Usefulness of routine aortic valve calcium score measurement for risk stratification of aortic stenosis and coronary artery disease in patients scheduled cardiac multislice computed tomography

Shimizu, Kazuki; Yamamoto, Masanori; Kōyama, Yutaka; Kodama, Atsuko; Sato, Hirotomo; Kano, Seiji; Teramoto, Tamio; Kimura, Masashi; Sawada, Koshi; Ogawa, Shinji; Baba, Hiroshi; Tsuchikane, Etsuo; Okawa, Yasuhide; Suzuki, Takahiko

doi:10.1016/j.ijcha.2015.10.008

Cited by 11 publications

(17 citation statements)

References 13 publications

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“…AVC is closely associated with AS severity, and AVC burden measured by CT helps diagnose severe AS when Doppler parameters are inconclusive, specifically in patients with suspected low-flow, low-gradient AS. For the quantification of AVC, Agatston scoring was the most frequently used method, exhibiting good diagnostic performance for determining severe AS, having c-indexes of 0.81–0.91, sensitivities of 80–89.7%, and specificities of 77–89% ( 3 4 24 25 26 ). However, in our study, using AVC score to diagnose severe AS had a c-index of 0.778, sensitivity of 58.3%, and specificity of 97.2% in the validation set.…”

Section: Discussionmentioning

confidence: 99%

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Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

et al. 2021

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Objective We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869–0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815–0.948] and 0.899 [95% CI, 0.820–0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant ( p > 0.05 for all). Conclusion Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

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Section: Discussionmentioning

confidence: 99%

“…Previous studies of AVC score on CT used different cutoff values for the calculated scores ( 3 4 24 25 26 ). Some studies suggested sex-specific cutoff values (≥ 1200 Agatston score for women and ≥ 2000 Agatston score for men) for diagnosing severe AS, and the current guideline accepted these cutoff values ( 4 6 26 ).…”

Section: Discussionmentioning

confidence: 99%

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

et al. 2021

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“…The AVCS area of interest comprised the sinotubular junction (STJ), valve cusp, aortic annulus, and left ventricle outflow tract. The start of acquisition was placed from the bottom of the valve to the level of the STJ (18). The specific scanning parameters and contrast medium administration of CT are described in the supplementary material.…”

Section: Methodsmentioning

confidence: 99%

Association between aortic valvular calcification and characteristics of the aortic valve in patients with bicuspid aortic valve stenosis

Choi

Shin

et al. 2018

Acta Radiol

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Background Aortic valve calcification quantification using cardiac computed tomography (CCT) is a reliable marker for aortic stenosis (AS) in patients with bicuspid aortic valve (BAV) disease. Purpose To determine the association of Agatston aortic valve calcium score (AVCS) with morphological and hemodynamic characteristics of BAV and define cut-off AVCS for optimizing the grade of AS in patients with bicuspid AS. Material and Methods This study included 161 BAV patients with AS regardless of aortic regurgitation who underwent transthoracic echocardiography and CCT. BAVs were classified according to orientation of cusps and presence of raphe. Associations of AVCS with characteristics of BAV morphology and functional variables were determined by linear regression analysis. Area under the receiver operating characteristic curve (AUC) was used to determine the cut-off AVCS greater than which the diagnosis of severe AS was optimized. Results AVCS was significantly different according to sex ( P < 0.001), AS severity ( P < 0.001), type of valvular dysfunction ( P = 0.011), and orientation of cusps ( P = 0.028). Multiple linear regression showed that AVCS was significantly associated with sex (estimate = -0.583, P < 0.001) and AS severity (estimate = 0.817, P < 0.001). AVCS was a predictor for severe AS with AUC of 0.80 in both women ( P = 0.002) and men ( P < 0.001). Its cut-off value was 1423 Agatston unit (AU) in women and 2573 AU in men. Conclusions In patients with bicuspid AS, AVCS was significantly higher in men and those with severe AS. However, AVCS was not significantly associated with morphological characteristics of BAV or the type of valvular dysfunction.

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“…Coronary artery calcific disease and the aortic calcification score are other independent risk factors for mortality and morbidity, such as MACCE [Ozturk 2016;Rencuzogullari 2018;Raggi 2000]. However, the relationship between SS and CAC score and aortic stiffness has been reported in a limited number of case series [Budoff 2002;Shimizu 2015].…”

Section: Introductionmentioning

confidence: 99%

SYNTAX and Coronary Artery Calcium Score Predict Atherosclerotic Plaque Formation in the Ascending Aorta

Haberal

Ballı

Tekin

et al. 2021

HSF

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Background: There is no study about the relationship between the complexity of coronary artery disease (SYNTAX SCORE; SS), and coronary artery calcium (CAC) score, accompanied with aortic calcium score (ACS) levels. The objective of this study was to investigate the relationship between the preoperative SS and CAC scores accompanying ACS in isolated CABG patients and their postoperative clinical results. Methods: This study included 130 consecutive CABG patients. The mean age of the patients was 62.3 ± 8.62 years (range: 47-84 years). SS was measured using coronary angiography by an experienced cardiologist. We investigated the ACS accompanied with CAC scores using a multidetector computed tomography (MDCT) in the same session, preoperatively. Measurements of the CAC score and ACS were measured by an experienced radiologist, who was unaware of the study in the same session. In order to investigate aortic wall pathology in patients with positive aortic calcification, we provided aortic tissue samples prior to the proximal anastomosis of bypass grafts using No:11 scalpel. Results: Overall median SS was 39 ± 7.2 (range: 15-41). CAC score was zero in 34 patients (26.1%). For the patients with a CAC score of zero, the median SS was 32 ± 9.4. There was no evidence of aortic calcification or plaque formation in 62 patients (47.6%). In these patients, the median SS was 35.6 ± 11.3. No significant difference was found when both groups were compared and for those patients with a calcific score of zero (P = .85). The median CAC score and ACS were 238 ± 122 AU (range: 0-1238 AU) and 112 ± 40 AU (range: 0-730 AU), respectively (P = .0033). For patients with a CAC score and ACS ≥400 AU, the mean SYNTAX score was ≥ 37. SS was correlated with CAC score (R:0.585; P < .0001). SYNTAX was correlated with ACS (R:0.557; P < .001). In multivariate analysis of SS (OR 1.053, 95% CI: 1.003–1.106, P = .039), gender (OR 0.189, 95% CI: 0.053–0.678, P = 0.011), age (OR 1.454, 95% CI: 1.256–1.632, P = .012), and diabetes mellitus (OR 0.341, 95% CI: 1.006–1.124, P = .014) were independent predictors for CAC score and aortic calcification. Conclusions: CAC score and ACS are strongly correlated with the complexity of coronary arteries in CABG patients. The total CAC score (≥ 400 AU) was independently associated with the degree of SS (>37). To prevent MACCE and mortality in CABG patients, we suggest the measurement of CAC score accompanied with ACS using MDCT as a non-invasive method. Highlight points: • Atherosclerotic plaque formation in aorta and coronary arteries are the main risk factors for stroke and infarction in CABG operations. •SYNTAX score value and aortic atherosclerosis levels are directly correlated. •SYNTAX score may predict the complications due to atherosclerosis during heart surgery.

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Usefulness of routine aortic valve calcium score measurement for risk stratification of aortic stenosis and coronary artery disease in patients scheduled cardiac multislice computed tomography

Cited by 11 publications

References 13 publications

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

Association between aortic valvular calcification and characteristics of the aortic valve in patients with bicuspid aortic valve stenosis

SYNTAX and Coronary Artery Calcium Score Predict Atherosclerotic Plaque Formation in the Ascending Aorta

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