Ultraschall Med 2015; 36(05): 480-486
DOI: 10.1055/s-0034-1385462
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

A Novel Ultrasound-Based Carotid Plaque Risk Index Associated with the Presence of Cerebrovascular Symptoms

Ein neuer ultraschallbasierter Karotis-Plaque-Risikoindex ist assoziiert mit dem Auftreten zerebrovaskulärer Symptome
B. Kanber
1   Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
,
T. C. Hartshorne
2   Department of Vascular and Endovascular Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK
,
M. A. Horsfield
1   Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
,
A. R. Naylor
2   Department of Vascular and Endovascular Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK
,
T. G. Robinson
1   Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
3   NIHR Biomedical Research Unit for Cardiovascular Sciences, University of Leicester, Leicester, UK
,
K. V. Ramnarine
4   Department of Medical Physics, University Hospitals of Leicester NHS Trust, Leicester, UK
› Author Affiliations
Further Information

Publication History

15 March 2014

14 September 2014

Publication Date:
12 November 2014 (online)

Abstract

Purpose: The purpose of this study was to determine the efficacy of a novel ultrasound-based carotid plaque risk index (CPRI) in predicting the presence of cerebrovascular symptoms in patients with carotid artery stenosis.

Materials and Methods: This was a cross-sectional, observational study involving 56 patients (mean age 76.6 years, 62.5 % male). Plaque grayscale median (GSM) and surface irregularity indices (SII) were measured in 82 stenosed carotid arteries (range 10 – 95 %) and combined with the degree of stenosis (DOS) in the form of (DOS*SII)/(1 + GSM). A reduced index DOS/(1 + GSM) not incorporating plaque surface irregularities was also investigated. Receiver operating characteristic curves (ROC) were used to study the diagnostic efficacy of CPRI, comparing against DOS and an equivalent risk index constructed using a conventional logistic regression based method with model parameters optimized to the dataset (CPRIlogistic).

Results: There were 42 stenosed carotid arteries with cerebrovascular symptoms, and 40 without symptoms. The presence of symptoms significantly correlated with DOS, GSM and SII (p < 0.01). The median CPRI of the symptomatic (asymptomatic) groups were 23.2 (9.2) compared with 0.71 (0.30) for CPRIlogistic (p < 0.01). The diagnostic performance of CPRI exceeded that of CPRIlogistic and DOS, and demonstrated a better separation of the symptomatic and asymptomatic groups.

Conclusion: Our novel risk index combines quantitative measures of carotid plaque echogenicity and surface irregularities with the degree of stenosis. It is a better predictor of cerebrovascular symptoms than the degree of stenosis and could be valuable in studies and clinical trials aimed at identifying vulnerable carotid artery stenoses.

Zusammenfassung

Ziel: Ziel der Studie war die Bestimmung der Leistungsfähigkeit des neuen ultraschallbasierten Karotis-Plaque-Risikoindex (CPRI) zur Vorhersage von zerebrovaskulären Symptomen bei Patienten mit Karotisstenose.

Material und Methoden: Eine Querschnitts-Beobachtungsstudie in 56 Patienten (∅ 76,6 Jahre, 62,5 % ♂). Medianer Grauwert (GSM) der Plaque und Oberflächenunregelmäßigkeits-Indizes (SII) wurden in 82 stenosierten Karotisarterien (Bereich 10 – 95 %) bestimmt und mit dem Stenosegrad (DOS) mittels der Formel (DOS*SII)/(1 + GSM) verbunden. Ein reduzierter Index DOS/(1 + GSM), ohne Berücksichtigung von SII wurde untersucht. „Receiver Operating Characteristic“ (ROC)-Kurven wurden angewandt, um die diagnostische Effizienz des CPRI mit DOS und einem gleichwertigen Risikoindex zu vergleichen, letzterer erstellt mittels einer auf konventionelle logistische Regression basierenden Methode mit für den Datensatz optimierten Modellparametern (CPRIlogistic).

Ergebnisse: 42 Stenosen mit zerebrovaskulären Symptomen und 40 symptomlose Stenosen lagen vor. Das Auftreten von Symptomen korrelierte signifikante mit DOS, GSM und SII (p < 0,01). Der Median des CPRI in den symtomatischen (asymtomatischen) Gruppen betrug 23,2 % (9,2) im Vergleich zu 0,71 (0,30) des CPRIlogistic der symptomatischen (asymtomatischen) Gruppen (p < 0,01). In diagnostischer Leistung war CPRI der CPRIlogistic und dem DOS überlegen und zeigte eine bessere Trennung zwischen symptomatischen und asymptomatischen Gruppen.

Schlussfolgerung: Unser neuer Risikoindex vereinigt die quantitative Messung der Karotisplaque-Echogenität und der Unregelmäßigkeiten der Oberfläche mit dem Grad der Stenose. Es ist ein besserer Prädiktor für zerebrovaskuläre Symptome als der DOS und könnte sich in Untersuchungen und klinischen Studien, die eine Identifizierung vulnerabler Karotisplaques zum Ziel haben, als wertvoll erweisen.

 
  • References

  • 1 European Carotid Surgery Trialists' Collaborative Group. MRC European Carotid Surgery Trial: interim results for symptomatic patients with severe (70-99%) or with mild (0-29%) carotid stenosis. Lancet 1991; 337: 1235-1243
  • 2 North American Symptomatic Carotid Endarterectomy Trial Collaborators. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. The New England Journal of Medicine 1991; 325: 445-453
  • 3 Naylor AR. Time to rethink management strategies in asymptomatic carotid artery disease. Nature Reviews Cardiology 2012; 9: 116-124
  • 4 Kanber B, Hartshorne TC, Horsfield MA et al. Dynamic variations in the ultrasound greyscale median of carotid artery plaques. Cardiovascular Ultrasound 2013; 11: 21
  • 5 Kanber B, Hartshorne TC, Horsfield MA et al. Quantitative assessment of carotid plaque surface irregularities and correlation to cerebrovascular symptoms. Cardiovascular Ultrasound 2013; 11: 38
  • 6 Aburahma AF, Kyer PD, Robinson PA et al. The correlation of ultrasonic carotid plaque morphology and carotid plaque hemorrhage: clinical implications. Surgery 1998; 124: n721-n 726
  • 7 Aburahma AF, Covelli MA, Robinson PA et al. The role of carotid duplex ultrasound in evaluating plaque morphology: potential use in selecting patients for carotid stenting. Journal of Endovascular Surgery 1999; 6: 59-65
  • 8 Kessler C, Von Maravic M, Brückmann H et al. Ultrasound for the assessment of the embolic risk of carotid plaques. Acta Neurologica Scandinavica 1995; 92: 231-234
  • 9 Pedro LM, Pedro MM, Gonçalves I et al. Computer-assisted carotid plaque analysis: characteristics of plaques associated with cerebrovascular symptoms and cerebral infarction. European Journal of Vascular and Endovascular Surgery 2000; 19: 118-123
  • 10 Carra G, Visonà A, Bonanome A et al. Carotid plaque morphology and cerebrovascular events. International Angiology 2003; 22: 284-289
  • 11 Ding S, Zhang M, Zhao Y et al. The role of carotid plaque vulnerability and inflammation in the pathogenesis of acute ischemic stroke. American Journal of the Medical Sciences 2008; 336: 27-31
  • 12 Golledge J, Cuming R, Ellis M et al. Carotid plaque characteristics and presenting symptom. British Journal of Surgery 1998; 84: 1697-1701
  • 13 Steinke W, Hennerici M, Rautenberg W et al. Symptomatic and asymptomatic high-grade carotid stenoses in Doppler color-flow imaging. Neurology 1992; 42: 131-138
  • 14 Prabhakaran S, Rundek T, Ramas R et al. Carotid plaque surface irregularity predicts ischemic stroke: the northern Manhattan study. Stroke 2006; 37: 2696-2701
  • 15 Biasi GM, Sampaolo A, Mingazzini P et al. Computer analysis of ultrasonic plaque echolucency in identifying high risk carotid bifurcation lesions. European Journal of Vascular and Endovascular Surgery 1999; 17: 476-479
  • 16 Elatrozy T, Nicolaides A, Tegos T et al. The objective characterisation of ultrasonic carotid plaque features. European Journal of Vascular and Endovascular Surgery 1998; 16: 223-230
  • 17 Grønholdt ML, Nordestgaard BG, Schroeder TV et al. Ultrasonic echolucent carotid plaques predict future strokes. Circulation 2001; 104: 68-73
  • 18 Polak JF, Shemanski L, Leary DH et al. Hypoechoic plaque at US of the carotid artery: an independent risk factor for incident stroke in adults aged 65 years or older. Cardiovascular Health Study. Radiology 1998; 208: 649-654
  • 19 Tegos TJ, Stavropoulos P, Sabetai MM et al. Determinants of carotid plaque instability: echoicity versus heterogeneity. European Journal of Vascular and Endovascular Surgery 2001; 22: 22-30
  • 20 Salem MK, Sayers RD, Bown MJ et al. Patients with recurrent ischaemic events from carotid artery disease have a large lipid core and low GSM. European Journal of Vascular and Endovascular Surgery 2012; 43: 147-153
  • 21 Ruiz-ares G, Fuentes B, Martínez-sánchez P et al. Utility of the assessment of echogenicity in the identification of symptomatic carotid artery atheroma plaques in ischemic stroke patients. Cerebrovascular Diseases 2012; 32: 535-541
  • 22 Hoogi A, Akkus Z, Van Oord D et al. Quantitative analysis of ultrasound contrast flow behavior in carotid plaque neovasculature. Ultrasound in Medicine & Biology 2013; 38: 2072-2083
  • 23 Prati P, Tosetto A, Casaroli M et al. Carotid plaque morphology improves stroke risk prediction: usefulness of a new ultrasonographic score. Cerebrovascular Diseases 2011; 31: 300-304
  • 24 Momjian-mayor I, Kuzmanovic I, Momjian S et al. Accuracy of a novel risk index combining degree of stenosis of the carotid artery and plaque surface echogenicity. Stroke 2012; 43: 1260-1265
  • 25 Pedro LM, Fernandes J, Pedro MM et al. Ultrasonographic risk score of carotid plaques. European Journal of Vascular and Endovascular Surgery 2002; 24: 492-498
  • 26 Afonso D, Seabra J, Suri JS et al. A CAD system for atherosclerotic plaque assessment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2013 2012. 1008-1011
  • 27 Nicolaides AN, Kakkos SK, Kyriacou E et al. Asymptomatic internal carotid artery stenosis and cerebrovascular risk stratification. Journal of Vascular Surgery 2010; 52: 1486-1496
  • 28 Kyriacou E, Nicolaides A, Pattichis CS et al. First and second order statistical texture features in carotid plaque image analysis: preliminary results from ongoing research. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012 2011. 6655-6658
  • 29 Mayor I, Momjian S, Lalive P et al. Carotid plaque: comparison between visual and grey-scale median analysis. Ultrasound in Medicine & Biology 2003; 29: 961-966
  • 30 Grant EG, Benson CB, Moneta GL et al. Carotid artery stenosis: gray-scale and Doppler US diagnosis--Society of Radiologists in Ultrasound Consensus Conference. Radiology 2003; 229: 340-346
  • 31 Oates CP, Naylor AR, Hartshorne T et al. Joint recommendations for reporting carotid ultrasound investigations in the United Kingdom. European Journal of Vascular and Endovascular Surgery 2008; 37: 251-261
  • 32 Piliouras N, Kalatzis I, Theocharakis P et al. Development of the probabilistic neural networkraphic characterization and identification of symptomatic carotid plaques. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011 2010. 6110-6113
  • 33 Kyriacou EC, Petroudi S, Pattichis CS et al. Prediction of high-risk asymptomatic carotid plaques based on ultrasonic image features. IEEE Transactions on Information Technology in Biomedicine 2013; 16: 966-973
  • 34 Acharya UR, Mookiah MRK, Vinitha Sree S et al. Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Medical & Biological Engineering & Computing 2013; 51: 513-523
  • 35 Acharya UR, Sree SV, Krishnan MMR et al. Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound in Medicine & Biology 2012; 38: 899-915
  • 36 Tsiaparas NN, Golemati S, Andreadis I et al. Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Transactions on Information Technology in Biomedicine 2011; 15: 130-137
  • 37 Mougiakakou SGR, Golemati S, Gousias I et al. Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks. Ultrasound in Medicine & Biology 2006; 33: 26-36
  • 38 Christodoulou CI, Pattichis CS, Pantziaris M et al. Texture-based classification of atherosclerotic carotid plaques. IEEE Transactions on Medical Imaging 2003; 22: 902-912
  • 39 Stoitsis J, Golemati S, Nikita KS et al. Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means. Proceedings of the IEEE Engineering in Medicine and Biology Society 2007; 2: 1407-1410