Inter- and intra-observer reproducibility of CT-Leaman score by an independent core lab.
CT based Leaman score (CT-LeSc)
Cardiac computed tomography angiography (CCTA)
Coronary artery disease (CAD)
Reproducibility
Journal
The international journal of cardiovascular imaging
ISSN: 1875-8312
Titre abrégé: Int J Cardiovasc Imaging
Pays: United States
ID NLM: 100969716
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
24
05
2023
accepted:
17
09
2023
medline:
27
11
2023
pubmed:
25
10
2023
entrez:
24
10
2023
Statut:
ppublish
Résumé
To assess the reproducibility of CT-based Leaman score (CT-LeSc). CT-LeSc can non-invasively quantify total coronary atherosclerotic burden and is an independent long-term predictor of cardiac events. Its calculation however relies on the subjective assessment of lesions using coronary computed tomography angiography and therefore is subject to intra- and inter-observer variability. Inter-observer reproducibility was assessed by calculating the CT-LeSc in 50 patients randomly selected from the SYNTAX III REVOLUTION and ABSORB trials by two separate teams, each made up of two cardiologists, who reported results by consensus. For intra-observer reproducibility, the CT-LeSc was calculated in same 50 patients on two occasions eight weeks apart, by the same team of two cardiologists. The level of agreement was measured by the weighted kappa statistic, with intra- and inter-observer variability used to evaluate the CT-LeSc's reproducibility. The variables evaluated by weighted kappa statistics were total number of lesions; number of calcified lesions; number of non-calcified lesions; number of mixed lesions; number of obstructive lesions; number of non-obstructive lesions; and the total CT-LeSc in increments of ten and five. During assessment of inter-observer variability the mean ± standard deviation (SD) CT-LeSc calculated by the first and second team was 15.36 ± 5.57 versus 15.24 ± 5.16. The mean of the differences (precision) was 0.97, with a SD (accuracy) 1.17. The inter-observer variability was lowest for Leaman score in increments of five (weighted kappa 0.93), and highest for the total number of calcified lesions (weighted kappa 0.66). During assessment of intra-observer variability, the mean ± SD CT-LeSc were 16.61 ± 5.28 versus 16.82 ± 5.55. The mean ± SD of the differences was 1.28 ± 1.02. The intra-observer variability was the lowest for Leaman score in increments of five (weighted kappa 0.93), and the highest for the total number of lesions and calcified lesions (weighted kappa 0.65). CT-LeSc has substantial to near-perfect agreement for reproducibility.
Identifiants
pubmed: 37875690
doi: 10.1007/s10554-023-02962-3
pii: 10.1007/s10554-023-02962-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2269-2277Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature B.V.
Références
Leaman DM, Brower RW, Meester GT, Serruys P, van den Brand M (1981) Coronary artery atherosclerosis: severity of the disease, severity of angina pectoris and compromised left ventricular function. Circulation 63:285–299
doi: 10.1161/01.CIR.63.2.285
pubmed: 7449052
Shaw LJ, Blankstein R, Bax JJ et al (2021) Society of cardiovascular computed tomography/North American society of cardiovascular imaging–expert consensus document on coronary CT imaging of atherosclerotic plaque. J Cardiovasc Comput Tomogr 15:93–109
doi: 10.1016/j.jcct.2020.11.002
pubmed: 33303383
Williams MC, Moss AJ, Dweck M et al (2019) Coronary artery plaque characteristics associated with adverse outcomes in the SCOT-HEART study. J Am Coll Cardiol 73:291–301
doi: 10.1016/j.jacc.2018.10.066
pubmed: 30678759
pmcid: 6342893
Mortensen MB, Dzaye O, Steffensen FH et al (2020) Impact of plaque burden versus stenosis on ischemic events in patients with coronary atherosclerosis. J Am Coll Cardiol 76:2803–2813
doi: 10.1016/j.jacc.2020.10.021
pubmed: 33303068
Inoue K, Motoyama S, Sarai M et al (2010) Serial coronary CT angiography-verified changes in plaque characteristics as an end point: evaluation of effect of statin intervention. JACC Cardiovasc Imaging 3:691–698
doi: 10.1016/j.jcmg.2010.04.011
pubmed: 20633846
van Rosendael AR, van den Hoogen IJ, Gianni U et al (2021) Association of statin treatment with progression of coronary atherosclerotic plaque composition. JAMA Cardiol 6:1257–1266
doi: 10.1001/jamacardio.2021.3055
pubmed: 34406326
Mushtaq S, De Araujo Gonçalves P, Garcia-Garcia HM et al (2015) Long-term prognostic effect of coronary atherosclerotic burden: validation of the computed tomography-leaman score. Circ Cardiovasc Imaging 8:e002332
doi: 10.1161/CIRCIMAGING.114.002332
pubmed: 25666717
Andreini D, Pontone G, Mushtaq S et al (2017) Long-term prognostic impact of CT-Leaman score in patients with non-obstructive CAD: results from the coronary CT angiography evaluation for clinical outcomes International multicenter (CONFIRM) study. Int J Cardiol 231:18–25
doi: 10.1016/j.ijcard.2016.12.137
pubmed: 28082093
Collet C, Onuma Y, Andreini D et al (2018) Coronary computed tomography angiography for heart team decision-making in multivessel coronary artery disease. Eur Heart J 39:3689–3698
pubmed: 30312411
pmcid: 6241466
Onuma Y, Grundeken MJ, Nakatani S et al (2017) Serial 5-year evaluation of side branches jailed by bioresorbable vascular scaffolds using 3-dimensional optical coherence tomography: insights from the ABSORB cohort B trial (a clinical evaluation of the bioabsorbable everolimus eluting coronary stent system in the treatment of patients with de novo native coronary artery lesions). Circ Cardiovasc Interv. https://doi.org/10.1161/CIRCINTERVENTIONS.116.004393
doi: 10.1161/CIRCINTERVENTIONS.116.004393
pubmed: 28893770
de Araújo Gonçalves P, Garcia-Garcia HM, Dores H et al (2013) Coronary computed tomography angiography-adapted Leaman score as a tool to noninvasively quantify total coronary atherosclerotic burden. Int J Cardiovasc Imaging 29:1575–1584
doi: 10.1007/s10554-013-0232-8
pubmed: 23636301
Ozaki Y, Garcia-Garcia HM, Rogers T et al (2020) Coronary artery disease assessed by computed tomography-based Leaman score in patients with low-risk transcatheter aortic valve implantation. Am J Cardiol 125:1216–1221
doi: 10.1016/j.amjcard.2020.01.022
pubmed: 32087995
Nicol ED, Stirrup J, Roughton M, Padley SP, Rubens MB (2009) 64-Channel cardiac computed tomography: intraobserver and interobserver variability (part 1): coronary angiography. J Comput Assist Tomogr 33:161–168
doi: 10.1097/RCT.0b013e31817c423e
pubmed: 19346839
Ferencik M, Nieman K, Achenbach S (2006) Noncalcified and calcified coronary plaque detection by contrast-enhanced multi-detector computed tomography: a study of interobserver agreement. J Am Coll Cardiol 47:207–209
doi: 10.1016/j.jacc.2005.10.005
pubmed: 16386688
Stolzmann P, Scheffel H, Leschka S et al (2008) Influence of calcifications on diagnostic accuracy of coronary CT angiography using prospective ECG triggering. Am J Roentgenol 191:1684–1689
doi: 10.2214/AJR.07.4040
Williams MC, Golay SK, Hunter A et al (2015) Observer variability in the assessment of CT coronary angiography and coronary artery calcium score: substudy of the scottish COmputed tomography of the HEART (SCOT-HEART) trial. Open Heart 2:e000234
doi: 10.1136/openhrt-2014-000234
pubmed: 26019881
pmcid: 4442169
Hoffmann H, Frieler K, Hamm B, Dewey M (2008) Intra- and interobserver variability in detection and assessment of calcified and noncalcified coronary artery plaques using 64-slice computed tomography: variability in coronary plaque measurement using MSCT. Int J Cardiovasc Imaging 24:735–742
doi: 10.1007/s10554-008-9299-z
pubmed: 18587663
Cheng VY, Nakazato R, Dey D et al (2009) Reproducibility of coronary artery plaque volume and composition quantification by 64-detector row coronary computed tomographic angiography: an intraobserver, interobserver, and interscan variability study. J Cardiovasc Comput Tomogr 3:312–320
doi: 10.1016/j.jcct.2009.07.001
pubmed: 19709947
Maroules CD, Hamilton-Craig C, Branch K et al (2018) Coronary artery disease reporting and data system (CAD-RADS(TM)): inter-observer agreement for assessment categories and modifiers. J Cardiovasc Comput Tomogr 12:125–130
doi: 10.1016/j.jcct.2017.11.014
pubmed: 29217341
Cury RC, Leipsic J, Abbara S et al (2022) CAD-RADS™ 2.0–2022 coronary artery disease-reporting and data system: an expert consensus document of the society of cardiovascular computed tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr 16:536–557
doi: 10.1016/j.jcct.2022.07.002
pubmed: 35864070
Rinehart S, Vazquez G, Qian Z, Murrieta L, Christian K, Voros S (2011) Quantitative measurements of coronary arterial stenosis, plaque geometry, and composition are highly reproducible with a standardized coronary arterial computed tomographic approach in high-quality CT datasets. J Cardiovasc Comput Tomogr 5:35–43
doi: 10.1016/j.jcct.2010.09.006
pubmed: 21131252
Mancini GJ, Kamimura C, Yeoh E, Ryomoto A, Mazer CD (2022) Measurement of plaque characteristics using coronary computed tomography angiography: achieving high interobserver performance. CJC open 4:189–196
doi: 10.1016/j.cjco.2021.09.022
pubmed: 35198936
Williams MC, Earls JP, Hecht H (2022) Quantitative assessment of atherosclerotic plaque, recent progress and current limitations. J Cardiovasc Comput Tomogr 16:124–137
doi: 10.1016/j.jcct.2021.07.001
pubmed: 34326003
Stone GW, Maehara A, Lansky AJ et al (2011) A prospective natural-history study of coronary atherosclerosis. N Engl J Med 364:226–235
doi: 10.1056/NEJMoa1002358
pubmed: 21247313
Newby DE, Adamson PD, Berry C et al (2018) Coronary CT angiography and 5-Year risk of myocardial infarction. N Engl J Med 379:924–933
doi: 10.1056/NEJMoa1805971
pubmed: 30145934
Ferencik M, Mayrhofer T, Bittner DO et al (2018) Use of high-risk coronary atherosclerotic plaque detection for risk stratification of patients with stable chest pain: a secondary analysis of the PROMISE randomized clinical trial. JAMA Cardiol 3:144–152
doi: 10.1001/jamacardio.2017.4973
pubmed: 29322167
pmcid: 5838601
Serruys PW, Hara H, Garg S et al (2021) Coronary computed tomographic angiography for complete assessment of coronary artery disease: JACC State-of-the-art review. J Am Coll Cardiol 78:713–736
doi: 10.1016/j.jacc.2021.06.019
pubmed: 34384554
Min JK, Dunning A, Lin FY et al (2011) Age- and sex-related differences in all-cause mortality risk based on coronary computed tomography angiography findings results from the international multicenter CONFIRM (coronary CT angiography evaluation for clinical outcomes: an international multicenter registry) of 23,854 patients without known coronary artery disease. J Am Coll Cardiol 58:849–860
doi: 10.1016/j.jacc.2011.02.074
pubmed: 21835321
Serruys PW, Kotoku N, Norgaard BL et al (2023) Computed tomographic angiography in coronary artery disease. EuorIntervention 18:e1307
doi: 10.4244/EIJ-D-22-00776
Bergström G, Persson M, Adiels M et al (2021) Prevalence of subclinical coronary artery atherosclerosis in the general population. Circulation 144:916–929
doi: 10.1161/CIRCULATIONAHA.121.055340
pubmed: 34543072
pmcid: 8448414
van Rosendael AR, Shaw LJ, Xie JX et al (2019) Superior risk stratification with coronary computed tomography angiography using a comprehensive atherosclerotic risk score. JACC Cardiovasc Imaging 12:1987–1997
doi: 10.1016/j.jcmg.2018.10.024
pubmed: 30660516
pmcid: 6635103
Gatti JW, De Araujo Gonçalves P, Garcia-Garcia HM (2020) Computed tomography angiography-based risk discrimination: an established bright future for prognostication. JACC Cardiovasc Imaging 13:1097–1098
doi: 10.1016/j.jcmg.2019.11.025
pubmed: 32273078