Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy
<p>Graphic illustration of the cardiac-PET scan sequence (<b>a</b>): Placement of single-lead ECG patch (<b>b</b>): <sup>82</sup>Rubidium PET Computer Tomography myocardial perfusion imaging sequence.</p> "> Figure 2
<p>Examples of two patients’ ST-segment deviations during the adenosine cardiac stress test showing 12-lead II, V5 and V6 leads as well as the single-lead leads. (<b>A</b>) shows ST deviation > 0.1 mV in single lead and V5, whereas (<b>B</b>) does not show ST deviations > 0.1 mV in any recordings.</p> "> Figure 3
<p>(<b>A</b>): Bland–Altman plot of II-lead and single-lead ECG results. Comparison of lead II from a 12-lead ECG versus a single-lead ECG on the anterolateral left chest. Limit of agreement = LoA Upper dotted line = upper LoA, lower dotted line = lower LoA. Dotted line in the middle = mean difference. Dark area around dotted lines = 95% confidence intervals (95% CI). Mean difference = −0.019 mV (95% CI: −0.030 to −0.009). Upper LoA = 0.082 mV (95% CI 0.064 to 0.100) and lower LoA = −0.121 mV (95% CI: −0.139 to −0.103). (<b>B</b>): Bland–Altman plot of V5-lead and single-lead ECG results. Comparison of lead V5 from a 12-lead ECG versus a single-lead ECG on the anterolateral left chest. Dotted line in the middle = mean difference. Limit of agreement = LoA. Upper dotted line = upper LoA, lower dotted line = lower LoA. Dark area around dotted lines = 95% confidence intervals (95% CI). Mean difference −0.005 mV (95% CI −0.021 to 0.010). Upper LoA 0.145 mV (95% CI: 0.118 to 0.172) and lower LoA −0.155 mV (95% CI: −0.182 to −0.128). (<b>C</b>): Bland–Altman plot of V6-lead and single-lead ECG results. Comparison of lead V6 from a 12-lead ECG versus a single-lead ECG on the anterolateral left chest. Limit of agreement = LoA Upper dotted line = upper LoA, lower dotted line = lower LoA. Dotted line in the middle = mean difference. Dark area around dotted lines = 95% confidence intervals (95% CI). Mean diff −0.006 mV (95% CI −0.021, 0.010). Upper LoA 0.138 (95% CI: 0.112; 0.164) and lower LoA −0.149 (95% CI −0.175; −0.123).</p> "> Figure 3 Cont.
<p>(<b>A</b>): Bland–Altman plot of II-lead and single-lead ECG results. Comparison of lead II from a 12-lead ECG versus a single-lead ECG on the anterolateral left chest. Limit of agreement = LoA Upper dotted line = upper LoA, lower dotted line = lower LoA. Dotted line in the middle = mean difference. Dark area around dotted lines = 95% confidence intervals (95% CI). Mean difference = −0.019 mV (95% CI: −0.030 to −0.009). Upper LoA = 0.082 mV (95% CI 0.064 to 0.100) and lower LoA = −0.121 mV (95% CI: −0.139 to −0.103). (<b>B</b>): Bland–Altman plot of V5-lead and single-lead ECG results. Comparison of lead V5 from a 12-lead ECG versus a single-lead ECG on the anterolateral left chest. Dotted line in the middle = mean difference. Limit of agreement = LoA. Upper dotted line = upper LoA, lower dotted line = lower LoA. Dark area around dotted lines = 95% confidence intervals (95% CI). Mean difference −0.005 mV (95% CI −0.021 to 0.010). Upper LoA 0.145 mV (95% CI: 0.118 to 0.172) and lower LoA −0.155 mV (95% CI: −0.182 to −0.128). (<b>C</b>): Bland–Altman plot of V6-lead and single-lead ECG results. Comparison of lead V6 from a 12-lead ECG versus a single-lead ECG on the anterolateral left chest. Limit of agreement = LoA Upper dotted line = upper LoA, lower dotted line = lower LoA. Dotted line in the middle = mean difference. Dark area around dotted lines = 95% confidence intervals (95% CI). Mean diff −0.006 mV (95% CI −0.021, 0.010). Upper LoA 0.138 (95% CI: 0.112; 0.164) and lower LoA −0.149 (95% CI −0.175; −0.123).</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Study Design
2.2. Patients
2.3. Data Collection
2.4. Outcomes
2.5. Variables
2.6. ECG Data Analyses
3. Statistics
4. Results
4.1. Primary Outcome
4.2. Secondary Outcomes
4.3. Exploratory Outcome
Reversible Lateral/Anterior Ischemia | No Reversible Lateral/Anterior Ischemia | Total | |
---|---|---|---|
Single-lead ST-deviations ≥ 0.1 mV | 2 | 7 | 9 |
No single-lead ST-deviations ≥ 0.1 mV | 22 | 62 | 84 |
Totals | 24 | 69 | 93 |
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- International Surgical Outcomes Study Group TISOS. Global patient outcomes after elective surgery: Prospective cohort study in 27 low-, middle- and high-income countries. Br. J. Anaesth. 2016, 117, 601–609. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fields, A.C.; Divino, C.M. Surgical outcomes in patients with chronic obstructive pulmonary disease undergoing abdominal operations: An analysis of 331,425 patients. Surgery 2016, 159, 1210–1216. [Google Scholar] [CrossRef] [PubMed]
- Corrales-Medina, V.F.; Musher, D.M.; Shachkina, S.; Chirinos, J.A. Acute pneumonia and the cardiovascular system. Lancet 2013, 381, 496–505. [Google Scholar] [CrossRef] [PubMed]
- Bavishi, C.; Bonow, R.O.; Trivedi, V.; Abbott, J.D.; Messerli, F.H.; Bhatt, D.L. Special Article—Acute myocardial injury in patients hospitalized with COVID-19 infection: A review. Prog. Cardiovasc. Dis. 2020, 63, 682–689. [Google Scholar] [CrossRef]
- Devereaux, P.J.; Xavier, D.; Pogue, J.; Guyatt, G.H.; Sigamani, A.; Garutti, I.; Leslie, K.; Rao-Melacini, P.; Chrolavicius, S.; Yang, H.; et al. Characteristics and Short-Term Prognosis of Perioperative Myocardial Infarction in Patients Undergoing Noncardiac Surgery: A Cohort Study. Ann. Intern. Med. 2011, 154, 523. [Google Scholar] [CrossRef]
- Writing Committee for the VISION Study Investigators; Devereaux, P.J.; Biccard, B.M.; Sigamani, A.; Xavier, D.; Chan, M.T.V.; Srinathan, S.K.; Walsh, M.; Abraham, V.; Pearse, R.; et al. Association of Postoperative High-Sensitivity Troponin Levels With Myocardial Injury and 30-Day Mortality Among Patients Undergoing Noncardiac Surgery. JAMA 2017, 317, 1642–1651. [Google Scholar] [CrossRef]
- Botto, F.; Alonso-Coello, P.; Chan, M.T.V.; Villar, J.C.; Xavier, D.; Srinathan, S.K.; Guyatt, G.; Cruz, P.; Graham, M.; Wang, C.Y.; et al. Myocardial Injury after Noncardiac Surgery: A Large, International, Prospective Cohort Study Establishing Diagnostic Criteria, Characteristics, Predictors, and 30-day Outcomes. Anesthesiology 2014, 120, 564–578. [Google Scholar]
- Gerbershagen, H.J.; Aduckathil, S.; van Wijck, A.J.M.; Peelen, L.M.; Kalkman, C.J.; Meissner, W. Pain Intensity on the First Day after Surgery: A Prospective Cohort Study Comparing 179 Surgical Procedures. Anesthesiology 2013, 118, 934–944. [Google Scholar] [CrossRef] [Green Version]
- Kannel, W.B.; Abbott, R.D. Incidence and Prognosis of Unrecognized Myocardial Infarction. N. Engl. J. Med. 1984, 311, 1144–1147. [Google Scholar] [CrossRef]
- Qureshi, W.T.; Zhang, Z.-M.; Chang, P.P.; Rosamond, W.D.; Kitzman, D.W.; Wagenknecht, L.E.; Soliman, E. Silent Myocardial Infarction and Long-Term Risk of Heart Failure in the Atherosclerosis Risk in Communities (ARIC) Study. J. Am. Coll. Cardiol. 2018, 71, 1–8. [Google Scholar] [CrossRef]
- Yano, K.; MacLean, C.J. The incidence and prognosis of unrecognized myocardial infarction in the Honolulu, Hawaii, Heart Program. Arch. Intern. Med. 1989, 149, 1528. [Google Scholar] [CrossRef]
- Kehlet, H. Enhanced postoperative recovery: Good from afar, but far from good? Anaesthesia 2020, 75, e54–e61. [Google Scholar] [CrossRef] [Green Version]
- Montalescot, G.; Sechtem, U.; Achenbach, S.; Andreotti, F.; Arden, C.; Budaj, A.; Bugiardini, R.; Crea, F.; Cuisset, T.; Di Mario, C.; et al. 2013 ESC guidelines on the management of stable coronary artery disease: The Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur. Heart J. 2014, 35, 2260–2261. [Google Scholar] [CrossRef] [Green Version]
- Zhu, H.; Pan, Y.; Wu, F.; Huan, R. Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method. Sensors 2019, 19, 4458. [Google Scholar] [CrossRef] [Green Version]
- Lee, H.J.; Lee, D.S.; Kwon, H.B.; Kim, D.Y.; Park, K.S. Reconstruction of 12-lead ECG Using a Single-patch Device. Methods Inf. Med. 2017, 56, 319–327. [Google Scholar] [CrossRef]
- Ibanez, B.; Stephan, J.; Agewall, S.; Antunes, M.; Bucciarelli-Ducci, C.; Bueno, H.; Caforio, A.; Crea, F.; Goudevenos, J.; Halvorsen, S.; et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur. Heart J. 2018, 39, 119–177. [Google Scholar]
- Rajbhandary, P.L.; Nallathambi, G.; Selvaraj, N.; Tran, T.; Colliou, O. ECG Signal Quality Assessments of a Small Bipolar Single-Lead Wearable Patch Sensor. Cardiovasc. Eng. Technol. 2022, 13, 783–796. [Google Scholar] [CrossRef]
- Jennings, M.; Guldenring, D.; Bond, R.; Rababah, A.; McLaughlin, J.; Finlay, D. Computing in Cardiology. In ST Changes Observed in Short Spaced Bipolar Leads Suitable for Patch Based Monitoring; Biopolis: Singapore, 2019. [Google Scholar] [CrossRef]
- Gibson, C.M.; Mehta, S.; Ceschim, M.R.; Frauenfelder, A.; Vieira, D.; Botelho, R.; Fernandez, F.; Villagran, C.; Niklitschek, S.; Matheus, C.I.; et al. Evolution of single-lead ECG for STEMI detection using a deep learning approach. Int. J. Cardiol. 2022, 346, 47–52. [Google Scholar] [CrossRef]
- El-Hamad, F.; Baumert, M. Comparison of single-lead and multi-lead ECG for QT variability assessment using autoregressive modelling. Physiol. Meas. 2022, 43, 105002. [Google Scholar] [CrossRef]
- Sandau, K.E.; Funk, M.; Auerbach, A.; Barsness, G.W.; Blum, K.; Cvach, M.; Lampert, R.; May, J.L.; McDaniel, G.M.; Perez, M.V.; et al. Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. Circulation 2017, 136, e273–e344. [Google Scholar] [CrossRef]
- Mc Ardle, B.A.; Dowsley, T.F.; deKemp, R.A.; Wells, G.A.; Beanlands, R.S. Does Rubidium-82 PET Have Superior Accuracy to SPECT Perfusion Imaging for the Diagnosis of Obstructive Coronary Disease?: A systematic review and meta-analysis. J. Am. Coll. Cardiol. 2012, 60, 1828–1837. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nelson, A.J.; Ardissino, M.; Psaltis, P.J. Current approach to the diagnosis of atherosclerotic coronary artery disease: More questions than answers. Ther. Adv. Chronic Dis. 2019, 10, 2040622319884819. [Google Scholar] [CrossRef] [PubMed]
- Duncker, D.; Ding, W.Y.; Etheridge, S.; Noseworthy, P.A.; Veltmann, C.; Yao, X.; Bunch, T.; Gupta, D. Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation. Sensors 2021, 21, 2539. [Google Scholar] [CrossRef] [PubMed]
- Loft, F.C.; Rasmussen, S.M.; Elvekjaer, M.; Haahr-Raunkjaer, C.; Sørensen, H.B.D.; Aasvang, E.K.; Meyhoff, C.S.; WARD-Project Group. Continuously monitored vital signs for detection of myocardial injury in high-risk patients—An observational study. Acta Anaesthesiol. Scand. 2022, 66, 674–683. [Google Scholar] [CrossRef]
Patient Characteristics | Data Cohort n = 93 |
---|---|
Age | 63.1 (SD 10.7) |
Sex | |
Female | 31 |
Male | 62 |
Height | 173 (SD 9.3) |
Weight | 87 (SD 17.8) |
BMI | |
Average (max, min) | 29 (45; 13) |
>30 (%) | 58 (62) |
<30 (%) | 35 (38) |
Comorbilities | |
Diabetes (%) | 38 (41) |
Hypertension (%) | 22 (24) |
Ischemic cardiac disease (%) | 20 (22) |
Myocardial infarction (%) | 10 (11) |
Renal disease (%) | 15 (16) |
PCI/stent (%) | 4 (4.3) |
Heart failure (%) | 7 (7.5) |
Heart transplant (%) | 5 (5.4) |
Stroke (%) | 3 (3.2) |
Cardiomyopathy (%) | 2 (2.2) |
Pacemaker (%) | 3 (3.2) |
Peripheral vascular disease (%) | 1 (1.1) |
Cause of referral | |
Other clinical trials (%) | 31 (33.3) |
Solid organ transplant (%) | 13 (14.0) |
Clinical evaluation (%) | 49 (52.7) |
Reversible Lateral/Anterior Ischemia | No Reversible Lateral/Anterior Ischemia | Total | |
---|---|---|---|
ST-deviations ≥ 0.1 mV on V5, V6 and/or II | 3 | 6 | 9 |
No ST-deviations ≥ 0.1 mV on V5, V6 and/or II | 21 | 63 | 84 |
Totals | 24 | 69 | 93 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Fabricius Ekenberg, L.; Høfsten, D.E.; Rasmussen, S.M.; Mølgaard, J.; Hasbak, P.; Sørensen, H.B.D.; Meyhoff, C.S.; Aasvang, E.K. Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy. Sensors 2023, 23, 2962. https://doi.org/10.3390/s23062962
Fabricius Ekenberg L, Høfsten DE, Rasmussen SM, Mølgaard J, Hasbak P, Sørensen HBD, Meyhoff CS, Aasvang EK. Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy. Sensors. 2023; 23(6):2962. https://doi.org/10.3390/s23062962
Chicago/Turabian StyleFabricius Ekenberg, Luna, Dan Eik Høfsten, Søren M. Rasmussen, Jesper Mølgaard, Philip Hasbak, Helge B. D. Sørensen, Christian S. Meyhoff, and Eske K. Aasvang. 2023. "Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy" Sensors 23, no. 6: 2962. https://doi.org/10.3390/s23062962