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Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics

Overview of attention for article published in BMC Cardiovascular Disorders, January 2017
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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1 policy source
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Title
Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics
Published in
BMC Cardiovascular Disorders, January 2017
DOI 10.1186/s12872-016-0462-5
Pubmed ID
Authors

Kathryn Backholer, Yoichiro Hirakawa, Andrew Tonkin, Graham Giles, Dianna J. Magliano, Stephen Colagiuri, Mark Harris, Paul Mitchell, Mark Nelson, Jonathan E. Shaw, David Simmons, Leon Simons, Anne Taylor, Jessica Harding, Bamini Gopinath, Mark Woodward

Abstract

To develop and recalibrate an Australian 5-year cardiovascular disease (CVD) mortality risk score to produce contemporary predictions of risk. Data were pooled from six Australian cohort studies (n = 54,829), with baseline data collected between 1989 and 2003. Participants included were aged 40-74 years and free of CVD at baseline. Variables were harmonised across studies and missing data were imputed using multiple imputation. Cox proportional hazards models were used to estimate the risk of CVD mortality associated with factors mutually independently predictive (p < 0.05) and a 5-year risk prediction algorithm was constructed. This algorithm was recalibrated to reflect contemporary national levels of CVD mortality and risk factors using national statistics. Over a mean 16.6 years follow-up, 1375 participants in the six studies died from CVD. The prediction model included age, sex, smoking, diabetes, systolic blood pressure, total and high-density lipoprotein cholesterol (HDLC), a social deprivation score, estimated glomerular filtration rate and its square and interactions of sex with diabetes, HDLC and deprivation score, and of age with systolic blood pressure and smoking. This model discriminated well when applied to a Scottish study population (c-statistic (95% confidence interval): 0.751 (0.709, 0.793)). Recalibration generally increased estimated risks, but well below those predicted by the European SCORE models. The resulting risk score, which includes markers of both chronic kidney disease and socioeconomic deprivation, is the first CVD mortality risk prediction tool for Australia to be derived using Australian data. The primary model, and the method of recalibration, is applicable elsewhere.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 14%
Student > Bachelor 7 14%
Researcher 6 12%
Student > Master 5 10%
Other 3 6%
Other 7 14%
Unknown 15 30%
Readers by discipline Count As %
Medicine and Dentistry 11 22%
Nursing and Health Professions 7 14%
Computer Science 3 6%
Biochemistry, Genetics and Molecular Biology 3 6%
Mathematics 2 4%
Other 6 12%
Unknown 18 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 January 2017.
All research outputs
#6,670,314
of 24,787,209 outputs
Outputs from BMC Cardiovascular Disorders
#330
of 1,856 outputs
Outputs of similar age
#116,403
of 430,884 outputs
Outputs of similar age from BMC Cardiovascular Disorders
#12
of 42 outputs
Altmetric has tracked 24,787,209 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,856 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 430,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.