*Result*: The association of artificial intelligence-enabled coronary plaque analysis with future non-ST elevation myocardial infarction.
Original Publication: Philadelphia, Pa. : Current Science, c1990-
Eur Heart J Cardiovasc Imaging. 2021 Feb 22;22(3):314-321. (PMID: 32793952)
J Cardiovasc Comput Tomogr. 2021 Jul-Aug;15(4):333-338. (PMID: 33423941)
Circulation. 2005 Jan 18;111(2):143-9. (PMID: 15623544)
Eur Heart J Cardiovasc Imaging. 2017 Dec 01;18(12):1331-1339. (PMID: 28950315)
J Am Coll Cardiol. 2014 Jun 3;63(21):2209-16. (PMID: 24632266)
J Am Coll Cardiol. 2018 Jun 5;71(22):2511-2522. (PMID: 29852975)
J Am Coll Cardiol. 2009 Jun 30;54(1):49-57. (PMID: 19555840)
Eur Heart J Cardiovasc Imaging. 2020 May 1;21(5):479-488. (PMID: 32065624)
Eur Heart J. 2020 Jan 14;41(3):407-477. (PMID: 31504439)
J Cardiovasc Comput Tomogr. 2014 Sep-Oct;8(5):368-74. (PMID: 25301042)
Circulation. 2000 Feb 15;101(6):598-603. (PMID: 10673250)
J Am Coll Cardiol. 2006 Apr 18;47(8 Suppl):C13-8. (PMID: 16631505)
J Am Coll Cardiol. 1988 Jul;12(1):56-62. (PMID: 3379219)
J Cardiovasc Comput Tomogr. 2009 Nov-Dec;3(6):372-82. (PMID: 20083056)
Circ Cardiovasc Imaging. 2010 Mar;3(2):179-86. (PMID: 20044512)
J Am Coll Cardiol. 2019 Sep 24;74(12):1582-1593. (PMID: 31537269)
Circulation. 2002 Aug 13;106(7):804-8. (PMID: 12176951)
J Am Heart Assoc. 2020 Mar 3;9(5):e013958. (PMID: 32089046)
J Cardiovasc Comput Tomogr. 2022 Jan-Feb;16(1):54-122. (PMID: 34955448)
J Am Coll Cardiol. 2015 Mar 3;65(8):846-855. (PMID: 25601032)
J Am Coll Cardiol. 2014 Aug 19;64(7):684-92. (PMID: 25125300)
Circulation. 1989 Apr;79(4):733-43. (PMID: 2647318)
JACC Cardiovasc Imaging. 2020 Jun;13(6):1409-1417. (PMID: 31734214)
Eur Heart J. 2020 Aug 14;41(31):2997-3004. (PMID: 32402086)
J Am Coll Cardiol. 2015 Jul 28;66(4):337-46. (PMID: 26205589)
J Am Coll Cardiol. 2000 Jan;35(1):106-11. (PMID: 10636267)
N Engl J Med. 2011 Jan 20;364(3):226-35. (PMID: 21247313)
*Further Information*
*Background: There is emerging evidence that plaque features may play a critical role in future acute coronary syndrome. In this study, we analyzed plaque features using an artificial intelligence-enabled algorithm in a clinical cohort who developed non-ST-elevation myocardial infarction (NSTEMI) following coronary CT angiogram (CCTA).
Methods: We performed a case-control study selected from 13 751 consecutive cases in a single center referred for outpatient CCTA. After a follow-up of 4.3 ± 4 years, 48 patients without preexisting coronary disease developed NSTEMI. Controls (N = 187) were matched to the cases on age, gender, BMI, and kilovoltage for CTA acquisition. Quantitative plaque analysis was performed using artificial intelligence-enabled Autoplaque software (Autoplaque version 3.0; Cedars-Sinai Medical Center, Los Angeles, California, USA). Multivariable Cox proportional hazards models were performed to identify the predictors of NSTEMI.
Results: The mean age was 64 ± 11 years. Both case and control groups had mild stenosis at baseline (26 vs 17%, P = 0.01). The total calcified plaque and fibrous plaque volume were not different (P = 0.10 and P = 0.13, respectively). Necrotic core plaque volume and fibrous fatty plaque volume were higher in the NSTEMI group (28 ± 29 vs 9 ± 13 mm3, 169 ± 157 vs 84 ± 105 mm3, respectively, both P < 0.01). In multivariable Cox regression, necrotic core volume portended the greatest risk of NSTEMI, a seven-fold higher than that of total plaque volume.
Conclusion: Using artificial intelligence-enabled plaque analysis, noncalcified plaque volume, especially necrotic core and fibrous fatty plaque volume are important precursors for future NSTEMI events.
(Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)*