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The influence of computerized interpretation of an electrocardiogram reading
American Journal of Emergency Medicine xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
American Journal of Emergency Medicine journal homepage: www.elsevier.com/locate/ajem
Correspondence
The influence of computerized interpretation of an electrocardiogram reading☆ To the Editor, The electrocardiogram (ECG) is still an essential diagnostic test when studying a patient with a cardiovascular disease [1]. Even though it is an accessible tool and used daily, its correct interpretation is still a challenge for many physicians. In the last decade, this fact has propelled the development of new ECG machines that include different automated interpretation programs, with the aim to improve the diagnostic efficiency of the ECG [2]. Although many of these programs have been validated and shown to have an adequate global diagnostic accuracy, frequent errors of interpretation in certain clinical situations and negative consequences resulting from erroneous lectures have been described in medical literature [3-5]. Nevertheless, no study up to now has analyzed how much the automated interpretation influences a physician's ECG reading. This study was done with participants who attended an annual course about ECGs given by health personnel of this hospital. At the beginning of the course, 2 models of a questionnaire were given out, depending on where the participants were seated, which included 12 ECGs representative of the most frequent cardiovascular diseases. The 2 models were distinguished by the presence or not of an automated interpretation given by the ECG machine. In the model that had the automated interpretation, the machine correctly diagnosed 6 of the ECGs, and 6 were incorrect according to 3 senior cardiologists. The participants had to give the most complete ECG interpretation possible of each of the 12 ECGs, with a maximum time of 15 minutes. The different interpretations of the participants of each of the 12 ECGs were evaluated before the beginning of the course by each cardiologist and were given a score of 2 points for the possible correct interpretations, 1 point for the possible correct syndrome diagnosis, and 0 point for an incorrect interpretation. For example, the first ECG was of a common atrial flutter. The following answers were given 2 points: atrial flutter, common atrial flutter, and atrial flutter with variable conduction. Meanwhile, the answers supraventricular tachycardia and narrow QRS tachycardia were given only 1 point. In the case of answers that were not thought of before the course, points were blindly given a posteriori by 2 cardiologists. In the case of conflict between the 2, a third independent cardiologist assigned the final points. A total of 123 participants were included in the study, and the majority of them were primary care and emergency physicians. Sixty-seven of them were included in a group without the automated interpretation, and 56 in a group with the automated reading. The characteristics of both groups that were analyzed are shown in Table 1. The results of the study showed that of the ECGs that were correctly diagnosed by the computer, the group that had the automated interpretation at their disposition obtained a higher average score (8.60 vs 7.80 points of 12; P b .001) and a higher percentage of diagnostic success, defined as an ECG interpretation score of 2 (62.8% vs 54.7%; P = .022), in comparison with the group that did not have the automated ☆ Conflict of interests: none.
interpretation. However, of the ECGs that were incorrectly diagnosed by the machine, the group that used the automated interpretation obtained a lower average score (3.33 vs 5.22 points of 12; P = .050) and a lower percentage of diagnostic success (21.1% vs 35.8%; P b .001) in comparison with the group that did not have the automated interpretation (Table 2). The multivariate analysis, adjusted by age and number of ECGs interpreted a week, showed that using a correct automated interpretation, in comparison of not using it, had an odds ratio of a better diagnostic success of 2.09 (95% confidence interval, 1.52-2.87; P b .001). Meanwhile, the use of an incorrect interpretation had an odds ratio of 0.33 (95% confidence interval, 0.23-0.47; P b .001). These results show a significant influence of an automated interpretation in a physician's ECG reading, doubling the diagnostic success rate when the interpretation is correct but at the cost of a 66% more probability of error when the diagnosis given is not correct. Given the grave consequences that an incorrect interpretation could cause in certain clinical situations, the results of this study emphasize 2 fundamental necessities in this area. One is the need for a continuous improvement of the new automated programs to improve their diagnostic efficiency. On the other hand, a continuous training of health personnel in the interpretation of ECGs is also needed to validate the automated interpretation and for an early detection of errors.
Pedro Martínez-Losas⁎ Javier Higueras Juan Carlos Gómez-Polo Instituto Cardiovascular, Hospital Clínico San Carlos, Madrid, Spain ⁎Corresponding author. Cardiology Department, Hospital Universitario Clínico San Carlos, C/ Profesor Martin Lagos s/n 28040, Madrid, Spain. Tel.: +34 913302712; fax: +34 913303220 E-mail address: [email protected] Philip Brabyn Servicio Cirugía Maxilofacial, Hospital Universitario La Princesa, Madrid, Spain
Juan Manuel Fuentes Ferrer Unidad de Gestión Clínica de Medicina Preventiva, Instituto de Investigación Sanitaria Hospital Clínico San Carlos, Madrid, Spain
Victoria Cañadas Godoy Julián Pérez Villacastín Instituto Cardiovascular, Hospital Clínico San Carlos, Madrid, Spain
http://dx.doi.org/10.1016/j.ajem.2016.07.029
0735-6757/© 2016 Elsevier Inc. All rights reserved.
Please cite this article as: Martínez-Losas P, et al, The influence of computerized interpretation of an electrocardiogram reading, Am J Emerg Med (2016), http://dx.doi.org/10.1016/j.ajem.2016.07.029
2
Correspondence
Table 1 Characteristics of the participants in the study
Field of work, n (%) Hospital Non-hospital Other Sex, n (%) Female Male ECG interpreted per week, n (%) N10 Between 5 and 10 b5 None Age (y), average ± standard deviation
Group without automated interpretation (N = 67)
Group with automated interpretation (N = 56)
38 (56.7) 26 (38.8) 3 (4.5)
35 (66) 17 (32.1) 1 (1.9)
54 (80.6) 13 (19.4)
44 (78.6) 12 (21.4)
22 (33.3) 36 (54.5) 7 (10.6) 1 (1.5) 35.88 ± 11.04
21 (38.2) 19 (34.5) 13 (23.6) 2 (3.6) 31.5 ± 9.35
P .489
.781
.088
.021
Table 2 Percentage of successful ECG diagnosis depending on groups
ECGs correctly diagnosed by the machine (%) 1-Common atrial flutter 2-Complete atrioventricular block 3-Pacemaker malfunction 4-Anterior myocardial infarction 5-Left ventricular hypertrophy 6-Inferior myocardial infarction ECGs incorrectly diagnosed by the machine (%) 1-Accessory pathway 2-Pacemaker 3-Brugada pattern 4-2:1 atrioventricular block 5-Pericarditis 6-Ventricular tachycardia
Group without automated interpretation (N = 67)
Group with automated interpretation (N = 56)
P
74.6 46.3 0 74.6 59.7 73.1
76.8 82.1 1.8 83.9 55.4 76.8
.464 b.001 .307 .001 .771 .083
52.2 3 47.8 3 32.8 76.1
32.1 1.8 19.6 0 16.1 57.1
.023 .003 .003 .135 .096 .029
References [1] Kligfield P, Gettes LS, Bailey JJ, Childers R, Deal BJ, Hancock EW, et al. Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation 2007;115:1306–24.
[2] Willems JL, Abreu-Lima C, Arnaud P, van Bemmel JH, Brohet C, Degani R, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med 1991;325:1767–73. [3] Hwan Bae M, Hoon Lee J, Heon Yang D, Sik Park H, Cho Y, Chull Chae S, et al. Erroneous computer electrocardiogram interpretation of atrial fibrillation and its clinical consequences. Clin Cardiol 2012;35:348–53. [4] Min MK, Ryu JH, Kim YI, Park MR, Park YM, Park SW, et al. Does cardiac catheterization laboratory activation by electrocardiography machine autointerpretation reduce door-to-balloon time? Am J Emerg Med 2014;32:1305–10. [5] Miller MD, Cb P, Ackerman MJ. Diagnostic accuracy of screening electrocardiograms in long QT syndrome I. Pediatrics 2001;108:8–12.
Please cite this article as: Martínez-Losas P, et al, The influence of computerized interpretation of an electrocardiogram reading, Am J Emerg Med (2016), http://dx.doi.org/10.1016/j.ajem.2016.07.029
Contents lists available at ScienceDirect
American Journal of Emergency Medicine journal homepage: www.elsevier.com/locate/ajem
Correspondence
The influence of computerized interpretation of an electrocardiogram reading☆ To the Editor, The electrocardiogram (ECG) is still an essential diagnostic test when studying a patient with a cardiovascular disease [1]. Even though it is an accessible tool and used daily, its correct interpretation is still a challenge for many physicians. In the last decade, this fact has propelled the development of new ECG machines that include different automated interpretation programs, with the aim to improve the diagnostic efficiency of the ECG [2]. Although many of these programs have been validated and shown to have an adequate global diagnostic accuracy, frequent errors of interpretation in certain clinical situations and negative consequences resulting from erroneous lectures have been described in medical literature [3-5]. Nevertheless, no study up to now has analyzed how much the automated interpretation influences a physician's ECG reading. This study was done with participants who attended an annual course about ECGs given by health personnel of this hospital. At the beginning of the course, 2 models of a questionnaire were given out, depending on where the participants were seated, which included 12 ECGs representative of the most frequent cardiovascular diseases. The 2 models were distinguished by the presence or not of an automated interpretation given by the ECG machine. In the model that had the automated interpretation, the machine correctly diagnosed 6 of the ECGs, and 6 were incorrect according to 3 senior cardiologists. The participants had to give the most complete ECG interpretation possible of each of the 12 ECGs, with a maximum time of 15 minutes. The different interpretations of the participants of each of the 12 ECGs were evaluated before the beginning of the course by each cardiologist and were given a score of 2 points for the possible correct interpretations, 1 point for the possible correct syndrome diagnosis, and 0 point for an incorrect interpretation. For example, the first ECG was of a common atrial flutter. The following answers were given 2 points: atrial flutter, common atrial flutter, and atrial flutter with variable conduction. Meanwhile, the answers supraventricular tachycardia and narrow QRS tachycardia were given only 1 point. In the case of answers that were not thought of before the course, points were blindly given a posteriori by 2 cardiologists. In the case of conflict between the 2, a third independent cardiologist assigned the final points. A total of 123 participants were included in the study, and the majority of them were primary care and emergency physicians. Sixty-seven of them were included in a group without the automated interpretation, and 56 in a group with the automated reading. The characteristics of both groups that were analyzed are shown in Table 1. The results of the study showed that of the ECGs that were correctly diagnosed by the computer, the group that had the automated interpretation at their disposition obtained a higher average score (8.60 vs 7.80 points of 12; P b .001) and a higher percentage of diagnostic success, defined as an ECG interpretation score of 2 (62.8% vs 54.7%; P = .022), in comparison with the group that did not have the automated ☆ Conflict of interests: none.
interpretation. However, of the ECGs that were incorrectly diagnosed by the machine, the group that used the automated interpretation obtained a lower average score (3.33 vs 5.22 points of 12; P = .050) and a lower percentage of diagnostic success (21.1% vs 35.8%; P b .001) in comparison with the group that did not have the automated interpretation (Table 2). The multivariate analysis, adjusted by age and number of ECGs interpreted a week, showed that using a correct automated interpretation, in comparison of not using it, had an odds ratio of a better diagnostic success of 2.09 (95% confidence interval, 1.52-2.87; P b .001). Meanwhile, the use of an incorrect interpretation had an odds ratio of 0.33 (95% confidence interval, 0.23-0.47; P b .001). These results show a significant influence of an automated interpretation in a physician's ECG reading, doubling the diagnostic success rate when the interpretation is correct but at the cost of a 66% more probability of error when the diagnosis given is not correct. Given the grave consequences that an incorrect interpretation could cause in certain clinical situations, the results of this study emphasize 2 fundamental necessities in this area. One is the need for a continuous improvement of the new automated programs to improve their diagnostic efficiency. On the other hand, a continuous training of health personnel in the interpretation of ECGs is also needed to validate the automated interpretation and for an early detection of errors.
Pedro Martínez-Losas⁎ Javier Higueras Juan Carlos Gómez-Polo Instituto Cardiovascular, Hospital Clínico San Carlos, Madrid, Spain ⁎Corresponding author. Cardiology Department, Hospital Universitario Clínico San Carlos, C/ Profesor Martin Lagos s/n 28040, Madrid, Spain. Tel.: +34 913302712; fax: +34 913303220 E-mail address: [email protected] Philip Brabyn Servicio Cirugía Maxilofacial, Hospital Universitario La Princesa, Madrid, Spain
Juan Manuel Fuentes Ferrer Unidad de Gestión Clínica de Medicina Preventiva, Instituto de Investigación Sanitaria Hospital Clínico San Carlos, Madrid, Spain
Victoria Cañadas Godoy Julián Pérez Villacastín Instituto Cardiovascular, Hospital Clínico San Carlos, Madrid, Spain
http://dx.doi.org/10.1016/j.ajem.2016.07.029
0735-6757/© 2016 Elsevier Inc. All rights reserved.
Please cite this article as: Martínez-Losas P, et al, The influence of computerized interpretation of an electrocardiogram reading, Am J Emerg Med (2016), http://dx.doi.org/10.1016/j.ajem.2016.07.029
2
Correspondence
Table 1 Characteristics of the participants in the study
Field of work, n (%) Hospital Non-hospital Other Sex, n (%) Female Male ECG interpreted per week, n (%) N10 Between 5 and 10 b5 None Age (y), average ± standard deviation
Group without automated interpretation (N = 67)
Group with automated interpretation (N = 56)
38 (56.7) 26 (38.8) 3 (4.5)
35 (66) 17 (32.1) 1 (1.9)
54 (80.6) 13 (19.4)
44 (78.6) 12 (21.4)
22 (33.3) 36 (54.5) 7 (10.6) 1 (1.5) 35.88 ± 11.04
21 (38.2) 19 (34.5) 13 (23.6) 2 (3.6) 31.5 ± 9.35
P .489
.781
.088
.021
Table 2 Percentage of successful ECG diagnosis depending on groups
ECGs correctly diagnosed by the machine (%) 1-Common atrial flutter 2-Complete atrioventricular block 3-Pacemaker malfunction 4-Anterior myocardial infarction 5-Left ventricular hypertrophy 6-Inferior myocardial infarction ECGs incorrectly diagnosed by the machine (%) 1-Accessory pathway 2-Pacemaker 3-Brugada pattern 4-2:1 atrioventricular block 5-Pericarditis 6-Ventricular tachycardia
Group without automated interpretation (N = 67)
Group with automated interpretation (N = 56)
P
74.6 46.3 0 74.6 59.7 73.1
76.8 82.1 1.8 83.9 55.4 76.8
.464 b.001 .307 .001 .771 .083
52.2 3 47.8 3 32.8 76.1
32.1 1.8 19.6 0 16.1 57.1
.023 .003 .003 .135 .096 .029
References [1] Kligfield P, Gettes LS, Bailey JJ, Childers R, Deal BJ, Hancock EW, et al. Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation 2007;115:1306–24.
[2] Willems JL, Abreu-Lima C, Arnaud P, van Bemmel JH, Brohet C, Degani R, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med 1991;325:1767–73. [3] Hwan Bae M, Hoon Lee J, Heon Yang D, Sik Park H, Cho Y, Chull Chae S, et al. Erroneous computer electrocardiogram interpretation of atrial fibrillation and its clinical consequences. Clin Cardiol 2012;35:348–53. [4] Min MK, Ryu JH, Kim YI, Park MR, Park YM, Park SW, et al. Does cardiac catheterization laboratory activation by electrocardiography machine autointerpretation reduce door-to-balloon time? Am J Emerg Med 2014;32:1305–10. [5] Miller MD, Cb P, Ackerman MJ. Diagnostic accuracy of screening electrocardiograms in long QT syndrome I. Pediatrics 2001;108:8–12.
Please cite this article as: Martínez-Losas P, et al, The influence of computerized interpretation of an electrocardiogram reading, Am J Emerg Med (2016), http://dx.doi.org/10.1016/j.ajem.2016.07.029