Cardiovascular Risk Prediction Models: A Scoping Review

Shelda Sajeev, Anthony Maeder

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference, ACSW 2019
PublisherAssociation for Computing Machinery (ACM)
ISBN (Electronic)9781450366038
DOIs
Publication statusPublished - 29 Jan 2019
Externally publishedYes
Event2019 Australasian Computer Science Week Multiconference, ACSW 2019 - Sydney, Australia
Duration: 29 Jan 201931 Jan 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 Australasian Computer Science Week Multiconference, ACSW 2019
CountryAustralia
CitySydney
Period29/01/1931/01/19

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Keywords

  • cardiovascular disease
  • data analysis
  • modeling
  • risk factors

Cite this

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). https://doi.org/10.1145/3290688.3290725