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      Diagnosing coronary artery disease by sound analysis from coronary stenosis induced turbulent blood flow: diagnostic performance in patients with stable angina pectoris

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          Abstract

          Optimizing risk assessment may reduce use of advanced diagnostic testing in patients with symptoms suggestive of stable coronary artery disease (CAD). Detection of diastolic murmurs from post-stenotic coronary turbulence with an acoustic sensor placed on the chest wall can serve as an easy, safe, and low-cost supplement to assist in the diagnosis of CAD. The aim of this study was to evaluate the diagnostic accuracy of an acoustic test (CAD-score) to detect CAD and compare it to clinical risk stratification and coronary artery calcium score (CACS). We prospectively enrolled patients with symptoms of CAD referred to either coronary computed tomography or invasive coronary angiography (ICA). All patients were tested with the CAD-score system. Obstructive CAD was defined as more than 50 % diameter stenosis diagnosed by quantitative analysis of the ICA. In total, 255 patients were included and obstructive CAD was diagnosed in 63 patients (28 %). Diagnostic accuracy evaluated by receiver operating characteristic curves was 72 % for the CAD-score, which was similar to the Diamond–Forrester clinical risk stratification score, 79 % ( p = 0.12), but lower than CACS, 86 % ( p < 0.01). Combining the CAD-score and Diamond–Forrester score, AUC increased to 82 %, which was significantly higher than the standalone CAD-score ( p < 0.01) and Diamond–Forrester score ( p < 0.05). Addition of the CAD-score to the Diamond–Forrester score increased correct reclassification, categorical net-reclassification index = 0.31 ( p < 0.01). This study demonstrates the potential use of an acoustic system to identify CAD. The combination of clinical risk scores and an acoustic test seems to optimize patient selection for diagnostic investigation.

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          A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension.

          The aim was to validate, update, and extend the Diamond-Forrester model for estimating the probability of obstructive coronary artery disease (CAD) in a contemporary cohort. Prospectively collected data from 14 hospitals on patients with chest pain without a history of CAD and referred for conventional coronary angiography (CCA) were used. Primary outcome was obstructive CAD, defined as ≥ 50% stenosis in one or more vessels on CCA. The validity of the Diamond-Forrester model was assessed using calibration plots, calibration-in-the-large, and recalibration in logistic regression. The model was subsequently updated and extended by revising the predictive value of age, sex, and type of chest pain. Diagnostic performance was assessed by calculating the area under the receiver operating characteristic curve (c-statistic) and reclassification was determined. We included 2260 patients, of whom 1319 had obstructive CAD on CCA. Validation demonstrated an overestimation of the CAD probability, especially in women. The updated and extended models demonstrated a c-statistic of 0.79 (95% CI 0.77-0.81) and 0.82 (95% CI 0.80-0.84), respectively. Sixteen per cent of men and 64% of women were correctly reclassified. The predicted probability of obstructive CAD ranged from 10% for 50-year-old females with non-specific chest pain to 91% for 80-year-old males with typical chest pain. Predictions varied across hospitals due to differences in disease prevalence. Our results suggest that the Diamond-Forrester model overestimates the probability of CAD especially in women. We updated the predictive effects of age, sex, type of chest pain, and hospital setting which improved model performance and we extended it to include patients of 70 years and older.
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            PredictABEL: an R package for the assessment of risk prediction models

            The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL (http://www.genabel.org) and CRAN (http://cran.r-project.org/).
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              Estimating the likelihood of significant coronary artery disease.

              Among 23 clinical characteristics examined in 3,627 consecutive, symptomatic patients referred for cardiac catheterization between 1969 and 1979, nine were found to be important for estimating the likelihood a patient had significant coronary artery disease. A model using these characteristics accurately estimated the likelihood of disease when applied prospectively to 1,811 patients referred since 1979 and when used to estimate the prevalence of disease in subgroups reported in the literature. Since accurate estimates of the likelihood of significant disease that are based on clinical characteristics are reproducible, they should be used in interpreting the results of additional noninvasive tests and in quantitating the added diagnostic value.
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                Author and article information

                Contributors
                +4578452050 , sw@dadlnet.dk
                Journal
                Int J Cardiovasc Imaging
                Int J Cardiovasc Imaging
                The International Journal of Cardiovascular Imaging
                Springer Netherlands (Dordrecht )
                1569-5794
                1875-8312
                3 September 2015
                3 September 2015
                2016
                : 32
                : 235-245
                Affiliations
                [ ]Department of Cardiology, Institute of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus, Denmark
                [ ]Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
                [ ]College of Medicine, Qatar University, Doha, Qatar
                [ ]Department of Internal Medicine, Hospital Unit West, Herning, Denmark
                Article
                753
                10.1007/s10554-015-0753-4
                4737789
                26335368
                d04c0671-6507-41b9-b7d9-826e0980e2e3
                © The Author(s) 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 9 April 2015
                : 22 August 2015
                Categories
                Original Paper
                Custom metadata
                © Springer Science+Business Media Dordrecht 2016

                Cardiovascular Medicine
                coronary artery disease,angina pectoris,heart sounds,cardiovascular diagnostic technic,sensitivity and specificity

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