Approaches to classification of microembolic signals in patients recovering from ischemic stroke
- Authors: Orlova E.V.1, Berdalin A.B.1, Lelyuk V.G.1
-
Affiliations:
- Federal Сenter of Brain Research and Neurotechnologies
- Issue: Vol 17, No 3 (2023)
- Pages: 74-82
- Section: Technologies
- URL: https://journals.rcsi.science/2075-5473/article/view/253962
- DOI: https://doi.org/10.54101/ACEN.2023.3.9
- ID: 253962
Cite item
Full Text
Abstract
Introduction. Microembolus detection by transcranial Doppler (TCD) is the only non-invasive modality for visualization of cerebral embolism. Currently, there is no unified classification of recorded microembolic signals (MES) that could be used in clinical practice.
The aim of the study is to investigate biophysical MES parameters in patients with ischemic stroke, as well as to assess approaches to microemboli differentiation by structure and origin to improve the diagnostic accuracy of the method and to reduce the risk of recurrent ischemic events.
Materials and methods. The inclusion criterion was TCD-detected signs of MES. We analyzed the data of 28 patients with ischemic stroke (9 women and 19 men; mean age was 58 years ± 13). We recorded power, duration, and frequency for each MES, and calculated an energy index.
Results. A total of 938 MES were reported. In patients with cardioembolic stroke and all other pathogenetic stroke subtypes, biophysical parameter limits were as follows: 14.65 dB for the average power, 9.45 ms for the average duration, and 0.16 J for the average energy index. For patients with atrial fibrillation, characteristic MES power was found to be >13 dB. The MES frequency limit was determined to be 650 Hz for microemboli differentiation by acoustic density.
Conclusion. The data obtained can be used to further search for optimal limit ranges for biophysical parameters of various MES in order to establish a single MES classification, which will increase the diagnostic value of microembolus detection by TCD in stroke treatment practice.
Full Text
##article.viewOnOriginalSite##About the authors
Ekaterina V. Orlova
Federal Сenter of Brain Research and Neurotechnologies
Author for correspondence.
Email: ekaterina.shlyk@gmail.com
ORCID iD: 0000-0002-4755-7565
SPIN-code: 3695-9148
Cand. Sci. (Med.), doctor of functional diagnostics, Department of ultrasound and functional diagnostics, Federal Center for Brain and Neurotechnologies, Moscow, Russia
Russian Federation, MoscowAlexandr B. Berdalin
Federal Сenter of Brain Research and Neurotechnologies
Email: alex_berdalin@mail.ru
ORCID iD: 0000-0001-5387-4367
SPIN-code: 3681-6911
Cand. Sci. (Med.), senior researcher Research Center for Radiology and Clinical Physiology, Federal Center for Brain and Neurotechno- logies, Moscow, Russia
Russian Federation, MoscowVladimir G. Lelyuk
Federal Сenter of Brain Research and Neurotechnologies
Email: vglelyuk@fccps.ru
ORCID iD: 0000-0002-9690-8325
SPIN-code: 1066-9840
D. Sci. (Med.), Professor
Russian Federation, MoscowReferences
- Гусев Е.И., Коновалов А.Н., Скворцова В.И. Неврология. Национальное руководство: руководство для врачей. М.; 2015. 1064 с. Gusev E.I., Konovalov A.N., Skvortsova V.I. Neurology. National leadership: a guide for physicians. Moscow; 2015. 1064 р. (In Russ.)
- Адаскин А.В. Программно-алгоритмическое обеспечение измерительно-вычислительного комплекса для исследования потоков жидкости с инородными включениями на примере комплекса медицинского назначения. Дисс. ... канд. тех. наук Москва, 2008. 137 с. Adaskin A.V. Software and algorithmic support of the measuring and computing complex for the study of fluid flows with foreign inclusions on the example of a medical complex. PhD tech. sci. diss. Moscow; 2008. 137 p. (In Russ.)
- King A., Markus H.S. Doppler embolic signals in cerebrovascular disease and prediction of stroke risk: a systematic review and meta-analysis. Stroke. 2009;40(12):3711–3717. doi: 10.1161/STROKEAHA.109.563056
- Maida C.D., Norrito R.L., Daidone M. et al. Neuroinflammatory mechanisms in ischemic stroke: focus on cardioembolic stroke, background, and therapeutic approaches. Int. J. Mol. Sci. 2020;21(18):6454. doi: 10.3390/ijms21186454
- Muengtaweepongsa S., Tantibundhit C. Microembolic signal detection by transcranial Doppler: Old method with a new indication. World J. Methodol. 2018;8(3):40–43. doi: 10.5662/wjm.v8.i3.40
- Ritter M.A., Dittrich R., Thoenissen N. et al. Prevalence and prognostic impact of microembolic signals in arterial sources of embolism. A systematic review of the literature. J. Neurol. 2008;255(7):953–961. doi: 10.1007/s00415-008-0638-8
- Devuyst G., Darbellay G., Vesin J. et al. Automatic classification of HITS into artifacts or solid or gaseous emboli by a wavelet representation combined with dual — gate TCD. Stroke. 2001;32(12):2803–2809. doi: 10.1161/hs1201.099714
- Ringelstein E.B., Droste D.W., Babikian V.L. et al. Consensus on microembolus detection by TCD. International Consensus Group on Microembolus Detection. Stroke. 1998;29(3):725–729. doi: 10.1161/01.str.29.3.725
- Рыбалко Н.В., Кузнецов А.Н., Виноградов О.И. Применение индекса модуляции частоты для определения состава микроэмболического материала. Вестник Национального медико-хирургического Центра им. Н.И. Пирогова. 2015;(1):6–9. Rybalko N.V., Kuznetsov A.N., Vinogradov O.I. Application of the frequency modulation index to determine the composition of microembolic material. Bulletin of the N.I. Pirogov National Medical and Surgical Center. 2015;(1):6–9. (In Russ.)
- Banahan C., Rogerson Z., Rousseau C. et al. An in vitro comparison of embolus differentiation techniques for clinically significant macroemboli: dual-frequency technique versus frequency modulation method. Ultrasound Med. Biol. 2014;40(11):2642–2654. doi: 10.1016/j.ultrasmedbio.2014.06.003
- Brucher R., Russell D. Assessment of temporal bone beam distortion when using multifrequency Doppler to differentiate cerebral microemboli. Cerebrovasc. Dis. 2002; 13(Suppl 4):1134–1141.
- Markus H.S., Punter M. Can transcranial Doppler discriminate between solid and gaseous microemboli? Assessment of a dual-frequency transducer system. Stroke. 2005;36(8):1731–1734. doi: 10.1161/01.STR.0000173399.20127.b3
- Russell D., Brucher R. Online automatic discrimination between solid and gaseous cerebral microemboli with the first multifrequency transcranial Doppler. Stroke. 2002;33(8):1975–1980. doi: 10.1161/01.str.0000022809.46400.4b
- Adams H.P. Jr, Bendixen B.H., Kappelle L.J. et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24(1):35–41. doi: 10.1161/01.str.24.1.35
- Choi Y., Saqqur M., Stewart E. et al. Relative energy index of microembolic signal can predict malignant microemboli. Stroke. 2010;41(4):700–706. doi: 10.1161/STROKEAHA.109.573733
- Шлык Е.В. Дифференциально-диагностические признаки артерио-артериальной и кардиальной микроэмболии при проведении транскраниального допплеровского мониторирования кровотока мозговых артерий. Ультразвуковая и функциональная диагностика. 2011;(6):97. Shlyk E.V. Diagnostic signs of arterial and cardiac microembolia during transcranial doppler monitoring of blood flow in cerebral arteries. Ultrasound and Functional Diagnostics 2011;(6):97. (In Russ.)
- Yan J., Li Z., Wills M. et al. Intracranial microembolic signals might be a potential risk factor for cognitive impairment. Neurol. Res. 2021;43(11):867–873. doi: 10.1080/01616412.2021.1939488
- Das A.S., Regenhardt R.W., LaRose S. et al. Microembolic signals detected by transcranial Doppler predict future stroke and poor outcomes. J. Neuroimaging. 2020;30(6):882–889. doi: 10.1111/jon.12749
Supplementary files
