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Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study

Overview of attention for article published in Age & Ageing, March 2018
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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)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
1 news outlet
twitter
158 tweeters

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
74 Mendeley
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Title
Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study
Published in
Age & Ageing, March 2018
DOI 10.1093/ageing/afy022
Pubmed ID
Authors

Daniel Stow, Fiona E Matthews, Stephen Barclay, Steve Iliffe, Andrew Clegg, Sarah De Biase, Louise Robinson, Barbara Hanratty

Abstract

recognising that a patient is nearing the end of life is essential, to enable professional carers to discuss prognosis and preferences for end of life care. investigate whether an electronic frailty index (eFI) generated from routinely collected data, can be used to predict mortality at an individual level. historical prospective case control study. UK primary care electronic health records. 13,149 individuals age 75 and over who died between 01/01/2015 and 01/01/2016, 1:1 matched by age and sex to individuals with no record of death in the same time period. two subsamples were randomly selected to enable development and validation of the association between eFI 3 months prior to death and mortality. Receiver operator characteristic (ROC) analyses were used to examine diagnostic accuracy of eFI at 3 months prior to death. an eFI > 0.19 predicted mortality in the development sample at 75% sensitivity and 69% area under received operating curve (AUC). In the validation dataset this cut point gave 76% sensitivity, 53% specificity. the eFI measured at a single time point has low predictive value for individual risk of death, even 3 months prior to death. Although the eFI is a strong predictor or mortality at a population level, its use for individuals is far less clear.

Twitter Demographics

The data shown below were collected from the profiles of 158 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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 19%
Researcher 13 18%
Other 8 11%
Student > Ph. D. Student 7 9%
Student > Postgraduate 4 5%
Other 11 15%
Unknown 17 23%
Readers by discipline Count As %
Medicine and Dentistry 28 38%
Computer Science 5 7%
Nursing and Health Professions 4 5%
Agricultural and Biological Sciences 3 4%
Physics and Astronomy 2 3%
Other 11 15%
Unknown 21 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 111. 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 15 February 2019.
All research outputs
#225,052
of 17,886,933 outputs
Outputs from Age & Ageing
#76
of 2,901 outputs
Outputs of similar age
#7,137
of 287,539 outputs
Outputs of similar age from Age & Ageing
#3
of 34 outputs
Altmetric has tracked 17,886,933 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 2,901 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has done particularly well, scoring higher than 97% 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 287,539 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 34 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.