Cardiovascular risk prediction in healthy older people.

Neumann, JT; Thao, LTP; Callander, E; Chowdhury, E; Williamson, JD; Nelson, MR; Donnan, G; Woods, RL; Reid, CM; Poppe, KK; Jackson, R; Tonkin, AM; McNeil, JJ

Cardiovascular risk prediction in healthy older people.

Neumann, JT Thao, LTP Callander, E

Chowdhury, E Williamson, JD Nelson, MR Donnan, G Woods, RL Reid, CM Poppe, KK Jackson, R Tonkin, AM McNeil, JJ

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Geroscience, 2022, 44, (1), pp. 403-413
Issue Date:: 2022-02

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Field	Value	Language
dc.contributor.author	Neumann, JT
dc.contributor.author	Thao, LTP
dc.contributor.author	Callander, E https://orcid.org/0000-0001-7233-6804
dc.contributor.author	Chowdhury, E
dc.contributor.author	Williamson, JD
dc.contributor.author	Nelson, MR
dc.contributor.author	Donnan, G
dc.contributor.author	Woods, RL
dc.contributor.author	Reid, CM
dc.contributor.author	Poppe, KK
dc.contributor.author	Jackson, R
dc.contributor.author	Tonkin, AM
dc.contributor.author	McNeil, JJ
dc.date.accessioned	2023-02-10T02:02:02Z
dc.date.available	2021-11-02
dc.date.available	2023-02-10T02:02:02Z
dc.date.issued	2022-02
dc.identifier.citation	Geroscience, 2022, 44, (1), pp. 403-413
dc.identifier.issn	2509-2715
dc.identifier.issn	2509-2723
dc.identifier.uri	http://hdl.handle.net/10453/166035
dc.description.abstract	Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Springer Nature
dc.relation.ispartof	Geroscience
dc.relation.isbasedon	10.1007/s11357-021-00486-z
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Aged
dc.subject.mesh	Brain Ischemia
dc.subject.mesh	Cardiovascular Diseases
dc.subject.mesh	Heart Disease Risk Factors
dc.subject.mesh	Humans
dc.subject.mesh	Risk Factors
dc.subject.mesh	Stroke
dc.subject.mesh	Humans
dc.subject.mesh	Brain Ischemia
dc.subject.mesh	Cardiovascular Diseases
dc.subject.mesh	Risk Factors
dc.subject.mesh	Aged
dc.subject.mesh	Stroke
dc.subject.mesh	Heart Disease Risk Factors
dc.subject.mesh	Aged
dc.subject.mesh	Brain Ischemia
dc.subject.mesh	Cardiovascular Diseases
dc.subject.mesh	Heart Disease Risk Factors
dc.subject.mesh	Humans
dc.subject.mesh	Risk Factors
dc.subject.mesh	Stroke
dc.title	Cardiovascular risk prediction in healthy older people.
dc.type	Journal Article
utslib.citation.volume	44
utslib.location.activity	Switzerland
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
utslib.copyright.status	open_access	*
dc.date.updated	2023-02-10T02:02:00Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	44
utslib.citation.issue	1

Abstract:

Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.

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http://hdl.handle.net/10453/166035