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Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography.

ABSTRACT: OBJECTIVES: Coronary artery disease (CAD) severity is associated with patient prognosis. However, few efficient scoring systems have been developed to screen severe CAD in patients with stable angina and suspected CAD before coronary angiography. Here, we present a novel scoring system for CAD severity before elective coronary angiography. METHODS: Five hundred fifty-one patients with stable angina who were admitted for coronary angiography were enrolled in this study. Patients were divided into training (n?=?347) and validation (n?=?204) cohorts. Severe CAD was defined as having a Gensini score of 20 or more. All patients underwent echocardiography (ECG) to detect ejection fraction and aortic valve calcification (AVC). Multivariable analysis was applied to determine independent risk factors and develop the scoring system. RESULTS: In the training cohort, age, male sex, AVC, abnormal ECG, diabetes, hyperlipidemia, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were identified as independent factors for severe CAD by multivariable analysis, and the Severe Prediction Scoring (SPS) system was developed. C-indices of receiver operating characteristic (ROC) curves for severe CAD were 0.744 and 0.710 in the training and validation groups, respectively. The SPS system also performed well during calibration, as demonstrated by Hosmer-Lemeshow analysis in the validation group. Compared with the Diamond-Forrester score, the SPS system performed better for severe CAD prediction before elective coronary angiography. CONCLUSIONS: Severe CAD prediction was achieved by analyzing age, sex, AVC, ECG, diabetes status, and lipid levels. Angina patients who achieve high scores using this predicting system should undergo early coronary angiography.

SUBMITTER: Chen ZW

PROVIDER: S-EPMC3979855 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography.

Chen Zhang-Wei ZW Chen Ying-Hua YH Qian Ju-Ying JY Ma Jian-Ying JY Ge Jun-Bo JB

PloS one 20140408 4

<h4>Objectives</h4>Coronary artery disease (CAD) severity is associated with patient prognosis. However, few efficient scoring systems have been developed to screen severe CAD in patients with stable angina and suspected CAD before coronary angiography. Here, we present a novel scoring system for CAD severity before elective coronary angiography.<h4>Methods</h4>Five hundred fifty-one patients with stable angina who were admitted for coronary angiography were enrolled in this study. Patients were ...[more]

PMID: 24714416

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Project description:PurposeConventional CT technology yields only modest accuracy of coronary artery stenosis assessment in severely calcified lesions. Reported herein are this study's initial observations on the potential of ultra-high-resolution CT (UHR-CT) for evaluating severely calcified coronary arterial lesions.Materials and methodsFifteen patients 45 years of age or older, with history of coronary artery disease, referred for invasive coronary angiography, were prospectively enrolled. Patients underwent UHR-CT within 30 days prior to cardiac catheterization. Image noise levels and diagnostic confidence (level 1-5) using UHR-CT were compared with reconstructed images simulating conventional CT technology. Stenosis assessment for the major coronary arteries and the left main coronary artery with UHR-CT and invasive angiography were compared. Results from clinically driven coronary CT using conventional technology were considered for comparison when available.ResultsMean patient age was 67 years (range, 53-79 years). Thirteen patients were men, nine had obesity. Radiation dose was 9.3 mSv owing to expanded x-ray exposure to accommodate research software application (70%-99% of R-R cycle). Overall image noise was considerably greater for UHR-CT (50.9 ± 7.8 [standard deviation]) versus conventional CT image reconstruction (19.5 ± 8.3, P < .01), yet diagnostic confidence scores for UHR-CT were high (4.3 ± 0.9). Average calcium score in patients without stents (n = 6) was 1205, and of 86 vessels evaluated, 22 had 70% or greater stenosis depicted with invasive angiography (26%). Stenosis comparison with invasive angiography yielded 86% (19 of 22) sensitivity and 88% (56 of 64) specificity (95% CI: 65%, 97%; and 77%, 95%, respectively).ConclusionInitial observations suggest UHR-CT may be effective in overcoming the limitation of conventional CT for accurately evaluating coronary artery stenoses in severely calcified vessels.Keywords: CT-Angiography, Coronary Arteries, ArteriosclerosisClinical trial registration no. NCT04272060See also commentary by Shanbhag and Chen in this issue.© RSNA, 2021.

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Project description:Background The commercial coronary computed tomographic angiography artificial intelligence (CCTA-AI) platform has made great progress in clinical application. However, research is needed to elucidate the current stage of commercial AI platforms and the role of radiologists. This study compared the diagnostic performance of the commercial CCTA-AI platform with that of a reader based on a multicenter and multidevice sample. Methods A total of 318 patients with suspected coronary artery disease (CAD) who underwent both CCTA and invasive coronary angiography (ICA) were included in a multicenter and multidevice validation cohort between 2017 and 2021. The commercial CCTA-AI platform was used to automatically assess coronary artery stenosis by using ICA findings as the gold standard. The CCTA reader was completed by radiologists. The diagnostic performance of the commercial CCTA-AI platform and CCTA reader was evaluated at the patient and segment levels. The cutoff values of models 1 and 2 were 50% and 70% stenosis, respectively. Results It took 20.4 seconds to accomplish post-processing per patient when using the CCTA-AI platform, which was significantly shorter than the time taken to complete this task with the CCTA reader (1,112.1 s). In the patient-based analysis, the area under the curve (AUC) was 0.85 using the CCTA-AI platform and 0.61 using the CCTA reader in model 1 (stenosis ratio: 50%). In contrast, the AUC was 0.78 using the CCTA-AI platform and 0.64 using the CCTA reader in model 2 (stenosis ratio: 70%). In the segment-based analysis, the AUCs of CCTA-AI were slightly better than those of the readers. The negative predictive value (NPV) increased from model 1 to model 2. Furthermore, the diagnostic performance was better for larger-diameter arteries. Conclusions The commercial CCTA-AI platform may provide a feasible solution for the diagnosis of coronary artery stenosis, and it has a diagnostic performance that is slightly better than that of a radiologist with a moderate level of experience (5–10 years of experience).

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Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography.

Publications

Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography.

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