- Email: [email protected]
Engineering experience and clinical electrocardiography
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ScienceDirect Journal of Electrocardiology 48 (2015) 900 – 901 www.jecgonline.com
Engineering experience and clinical electrocardiography
Over past decades, numerous advances in clinical medicine have been made possible by the progressive application of engineering methods. In cardiology, obvious examples include not only the vastly improved imaging technologies but also artificial valves, implantable devices, technological support of ablation techniques, and many others. Electrocardiography also substantially benefited from the improved signal acquisition electronics as well as from the development of algorithms for the automatic measurement and interpretation of ECG recordings, irrespective of the fact that automatic processing of ECGs is still being advanced and is still not entirely perfect. Indeed, clinical value emerges of novel electrocardiographic indices that would be impossible to obtain based purely on visual assessment and manual calliper measurement of standard recordings and that fully depend on advanced computerized signal processing. Novel risk factors [1,2], advanced physiologic observations [3,4], and improved drug safety assessment methodologies [5] have been obtained in this way. Moreover, possibilities emerged more recently of storing vast quantities of digital ECG data at previously unthinkably low costs and of copying them without any loss of quality. This substantially benefitted the collaboration between different research teams allowing, amongst others, clinical groups to share their data with biomedical engineers. This is expected to facilitate the development of novel signal processing and characterisation techniques which in turn offer innovative clinical use of ECGs. Unfortunately, the collaboration between clinical and engineering research groups is frequently unidirectional and not too effective. The Internet makes it possible for valuable collections of recordings obtained in clinical and physiologic studies to be accessible by engineering groups. The resources of the Physionet [6,7] and Thew [8,9] databases and other similar data sources are very helpful. However, whilst engineering groups could surely contribute to solving unmet clinical needs, specific clinical problems require, amongst others, specific data collections that frequently require focused clinical efforts and are therefore not readily available on general data sharing platforms. As a http://dx.doi.org/10.1016/j.jelectrocard.2015.07.011 0022-0736/© 2015 Elsevier Inc. All rights reserved.
consequence, engineering knowledge and expertise are frequently underutilised. The study by Mincholé et al [10] presented in this issue of the Journal is perhaps an example of underutilisation of engineering expertise that would have been used much more effectively if the engineering group of authors had access to problem-specific clinical data. The authors compared a particular ECG-based characteristic in a relatively small number of patients who developed torsade de pointes tachycardia on intravenous sotalol with a group of healthy subjects who received corresponding oral doses of the drug. The separation of the groups is of interest but the presented data analysis makes it impossible to decide whether the differences reflect the increased susceptibility to the torsade de pointes tachycardia or whether they are completely unrelated to sotalol and/or proarrhythmia risk and only expose the drug-independent difference between cardiac patients who were prescribed sotalol for some clinical reasons and healthy subjects who received the drug in an experimental study, i.e. without any clinical needs of their own. Mincholé at al clearly understand and indeed acknowledge in their text that the title of their article is somewhat stretched (probably with the expectation that the findings could be extrapolated to larger patient populations in further studies), and that it would have been much more appropriate to compare their ECG index between larger groups of patients who all received sotalol for similar clinical reasons and who subsequently either did or did not develop torsade de pointes tachycardia as a reaction to the drug. Besides, as already discussed some years ago [11], we should not restrict our understanding of sotalol induced ECG changes to the same set of data analysed by different groups over and over again [12–14]. From the point of view of clinical utility, it is easy to be critical of Mincholé at al since without proper comparison of sotalol-treated patient groups with and without proarrhythmic episodes, the study is of no clinical value. However, clinical readers of the Journal should realise that such a criticism is not entirely fair. Mincholé at al are engineers who relied on external data sources without any possibility of
Editorial / Journal of Electrocardiology 48 (2015) 900–901
starting their own investigations by focused clinical data collections. Rather than being critical (of this and many other similar engineering studies), clinical researchers should develop structures for sharing their data collection resources with engineering colleagues. Naturally, collaborative efforts are needed from both sides. Engineering groups should be equally encouraged to seek clinical collaboration before attempting to suggest clinically relevant techniques or measurements based on inappropriate data. For the purposes of clinically focused studies, widespread collections of clinical data accompanied by restricted if not insufficient clinical details are not necessarily the most useful. Repeatedly, engineering groups implement advanced data processing concepts only to search subsequently within unsorted collections of very different medical data to what clinical problems might the new concepts be possibly applied. Very rarely do such blinded searches lead to a proof of concept of technologies subsequently valued by the clinical community. Much closer and tighter collaborations between technologists and clinicians are needed. Not only do the engineers profit from the access to problem-driven data collection but the design of the clinical studies collecting the data also benefits from prior technological considerations. Only when clinicians and engineers work truly closely together are ECG processing methodologies with a true potential of impacting clinical practice created. At present, not many large universities with both engineering and medical faculties actively promote interdisciplinary projects. It can only be hoped that scientific programme committees of multi-disciplinary meetings will recognise this need by facilitating not only medical education amongst engineers and engineering education amongst clinicians but also the creation of interdisciplinary teams unified by a mutual clinically defined goal.
Marek Malik, PhD, MD St Paul's Cardiac Electrophysiology, University of London and Imperial College, London National Heart and Lung Institute, Imperial College Dovehouse Street, London SW3 6LY E-mail address: [email protected]
901
References [1] Berger RD, Kasper EK, Baughman KL, Marban E, Calkins H, Tomaselli GF. Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. Circulation 1997;96:1557–65. [2] Verrier RL, Klingenheben T, Malik M, El-Sherif N, Exner DV, Hohnloser SH, et al. Microvolt T-wave alternans: physiological basis, methods of measurement, and clinical utility consensus guideline by International Society for Holter and Noninvasive Electrocardiology. J Am Coll Cardiol 2011;58:1309–24. [3] Smetana P, Batchvarov VN, Hnatkova K, Camm AJ, Malik M. Sex differences in repolarization homogeneity and its circadian pattern. Am J Physiol Heart Circ Physiol 2002;282:H1889–97. [4] Malik M, Hnatkova K, Novotny T, Schmidt G. Subject-specific profiles of QT/RR hysteresis. Am J Physiol Heart Circ Physiol 2008;295:H2356–63. [5] Garnett CE, Zhu H, Malik M, Fossa AA, Zhang J, Badilini F, et al. Methodologies to characterize the QT/corrected QT interval in the presence of drug-induced heart rate changes or other autonomic effects. Am Heart J 2012;163:912–30. [6] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:E215–20. [7] Costa M, Moody GB, Henry I, Goldberger AL. PhysioNet: an NIH research resource for complex signals. J Electrocardiol 2003;36:139–44 [Suppl.]. [8] Couderc JP. A unique digital electrocardiographic repository for the development of quantitative electrocardiography and cardiac safety: the Telemetric and Holter ECG Warehouse (THEW). J Electrocardiol 2010;43:595–600. [9] Couderc JP. The Telemetric and Holter ECG Warehouse (THEW): the first three years of development and research. J Electrocardiol 2012;45:677–83. [10] Mincholé A, Bueno-Orovio A, Laguna P, Pueyo E, Rodriguez B. ECGbased estimation of dispersion of APD restitution as a tool to stratify sotalol-induced arrhythmic risk. J Electrocardiol 2015, http://dx.doi.org/ 10.1016/j.jelectrocard.2015.06.006 -this issue. [11] Malik M. Drug induced changes in the T-wave morphology. Drug Saf 2009;32:613–7. [12] Couderc JP, Zareba W, Moss AJ, Sarapa N, Morganroth J, Darpo B. Identification of sotalol-induced changes in repolarization with T wave area-based repolarization duration parameters. J Electrocardiol 2003;36:115–20. [13] Sarapa N, Morganroth J, Couderc JP, Francom SF, Darpo B, Fleishaker JC, et al. Electrocardiographic identification of drug-induced QT prolongation: assessment by different recording and measurement methods. Ann Noninvasive Electrocardiol 2004;9:48–57. [14] Graff C, Andersen MP, Xue JQ, Hardahl TB, Kanters JK, Toft E, et al. Identifying drug-induced repolarization abnormalities from distinct ECG patterns in congenital long QT syndrome: a study of sotalol effects on T-wave morphology. Drug Saf 2009;32:599–611.
ScienceDirect Journal of Electrocardiology 48 (2015) 900 – 901 www.jecgonline.com
Engineering experience and clinical electrocardiography
Over past decades, numerous advances in clinical medicine have been made possible by the progressive application of engineering methods. In cardiology, obvious examples include not only the vastly improved imaging technologies but also artificial valves, implantable devices, technological support of ablation techniques, and many others. Electrocardiography also substantially benefited from the improved signal acquisition electronics as well as from the development of algorithms for the automatic measurement and interpretation of ECG recordings, irrespective of the fact that automatic processing of ECGs is still being advanced and is still not entirely perfect. Indeed, clinical value emerges of novel electrocardiographic indices that would be impossible to obtain based purely on visual assessment and manual calliper measurement of standard recordings and that fully depend on advanced computerized signal processing. Novel risk factors [1,2], advanced physiologic observations [3,4], and improved drug safety assessment methodologies [5] have been obtained in this way. Moreover, possibilities emerged more recently of storing vast quantities of digital ECG data at previously unthinkably low costs and of copying them without any loss of quality. This substantially benefitted the collaboration between different research teams allowing, amongst others, clinical groups to share their data with biomedical engineers. This is expected to facilitate the development of novel signal processing and characterisation techniques which in turn offer innovative clinical use of ECGs. Unfortunately, the collaboration between clinical and engineering research groups is frequently unidirectional and not too effective. The Internet makes it possible for valuable collections of recordings obtained in clinical and physiologic studies to be accessible by engineering groups. The resources of the Physionet [6,7] and Thew [8,9] databases and other similar data sources are very helpful. However, whilst engineering groups could surely contribute to solving unmet clinical needs, specific clinical problems require, amongst others, specific data collections that frequently require focused clinical efforts and are therefore not readily available on general data sharing platforms. As a http://dx.doi.org/10.1016/j.jelectrocard.2015.07.011 0022-0736/© 2015 Elsevier Inc. All rights reserved.
consequence, engineering knowledge and expertise are frequently underutilised. The study by Mincholé et al [10] presented in this issue of the Journal is perhaps an example of underutilisation of engineering expertise that would have been used much more effectively if the engineering group of authors had access to problem-specific clinical data. The authors compared a particular ECG-based characteristic in a relatively small number of patients who developed torsade de pointes tachycardia on intravenous sotalol with a group of healthy subjects who received corresponding oral doses of the drug. The separation of the groups is of interest but the presented data analysis makes it impossible to decide whether the differences reflect the increased susceptibility to the torsade de pointes tachycardia or whether they are completely unrelated to sotalol and/or proarrhythmia risk and only expose the drug-independent difference between cardiac patients who were prescribed sotalol for some clinical reasons and healthy subjects who received the drug in an experimental study, i.e. without any clinical needs of their own. Mincholé at al clearly understand and indeed acknowledge in their text that the title of their article is somewhat stretched (probably with the expectation that the findings could be extrapolated to larger patient populations in further studies), and that it would have been much more appropriate to compare their ECG index between larger groups of patients who all received sotalol for similar clinical reasons and who subsequently either did or did not develop torsade de pointes tachycardia as a reaction to the drug. Besides, as already discussed some years ago [11], we should not restrict our understanding of sotalol induced ECG changes to the same set of data analysed by different groups over and over again [12–14]. From the point of view of clinical utility, it is easy to be critical of Mincholé at al since without proper comparison of sotalol-treated patient groups with and without proarrhythmic episodes, the study is of no clinical value. However, clinical readers of the Journal should realise that such a criticism is not entirely fair. Mincholé at al are engineers who relied on external data sources without any possibility of
Editorial / Journal of Electrocardiology 48 (2015) 900–901
starting their own investigations by focused clinical data collections. Rather than being critical (of this and many other similar engineering studies), clinical researchers should develop structures for sharing their data collection resources with engineering colleagues. Naturally, collaborative efforts are needed from both sides. Engineering groups should be equally encouraged to seek clinical collaboration before attempting to suggest clinically relevant techniques or measurements based on inappropriate data. For the purposes of clinically focused studies, widespread collections of clinical data accompanied by restricted if not insufficient clinical details are not necessarily the most useful. Repeatedly, engineering groups implement advanced data processing concepts only to search subsequently within unsorted collections of very different medical data to what clinical problems might the new concepts be possibly applied. Very rarely do such blinded searches lead to a proof of concept of technologies subsequently valued by the clinical community. Much closer and tighter collaborations between technologists and clinicians are needed. Not only do the engineers profit from the access to problem-driven data collection but the design of the clinical studies collecting the data also benefits from prior technological considerations. Only when clinicians and engineers work truly closely together are ECG processing methodologies with a true potential of impacting clinical practice created. At present, not many large universities with both engineering and medical faculties actively promote interdisciplinary projects. It can only be hoped that scientific programme committees of multi-disciplinary meetings will recognise this need by facilitating not only medical education amongst engineers and engineering education amongst clinicians but also the creation of interdisciplinary teams unified by a mutual clinically defined goal.
Marek Malik, PhD, MD St Paul's Cardiac Electrophysiology, University of London and Imperial College, London National Heart and Lung Institute, Imperial College Dovehouse Street, London SW3 6LY E-mail address: [email protected]
901
References [1] Berger RD, Kasper EK, Baughman KL, Marban E, Calkins H, Tomaselli GF. Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. Circulation 1997;96:1557–65. [2] Verrier RL, Klingenheben T, Malik M, El-Sherif N, Exner DV, Hohnloser SH, et al. Microvolt T-wave alternans: physiological basis, methods of measurement, and clinical utility consensus guideline by International Society for Holter and Noninvasive Electrocardiology. J Am Coll Cardiol 2011;58:1309–24. [3] Smetana P, Batchvarov VN, Hnatkova K, Camm AJ, Malik M. Sex differences in repolarization homogeneity and its circadian pattern. Am J Physiol Heart Circ Physiol 2002;282:H1889–97. [4] Malik M, Hnatkova K, Novotny T, Schmidt G. Subject-specific profiles of QT/RR hysteresis. Am J Physiol Heart Circ Physiol 2008;295:H2356–63. [5] Garnett CE, Zhu H, Malik M, Fossa AA, Zhang J, Badilini F, et al. Methodologies to characterize the QT/corrected QT interval in the presence of drug-induced heart rate changes or other autonomic effects. Am Heart J 2012;163:912–30. [6] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:E215–20. [7] Costa M, Moody GB, Henry I, Goldberger AL. PhysioNet: an NIH research resource for complex signals. J Electrocardiol 2003;36:139–44 [Suppl.]. [8] Couderc JP. A unique digital electrocardiographic repository for the development of quantitative electrocardiography and cardiac safety: the Telemetric and Holter ECG Warehouse (THEW). J Electrocardiol 2010;43:595–600. [9] Couderc JP. The Telemetric and Holter ECG Warehouse (THEW): the first three years of development and research. J Electrocardiol 2012;45:677–83. [10] Mincholé A, Bueno-Orovio A, Laguna P, Pueyo E, Rodriguez B. ECGbased estimation of dispersion of APD restitution as a tool to stratify sotalol-induced arrhythmic risk. J Electrocardiol 2015, http://dx.doi.org/ 10.1016/j.jelectrocard.2015.06.006 -this issue. [11] Malik M. Drug induced changes in the T-wave morphology. Drug Saf 2009;32:613–7. [12] Couderc JP, Zareba W, Moss AJ, Sarapa N, Morganroth J, Darpo B. Identification of sotalol-induced changes in repolarization with T wave area-based repolarization duration parameters. J Electrocardiol 2003;36:115–20. [13] Sarapa N, Morganroth J, Couderc JP, Francom SF, Darpo B, Fleishaker JC, et al. Electrocardiographic identification of drug-induced QT prolongation: assessment by different recording and measurement methods. Ann Noninvasive Electrocardiol 2004;9:48–57. [14] Graff C, Andersen MP, Xue JQ, Hardahl TB, Kanters JK, Toft E, et al. Identifying drug-induced repolarization abnormalities from distinct ECG patterns in congenital long QT syndrome: a study of sotalol effects on T-wave morphology. Drug Saf 2009;32:599–611.