2021
DOI: 10.1186/s12874-021-01472-x
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Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review

Abstract: Objective The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories. Methods We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms covered three areas: data type, modelling type and disease area including search terms such as “longitudinal”, “trajector*” and “cardiovasc*” respectively. Studies were filtered to meet the following inclusion cr… Show more

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Cited by 7 publications
(4 citation statements)
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“…Unhealthy lifestyle habits, including a poor and unbalanced diet, lack of physical exercise, obesity, excessive smoking, and drinking, can lead to CVD. Risk prediction models can be used to understand and manage CVD and have become an essential part of the clinical decision making [ 121 ]. Many risk prediction models for CVD use one data point per patient (usually at the baseline), such as the widely used Framingham risk score, which predicts the risk of coronary heart disease [ 122 ], or QRISK3, which predicts risk of CVD in a subset of the UK population and is widely used in CVD risk stratification in the UK [ 123 ].…”
Section: Role Of Tea Plant and Related Compounds In Cardiovascular Di...mentioning
confidence: 99%
“…Unhealthy lifestyle habits, including a poor and unbalanced diet, lack of physical exercise, obesity, excessive smoking, and drinking, can lead to CVD. Risk prediction models can be used to understand and manage CVD and have become an essential part of the clinical decision making [ 121 ]. Many risk prediction models for CVD use one data point per patient (usually at the baseline), such as the widely used Framingham risk score, which predicts the risk of coronary heart disease [ 122 ], or QRISK3, which predicts risk of CVD in a subset of the UK population and is widely used in CVD risk stratification in the UK [ 123 ].…”
Section: Role Of Tea Plant and Related Compounds In Cardiovascular Di...mentioning
confidence: 99%
“…presence or absence of disease) or decision trees (e.g. high-risk or low-risk) [9,10]. However, these approaches are limited in their accuracy, as they tend to rely on a single set of features and metrics that can be used to accurately predict the risk of heart disease.…”
Section: Introductionmentioning
confidence: 99%
“…Reviews of JM methods have been described elsewhere [ 9 , 10 ]. However, papers using JM in CVD have been limited to a few longitudinal variables at most [ 11 ], and the capability of JM in handling higher-dimensional data remains unclear. Machine Learning (ML) methods have been the solution for higher-dimensional data.…”
Section: Introductionmentioning
confidence: 99%