A Cox-based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-year CVD Risk Prediction

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aut.relation.articlenumber8392348en_NZ
aut.relation.journalAdvances in Preventive Medicineen_NZ
aut.relation.volume2019en_NZ
aut.researcherHutcheson, Catherine
dc.contributor.authorJia, Xen_NZ
dc.contributor.authorBaig, MMen_NZ
dc.contributor.authorMirza, Fen_NZ
dc.contributor.authorGholamHosseini, Hen_NZ
dc.date.accessioned2019-07-31T00:22:50Z
dc.date.available2019-07-31T00:22:50Z
dc.date.copyright2019en_NZ
dc.date.issued2019en_NZ
dc.description.abstractBackground and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.en_NZ
dc.identifier.citationAdvances in Preventive Medicine, vol. 2019, Article ID 8392348, 11 pages, 2019. https://doi.org/10.1155/2019/8392348.
dc.identifier.doi10.1155/2019/8392348en_NZ
dc.identifier.issn2090-3480en_NZ
dc.identifier.issn2090-3499en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/12703
dc.languageenen_NZ
dc.publisherHindawi Limiteden_NZ
dc.relation.urihttps://www.hindawi.com/journals/apm/2019/8392348/
dc.rightsCopyright © 2019 Xiaona Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.accessrightsOpenAccessen_NZ
dc.titleA Cox-based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-year CVD Risk Predictionen_NZ
dc.typeJournal Article
pubs.elements-id358358
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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