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A simple prediction model to estimate obstructive coronary artery disease

Chen, Shiqun, Liu, Yong, Islam, Sheikh Mohammed Shariful, Yao, Hua, Zhou, Yingling, Chen, Ji-yan and Li, Qiang 2018, A simple prediction model to estimate obstructive coronary artery disease, BMC cardiovascular disorders, vol. 18, doi: 10.1186/s12872-018-0745-0.

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Title A simple prediction model to estimate obstructive coronary artery disease
Author(s) Chen, Shiqun
Liu, Yong
Islam, Sheikh Mohammed SharifulORCID iD for Islam, Sheikh Mohammed Shariful orcid.org/0000-0001-7926-9368
Yao, Hua
Zhou, Yingling
Chen, Ji-yan
Li, Qiang
Journal name BMC cardiovascular disorders
Volume number 18
Article ID 7
Total pages 9
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2018-01-16
ISSN 1471-2261
Summary 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.
Language eng
DOI 10.1186/s12872-018-0745-0
Field of Research 1102 Cardiovascular Medicine And Haematology
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30106116

Document type: Journal Article
Collections: School of Exercise and Nutrition Sciences
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.