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A simple prediction model to estimate obstructive coronary artery disease.

ABSTRACT: A simple noninvasive model to predict obstructive coronary artery disease (OCAD) may promote risk stratification and reduce the burden of coronary artery disease (CAD). This study aimed to develop pre-procedural, noninvasive prediction models that better estimate the probability of OCAD among patients with suspected CAD undergoing elective coronary angiography (CAG).We included 1262 patients, who had reliable Framingham risk variable data, in a cohort without known CAD from a prospective registry of patients referred for elective CAG. We investigated pre-procedural OCAD (?50% stenosis in at least one major coronary vessel based on CAG) predictors.A total of 945 (74.9%) participants had OCAD. The final modified Framingham scoring (MFS) model consisted of anemia, high-sensitivity C-reactive protein, left ventricular ejection fraction, and five Framingham factors (age, sex, total and high-density lipoprotein cholesterol, and hypertension). Bootstrap method (1000 times) revealed that the model demonstrated a good discriminative power (c statistic, 0.729?±?0.0225; 95% CI, 0.69-0.77). MFS provided adequate goodness of fit (P?=?0.43) and showed better performance than Framingham score (c statistic, 0.703 vs. 0.521; P?

SUBMITTER: Chen S

PROVIDER: S-EPMC5771201 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A simple prediction model to estimate obstructive coronary artery disease.

Chen Shiqun S Liu Yong Y Islam Sheikh Mohammed Shariful SMS Yao Hua H Zhou Yingling Y Chen Ji-Yan JY Li Qiang Q

BMC cardiovascular disorders 20180116 1

<h4>Background</h4>A simple noninvasive model to predict obstructive coronary artery disease (OCAD) may promote risk stratification and reduce the burden of coronary artery disease (CAD). This study aimed to develop pre-procedural, noninvasive prediction models that better estimate the probability of OCAD among patients with suspected CAD undergoing elective coronary angiography (CAG).<h4>Methods</h4>We included 1262 patients, who had reliable Framingham risk variable data, in a cohort without k ...[more]

PMID: 29338684

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Project description:ObjectivesTo develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.DesignRetrospective pooled analysis of individual patient data.Setting18 hospitals in Europe and the United States.ParticipantsPatients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively).Main outcome measuresObstructive coronary artery disease (≥ 50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined.ResultsWe included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory.ConclusionsUpdated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.

Dataset Information

A simple prediction model to estimate obstructive coronary artery disease.

Publications

A simple prediction model to estimate obstructive coronary artery disease.

Similar Datasets

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