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dc.contributor.authorNeumann, J.T.
dc.contributor.authorThao, L.T.P.
dc.contributor.authorCallander, E.
dc.contributor.authorChowdhury, Enayet
dc.contributor.authorWilliamson, J.D.
dc.contributor.authorNelson, M.R.
dc.contributor.authorDonnan, G.
dc.contributor.authorWoods, R.L.
dc.contributor.authorReid, Christopher
dc.contributor.authorPoppe, K.K.
dc.contributor.authorJackson, R.
dc.contributor.authorTonkin, A.M.
dc.contributor.authorMcNeil, J.J.
dc.date.accessioned2023-04-05T04:51:59Z
dc.date.available2023-04-05T04:51:59Z
dc.date.issued2022
dc.identifier.citationNeumann, J.T. and Thao, L.T.P. and Callander, E. and Chowdhury, E. and Williamson, J.D. and Nelson, M.R. and Donnan, G. et al. 2022. Cardiovascular risk prediction in healthy older people. GeroScience. 44 (1): pp. 403-413.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/91330
dc.identifier.doi10.1007/s11357-021-00486-z
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.languageEnglish
dc.publisherSPRINGER
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/nhmrc/1136372
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/nhmrc/1127060
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/nhmrc/334047
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectGeriatrics & Gerontology
dc.subjectRisk prediction
dc.subjectMajor adverse cardiovascular event
dc.subjectMACE
dc.subjectElderly
dc.subjectModel
dc.subjectRisk factors
dc.subjectMYOCARDIAL-INFARCTION
dc.subjectLDL CHOLESTEROL
dc.subjectDISEASE
dc.subjectPREVENTION
dc.subjectElderly
dc.subjectMACE
dc.subjectMajor adverse cardiovascular event
dc.subjectModel
dc.subjectRisk factors
dc.subjectRisk prediction
dc.subjectAged
dc.subjectBrain Ischemia
dc.subjectCardiovascular Diseases
dc.subjectHeart Disease Risk Factors
dc.subjectHumans
dc.subjectRisk Factors
dc.subjectStroke
dc.subjectHumans
dc.subjectBrain Ischemia
dc.subjectCardiovascular Diseases
dc.subjectRisk Factors
dc.subjectAged
dc.subjectStroke
dc.subjectHeart Disease Risk Factors
dc.titleCardiovascular risk prediction in healthy older people
dc.typeJournal Article
dcterms.source.volume44
dcterms.source.number1
dcterms.source.startPage403
dcterms.source.endPage413
dcterms.source.issn2509-2715
dcterms.source.titleGeroScience
dc.date.updated2023-04-05T04:51:53Z
curtin.departmentCurtin School of Population Health
curtin.accessStatusOpen access
curtin.facultyFaculty of Health Sciences
curtin.contributor.orcidChowdhury, Enayet [0000-0002-9709-794X]
curtin.contributor.orcidReid, Christopher [0000-0001-9173-3944]
curtin.contributor.researcheridChowdhury, Enayet [I-1267-2019]
dcterms.source.eissn2509-2723
curtin.contributor.scopusauthoridChowdhury, Enayet [35278162800]
curtin.repositoryagreementV3


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