Cardiovascular risk prediction in healthy older people
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 cardiova...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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SPRINGER
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/nhmrc/1136372 http://hdl.handle.net/20.500.11937/91330 |
| _version_ | 1848765509792694272 |
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| author | Neumann, J.T. Thao, L.T.P. Callander, E. Chowdhury, Enayet Williamson, J.D. Nelson, M.R. Donnan, G. Woods, R.L. Reid, Christopher Poppe, K.K. Jackson, R. Tonkin, A.M. McNeil, J.J. |
| author_facet | Neumann, J.T. Thao, L.T.P. Callander, E. Chowdhury, Enayet Williamson, J.D. Nelson, M.R. Donnan, G. Woods, R.L. Reid, Christopher Poppe, K.K. Jackson, R. Tonkin, A.M. McNeil, J.J. |
| author_sort | Neumann, J.T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:36:23Z |
| format | Journal Article |
| id | curtin-20.500.11937-91330 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:23Z |
| publishDate | 2022 |
| publisher | SPRINGER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-913302023-05-09T07:01:00Z Cardiovascular risk prediction in healthy older people Neumann, J.T. Thao, L.T.P. Callander, E. Chowdhury, Enayet Williamson, J.D. Nelson, M.R. Donnan, G. Woods, R.L. Reid, Christopher Poppe, K.K. Jackson, R. Tonkin, A.M. McNeil, J.J. Science & Technology Life Sciences & Biomedicine Geriatrics & Gerontology Risk prediction Major adverse cardiovascular event MACE Elderly Model Risk factors MYOCARDIAL-INFARCTION LDL CHOLESTEROL DISEASE PREVENTION Elderly MACE Major adverse cardiovascular event Model Risk factors Risk prediction Aged Brain Ischemia Cardiovascular Diseases Heart Disease Risk Factors Humans Risk Factors Stroke Humans Brain Ischemia Cardiovascular Diseases Risk Factors Aged Stroke Heart Disease Risk Factors 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/91330 10.1007/s11357-021-00486-z English http://purl.org/au-research/grants/nhmrc/1136372 http://purl.org/au-research/grants/nhmrc/1127060 http://purl.org/au-research/grants/nhmrc/334047 http://creativecommons.org/licenses/by/4.0/ SPRINGER fulltext |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Geriatrics & Gerontology Risk prediction Major adverse cardiovascular event MACE Elderly Model Risk factors MYOCARDIAL-INFARCTION LDL CHOLESTEROL DISEASE PREVENTION Elderly MACE Major adverse cardiovascular event Model Risk factors Risk prediction Aged Brain Ischemia Cardiovascular Diseases Heart Disease Risk Factors Humans Risk Factors Stroke Humans Brain Ischemia Cardiovascular Diseases Risk Factors Aged Stroke Heart Disease Risk Factors Neumann, J.T. Thao, L.T.P. Callander, E. Chowdhury, Enayet Williamson, J.D. Nelson, M.R. Donnan, G. Woods, R.L. Reid, Christopher Poppe, K.K. Jackson, R. Tonkin, A.M. McNeil, J.J. Cardiovascular risk prediction in healthy older people |
| title | Cardiovascular risk prediction in healthy older people |
| title_full | Cardiovascular risk prediction in healthy older people |
| title_fullStr | Cardiovascular risk prediction in healthy older people |
| title_full_unstemmed | Cardiovascular risk prediction in healthy older people |
| title_short | Cardiovascular risk prediction in healthy older people |
| title_sort | cardiovascular risk prediction in healthy older people |
| topic | Science & Technology Life Sciences & Biomedicine Geriatrics & Gerontology Risk prediction Major adverse cardiovascular event MACE Elderly Model Risk factors MYOCARDIAL-INFARCTION LDL CHOLESTEROL DISEASE PREVENTION Elderly MACE Major adverse cardiovascular event Model Risk factors Risk prediction Aged Brain Ischemia Cardiovascular Diseases Heart Disease Risk Factors Humans Risk Factors Stroke Humans Brain Ischemia Cardiovascular Diseases Risk Factors Aged Stroke Heart Disease Risk Factors |
| url | http://purl.org/au-research/grants/nhmrc/1136372 http://purl.org/au-research/grants/nhmrc/1136372 http://purl.org/au-research/grants/nhmrc/1136372 http://hdl.handle.net/20.500.11937/91330 |