: left ventricular hypertrophy; AV: atrioventricular; RBBB: right bundle brunch block; LBBB: left bundle brunch block; MI: myocardial infarction. ious automatic ECG analysis programs by Lyon, Aurore et al., the sensitivity ranged from 75.9% to almost 100%, depending on the specific conclusion and method of electrocardiogram analysis (15). Also, in the study of de Chazal P. et al., the sen- sitivity was 75.9% for determining supraventricular extrasys- toles, and 77.7% for ventricular extrasystoles (16). The diagnostic accuracy indicators for the blockage of the left leg of the His bundle, right bundle, extrasystole, atrial fibrilla- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem E. Almukhambetova et al. 4 tion, ventricular fibrillation, sinus node weakness syndrome, and normal ECG portrayed in a study by Niwas et al., were near 99% (17). Remote analysis and advisory support in making diagnostic and clinical decisions based on the interpretation of electro- cardiograms help in using the practical clinical experience of highly qualified consultants where it is needed the most. During transportation to a medical facility, the program also allows monitoring of the patient’s condition and ensures that the medical facility is ready to receive a patient with an ur- gent condition, inasmuch as emergency revascularization in acute myocardial infarction. To record and decipher the ECG in an ordinary situation, the patient will have to get to an out- patient clinic or hospital, where the ECG will be further regis- tered and analyzed. The great deal of effort, money, and time are required from the patient. As a result, the implementa- tion of emergency care may be delayed. Additionally, mate- rial costs for fuel and support of sanitary transport are also required to achieve the goal. With the introduction of a system for remote analysis of ECGs, all these problems are automatically solved, and it becomes possible to receive highly qualified diagnostic as- sistance in the conditions of the pre-hospital stage. Be- sides, the direct economic effect of the mass introduction of ECGs recording and transmission devices is obvious, as the number of visits of specialized intensive care teams de- crease. Generally, during usage of remote ECG analysis, vari- ous problems occurred in about 0.3% of cases due to obtain- ing an "atypical" electrocardiogram (artifacts, etc.), which can be the result of incorrect positioning of the electrodes, patient’s muscle tremors, hardware errors, the performer’s inexperience, and software failures. As a consequence, re- registration and transmission of ECGs are often required in these cases. This method is recommended to be implemented in practi- cal healthcare for early diagnosis and assistance, which can lead to an improvement in the health indicators of the popu- lation. 5. Limitations As with any cross-sectional study, there was a risk of selection bias. The research was held at the particular region – Almaty city (Republic of Kazakhstan). 6. Conclusion The findings of this study showed the 93.3% accuracy of au- tomatic ECG analysis device in interpretation of ECG abnor- malities in prehospital setting compared with the cardiolo- gist interpretations. Using the device causes a decrease in the number of specialized intensive care teams’ visits. 7. Declarations 7.1. Acknowledgments The authors of the article express their sincere gratitude to the management and staff of the Almaty Ambulance Service for their assistance in carrying out this work. 7.2. Author contribution The contribution of each author is in the analytical search for scientific publications, writing the article and approving the content. 7.3. Funding None. 7.4. Conflict of Interest No potential and actual conflicts of interest were present dur- ing our investigation. 7.5. Availability of data The data of medical records of patients used in the publica- tion are available only to healthcare workers of the Republic of Kazakhstan, who are working on the electronic resource of the complex medical information system called “Damumed” (https://alm.dmed.kz/Authentication/Authentication/SignIn ?ReturnUrl=%2F) References 1. Smulyan H. The Computerized ECG: Friend and Foe. The American Journal of Medicine. 2019;132(2):153-60. 2. Guo S-L, Han L-N, Liu H-W, Si Q-J, Kong D-F, Guo F-S. The future of remote ECG monitoring systems. Journal of geriatric cardiology: JGC. 2016;13(6):528-30. 3. Bansal A, Kumar S, Bajpai A, Tiwari V, Nayak M, Venkate- san S., et al. 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This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Limitations Conclusion Declarations References