A simple prediction model to estimate obstructive coronary artery disease
Abstract Background 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...
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doaj-d5afd1ba79874791ac1739a25a9d23fc2020-11-25T01:38:55ZengBMCBMC Cardiovascular Disorders1471-22612018-01-011811910.1186/s12872-018-0745-0A simple prediction model to estimate obstructive coronary artery diseaseShiqun Chen0Yong Liu1Sheikh Mohammed Shariful Islam2Hua Yao3Yingling Zhou4Ji-yan Chen5Qiang Li6Department of Cardiology, Provincial Key Laboratory of Coronary Heart Disease, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical SciencesDepartment of Cardiology, Provincial Key Laboratory of Coronary Heart Disease, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical SciencesThe George Institute for Global Health, University of SydneyDepartment of Cardiology, Provincial Key Laboratory of Coronary Heart Disease, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical SciencesDepartment of Cardiology, Provincial Key Laboratory of Coronary Heart Disease, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical SciencesDepartment of Cardiology, Provincial Key Laboratory of Coronary Heart Disease, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical SciencesThe George Institute for Global Health, University of SydneyAbstract Background 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). Methods 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. Results 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 < 0.001) in predicting OCAD, thereby identifying patients with high risks for OCAD (risk score ≥ 27) with ≥70% predictive value in 68.8% of subjects (range, 37.2–87.3% for low [≤17] and very high [≥41] risk scores). Conclusion Our data suggested that the simple MFS risk stratification tool, which is available in most primary-level clinics, showed good performance in estimating the probability of OCAD in relatively stable patients with suspected CAD; nevertheless, further validation is needed.http://link.springer.com/article/10.1186/s12872-018-0745-0Prediction modelObstructive coronary artery diseaseFramingham risk |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shiqun Chen Yong Liu Sheikh Mohammed Shariful Islam Hua Yao Yingling Zhou Ji-yan Chen Qiang Li |
spellingShingle |
Shiqun Chen Yong Liu Sheikh Mohammed Shariful Islam Hua Yao Yingling Zhou Ji-yan Chen Qiang Li A simple prediction model to estimate obstructive coronary artery disease BMC Cardiovascular Disorders Prediction model Obstructive coronary artery disease Framingham risk |
author_facet |
Shiqun Chen Yong Liu Sheikh Mohammed Shariful Islam Hua Yao Yingling Zhou Ji-yan Chen Qiang Li |
author_sort |
Shiqun Chen |
title |
A simple prediction model to estimate obstructive coronary artery disease |
title_short |
A simple prediction model to estimate obstructive coronary artery disease |
title_full |
A simple prediction model to estimate obstructive coronary artery disease |
title_fullStr |
A simple prediction model to estimate obstructive coronary artery disease |
title_full_unstemmed |
A simple prediction model to estimate obstructive coronary artery disease |
title_sort |
simple prediction model to estimate obstructive coronary artery disease |
publisher |
BMC |
series |
BMC Cardiovascular Disorders |
issn |
1471-2261 |
publishDate |
2018-01-01 |
description |
Abstract Background 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). Methods 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. Results 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 < 0.001) in predicting OCAD, thereby identifying patients with high risks for OCAD (risk score ≥ 27) with ≥70% predictive value in 68.8% of subjects (range, 37.2–87.3% for low [≤17] and very high [≥41] risk scores). Conclusion Our data suggested that the simple MFS risk stratification tool, which is available in most primary-level clinics, showed good performance in estimating the probability of OCAD in relatively stable patients with suspected CAD; nevertheless, further validation is needed. |
topic |
Prediction model Obstructive coronary artery disease Framingham risk |
url |
http://link.springer.com/article/10.1186/s12872-018-0745-0 |
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