Cardiovascular Risk Prediction Models: A Scoping Review

Shelda Sajeev; Anthony Maeder

doi:10.1145/3290688.3290725

Cardiovascular Risk Prediction Models: A Scoping Review

Shelda Sajeev
, Anthony Maeder

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Background: The prevention of cardiovascular disease is a public health priority as it is associated with increasing morbidity and mortality worldwide. Objective: A scoping review of the existing cardiovascular risk prediction models, to provide a basis for suggesting future research directions. Methods: PubMed and Scopus were searched from 2008 to 2018 for review papers investigating the formulation and effectiveness of risk prediction models for cardiovascular disease. Results: 229 references were screened of which 4 articles were included in the review, describing development of 436 prediction models. Most of the work reported was from USA and Europe. Conclusions: Availability of larger datasets from Electronic Health Records for more comprehensive and targeted risk prediction, and advancement in data analysis and modeling methods like machine learning to enable cohort directed approaches, has prompted researchers and clinicians to rethink risk modeling.

Original language	English
Title of host publication	Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019
Publisher	Association for Computing Machinery (ACM)
ISBN (Electronic)	9781450366038
DOIs	https://doi.org/10.1145/3290688.3290725
Publication status	Published - 29 Jan 2019
Externally published	Yes
Event	2019 Australasian Computer Science Week Multiconference, ACSW 2019 - Sydney, Australia Duration: 29 Jan 2019 → 31 Jan 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2019 Australasian Computer Science Week Multiconference, ACSW 2019
Country/Territory	Australia
City	Sydney
Period	29/01/19 → 31/01/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

cardiovascular disease
data analysis
modeling
risk factors

Access to Document

10.1145/3290688.3290725

Cite this

@inproceedings{911ced40a09040b7b162ca9178c169a8,

title = "Cardiovascular Risk Prediction Models: A Scoping Review",

abstract = "Background: The prevention of cardiovascular disease is a public health priority as it is associated with increasing morbidity and mortality worldwide. Objective: A scoping review of the existing cardiovascular risk prediction models, to provide a basis for suggesting future research directions. Methods: PubMed and Scopus were searched from 2008 to 2018 for review papers investigating the formulation and effectiveness of risk prediction models for cardiovascular disease. Results: 229 references were screened of which 4 articles were included in the review, describing development of 436 prediction models. Most of the work reported was from USA and Europe. Conclusions: Availability of larger datasets from Electronic Health Records for more comprehensive and targeted risk prediction, and advancement in data analysis and modeling methods like machine learning to enable cohort directed approaches, has prompted researchers and clinicians to rethink risk modeling.",

keywords = "cardiovascular disease, data analysis, modeling, risk factors",

author = "Shelda Sajeev and Anthony Maeder",

year = "2019",

month = jan,

day = "29",

doi = "10.1145/3290688.3290725",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery (ACM)",

booktitle = "Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019",

address = "United States",

note = "2019 Australasian Computer Science Week Multiconference, ACSW 2019 ; Conference date: 29-01-2019 Through 31-01-2019",

}

Sajeev, S & Maeder, A 2019, Cardiovascular Risk Prediction Models: A Scoping Review. in Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019., a21, ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2019 Australasian Computer Science Week Multiconference, ACSW 2019, Sydney, Australia, 29/01/19. https://doi.org/10.1145/3290688.3290725

Cardiovascular Risk Prediction Models: A Scoping Review. / Sajeev, Shelda; Maeder, Anthony.
Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019. Association for Computing Machinery (ACM), 2019. a21 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cardiovascular Risk Prediction Models

T2 - 2019 Australasian Computer Science Week Multiconference, ACSW 2019

AU - Sajeev, Shelda

AU - Maeder, Anthony

PY - 2019/1/29

Y1 - 2019/1/29

N2 - Background: The prevention of cardiovascular disease is a public health priority as it is associated with increasing morbidity and mortality worldwide. Objective: A scoping review of the existing cardiovascular risk prediction models, to provide a basis for suggesting future research directions. Methods: PubMed and Scopus were searched from 2008 to 2018 for review papers investigating the formulation and effectiveness of risk prediction models for cardiovascular disease. Results: 229 references were screened of which 4 articles were included in the review, describing development of 436 prediction models. Most of the work reported was from USA and Europe. Conclusions: Availability of larger datasets from Electronic Health Records for more comprehensive and targeted risk prediction, and advancement in data analysis and modeling methods like machine learning to enable cohort directed approaches, has prompted researchers and clinicians to rethink risk modeling.

AB - Background: The prevention of cardiovascular disease is a public health priority as it is associated with increasing morbidity and mortality worldwide. Objective: A scoping review of the existing cardiovascular risk prediction models, to provide a basis for suggesting future research directions. Methods: PubMed and Scopus were searched from 2008 to 2018 for review papers investigating the formulation and effectiveness of risk prediction models for cardiovascular disease. Results: 229 references were screened of which 4 articles were included in the review, describing development of 436 prediction models. Most of the work reported was from USA and Europe. Conclusions: Availability of larger datasets from Electronic Health Records for more comprehensive and targeted risk prediction, and advancement in data analysis and modeling methods like machine learning to enable cohort directed approaches, has prompted researchers and clinicians to rethink risk modeling.

KW - cardiovascular disease

KW - data analysis

KW - modeling

KW - risk factors

UR - https://www.scopus.com/pages/publications/85061285361

U2 - 10.1145/3290688.3290725

DO - 10.1145/3290688.3290725

M3 - Conference contribution

AN - SCOPUS:85061285361

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019

PB - Association for Computing Machinery (ACM)

Y2 - 29 January 2019 through 31 January 2019

ER -

Cardiovascular Risk Prediction Models: A Scoping Review

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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