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Cardiovascular risk prediction in healthy older people
Neumann JT, Thao LTP, Callander E, Chowdhury E, Williamson JD, Nelson MR, Donnan G, Woods RL et al.. GeroScience, 2021-11-11
Topic: Risk, prevention and care in population studies
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Neumann, J. T., Thao, L. T. P., Callander, E., Chowdhury, E., Williamson, J. D., Nelson, M. R., et al. (2022). Cardiovascular risk prediction in healthy older people. GeroScience. https://doi.org/10.1007/s11357-021-00486-z
Vancouver
Neumann JT, Thao LTP, Callander E, Chowdhury E, Williamson JD, Nelson MR, et al. Cardiovascular risk prediction in healthy older people. GeroScience. 2022. doi:10.1007/s11357-021-00486-z. PMID: 34762275.
BibTeX
@article{neumann2022-34762275,
author = {Neumann, J. T. and Thao, L. T. P. and Callander, E. and Chowdhury, E. and Williamson, J. D. and Nelson, M. R. and Donnan, G. and Woods, R. L. and others},
title = {{Cardiovascular risk prediction in healthy older people}},
journal = {GeroScience},
year = {2022},
doi = {10.1007/s11357-021-00486-z},
pmid = {34762275},
url = {https://doi.org/10.1007/s11357-021-00486-z}
}
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Key findings
- Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important.
Why it matters
This study developed a model to predict major adverse cardiovascular events in healthy older people, using predictors including age, sex, smoking, blood pressure, cholesterol, creatinine, diabetes, and antihypertensive use. The model was internally and externally validated in older individuals without prior cardiovascular disease events, dementia, or physical disability.
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Study details
- Study designrandomized controlled trial
- Participants18,548
- Evidence strengthHigh
How this study scored 68/100
Published in a peer-reviewed journal, and it is a randomised controlled trial. Scored zero for effect reported because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (GeroScience) |
| Journal's citation record | 1 / 4 | GeroScience: journal h-index 83 (OpenAlex, 2026-10-29) |
| Study design | 11 / 12 | A randomised controlled trial |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 4 / 4 | 18,548 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 1 / 2 | Randomised (as stated in the record) |
| Times cited | 4 / 7 | Cited 50 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.3 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 3 / 6 | Cited 4.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 38 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author John J. McNeil: h-index 91 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | The University of Melbourne: institution h-index 1,342 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 11 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 34762275) |
| Free to read | 2 / 2 | Free to read |
| Total | 68 / 100 | — |
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