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Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data

Overview of attention for article published in BMC Medicine, January 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

news
9 news outlets
policy
1 policy source
twitter
14 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
136 Mendeley
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Title
Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data
Published in
BMC Medicine, January 2016
DOI 10.1186/s12916-016-0549-y
Pubmed ID
Authors

K. Walters, S. Hardoon, I. Petersen, S. Iliffe, R. Z. Omar, I. Nazareth, G. Rait

Abstract

Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Dementia incidence was 1.88 (95 % CI, 1.83-1.93) and 16.53 (95 % CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95 % CI, 1.95-2.11), C index 0.84 (95 % CI, 0.81-0.87), and calibration slope 0.98 (95 % CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in 'ruling out' those at very low risk from further testing or intensive preventative activities.

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Italy 1 <1%
Japan 1 <1%
Canada 1 <1%
Unknown 130 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 20%
Researcher 22 16%
Student > Master 22 16%
Unspecified 13 10%
Other 11 8%
Other 41 30%
Readers by discipline Count As %
Medicine and Dentistry 45 33%
Unspecified 25 18%
Psychology 21 15%
Social Sciences 8 6%
Nursing and Health Professions 7 5%
Other 30 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 81. 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 04 November 2019.
All research outputs
#214,355
of 13,763,705 outputs
Outputs from BMC Medicine
#188
of 2,168 outputs
Outputs of similar age
#7,271
of 336,556 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 13,763,705 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,168 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.2. This one has done particularly well, scoring higher than 91% 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 336,556 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them