Literature DB >> 35111621

The use of lesion-specific calcium morphology to guide the appropriate use of dynamic CT myocardial perfusion imaging and CT fractional flow reserve.

Xu Dai1, Zhigang Lu2, Yarong Yu3, Lihua Yu3, Hao Xu4, Jiayin Zhang3.   

Abstract

BACKGROUND: We aimed to optimize the diagnostic strategy for dynamic computed tomography myocardial perfusion imaging (CT-MPI) and CT fractional flow reserve (CT-FFR) in the evaluation of coronary artery disease (CAD).
METHODS: Patients who had undergone coronary CT angiography (CCTA) + dynamic CT-MPI and invasive coronary angiography (ICA)/FFR within a 4-week period were retrospectively included. Lesion-specific characteristics were recorded, and multivariate logistic regression was performed to determine the predictors of mismatched CT findings with ICA results. An optimized diagnostic strategy was proposed based on the diagnostic performance of dynamic CT-MPI and CT-FFR compared with ICA/FFR. A net reclassification index (NRI) was calculated to determine the incremental discriminatory power of optimized CT-FFR + dynamic CT-MPI strategy compared to CT-FFR alone.
RESULTS: The study included 180 patients with 229 diseased vessels. For CT-FFR, a calcified lesion with a calcium arc >180° was the only independent predictor for misdiagnosis of ischemic coronary stenosis (odds ratio =2.367; P=0.002). For noncalcified lesions and calcified lesions with a calcium arc ≤180°, the sensitivity and negative predictive value (NPV) of CT-FFR were similar to those of CT-MPI (all P values >0.05), whereas the specificity and positive predictive value (PPV) of CT-FFR were significantly lower (all P values <0.05). For calcified lesions with a calcium arc >180°, the specificity, NPV, and PPV of CT-FFR were inferior to those of CT-MPI (21.2% vs. 100%, 58.3% vs. 86.8%, and 62.9% vs. 100%, respectively; all P values <0.05). As guided by lesion-specific calcium morphology, an optimized CT-FFR + dynamic CT-MPI strategy (NRI =0.2; P=0.004) would have resulted in a 27.0% and 33.9% reduction of radiation dose and contrast medium consumption, respectively, and 25.3% of patients would have avoided unnecessary invasive tests.
CONCLUSIONS: The diagnostic performance of CT-FFR was significantly inferior in lesions with a calcium arc >180°. Lesion-specific calcium morphology is the preferred parameter to guide the appropriate use of CT-based functional assessment. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Coronary artery disease (CAD); computed tomography (CT); fractional flow reserve (FFR); myocardial blood flow (MBF); myocardial perfusion imaging (MPI)

Year:  2022        PMID: 35111621      PMCID: PMC8739094          DOI: 10.21037/qims-21-491

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  30 in total

Review 1.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association.

Authors:  Manuel D Cerqueira; Neil J Weissman; Vasken Dilsizian; Alice K Jacobs; Sanjiv Kaul; Warren K Laskey; Dudley J Pennell; John A Rumberger; Thomas Ryan; Mario S Verani
Journal:  Circulation       Date:  2002-01-29       Impact factor: 29.690

2.  Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry.

Authors:  Christian Tesche; Katharina Otani; Carlo N De Cecco; Adriaan Coenen; Jakob De Geer; Mariusz Kruk; Young-Hak Kim; Moritz H Albrecht; Stefan Baumann; Matthias Renker; Richard R Bayer; Taylor M Duguay; Sheldon E Litwin; Akos Varga-Szemes; Daniel H Steinberg; Dong Hyun Yang; Cezary Kepka; Anders Persson; Koen Nieman; U Joseph Schoepf
Journal:  JACC Cardiovasc Imaging       Date:  2019-08-14

3.  Influence of Coronary Calcification on the Diagnostic Performance of CT Angiography Derived FFR in Coronary Artery Disease: A Substudy of the NXT Trial.

Authors:  Bjarne L Nørgaard; Sara Gaur; Jonathon Leipsic; Hiroshi Ito; Toru Miyoshi; Seung-Jung Park; Ligita Zvaigzne; Nikolaos Tzemos; Jesper M Jensen; Nicolaj Hansson; Brian Ko; Hiram Bezerra; Evald H Christiansen; Anne Kaltoft; Jens F Lassen; Hans Erik Bøtker; Stephan Achenbach
Journal:  JACC Cardiovasc Imaging       Date:  2015-08-19

4.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ewout W Steyerberg
Journal:  Stat Med       Date:  2010-11-05       Impact factor: 2.373

5.  Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation.

Authors:  Pim A L Tonino; William F Fearon; Bernard De Bruyne; Keith G Oldroyd; Massoud A Leesar; Peter N Ver Lee; Philip A Maccarthy; Marcel Van't Veer; Nico H J Pijls
Journal:  J Am Coll Cardiol       Date:  2010-06-22       Impact factor: 24.094

6.  Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve.

Authors:  Yuehua Li; Mengmeng Yu; Xu Dai; Zhigang Lu; Chengxing Shen; Yining Wang; Bin Lu; Jiayin Zhang
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

7.  Comparison of clinical interpretation with visual assessment and quantitative coronary angiography in patients undergoing percutaneous coronary intervention in contemporary practice: the Assessing Angiography (A2) project.

Authors:  Brahmajee K Nallamothu; John A Spertus; Alexandra J Lansky; David J Cohen; Philip G Jones; Faraz Kureshi; Gregory J Dehmer; Joseph P Drozda; Mary Norine Walsh; John E Brush; Gerald C Koenig; Thad F Waites; D Scott Gantt; George Kichura; Richard A Chazal; Peter K O'Brien; C Michael Valentine; John S Rumsfeld; Johan H C Reiber; Joann G Elmore; Richard A Krumholz; W Douglas Weaver; Harlan M Krumholz
Journal:  Circulation       Date:  2013-03-07       Impact factor: 29.690

8.  Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses.

Authors:  N H Pijls; B De Bruyne; K Peels; P H Van Der Voort; H J Bonnier; J J Bartunek J Koolen; J J Koolen
Journal:  N Engl J Med       Date:  1996-06-27       Impact factor: 91.245

9.  Calcification remodeling index assessed by cardiac CT predicts severe coronary stenosis in lesions with moderate to severe calcification.

Authors:  Mengmeng Yu; Yuehua Li; Wenbin Li; Zhigang Lu; Meng Wei; Jiayin Zhang
Journal:  J Cardiovasc Comput Tomogr       Date:  2017-09-30

10.  Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

Authors:  Fay M A Nous; Ricardo P J Budde; Marisa M Lubbers; Yuzo Yamasaki; Isabella Kardys; Tobias A Bruning; Jurgen M Akkerhuis; Marcel J M Kofflard; Bas Kietselaer; Tjebbe W Galema; Koen Nieman
Journal:  Eur Radiol       Date:  2020-03-12       Impact factor: 5.315

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  1 in total

1.  Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis.

Authors:  Lihua Yu; Xiuyu Chen; Runjianya Ling; Yarong Yu; Wenyi Yang; Jianqing Sun; Jiayin Zhang
Journal:  Eur Radiol       Date:  2022-10-18       Impact factor: 7.034

  1 in total

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