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Systematic review of prediction models for delirium in the older adult inpatient

Overview of attention for article published in BMJ Open, April 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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82 tweeters

Citations

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17 Dimensions

Readers on

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84 Mendeley
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Title
Systematic review of prediction models for delirium in the older adult inpatient
Published in
BMJ Open, April 2018
DOI 10.1136/bmjopen-2017-019223
Pubmed ID
Authors

Heidi Lindroth, Lisa Bratzke, Suzanne Purvis, Roger Brown, Mark Coburn, Marko Mrkobrada, Matthew T V Chan, Daniel H J Davis, Pratik Pandharipande, Cynthia M Carlsson, Robert D Sanders

Abstract

To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population. Systematic review. PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development. age >60 years, inpatient, developed/validated a prognostic delirium prediction model. alcohol-related delirium, sample size ≤50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author. The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60 years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified. Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such models.

Twitter Demographics

The data shown below were collected from the profiles of 82 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 15 18%
Researcher 13 15%
Student > Master 13 15%
Student > Ph. D. Student 9 11%
Professor > Associate Professor 8 10%
Other 14 17%
Unknown 12 14%
Readers by discipline Count As %
Medicine and Dentistry 39 46%
Nursing and Health Professions 10 12%
Psychology 3 4%
Unspecified 3 4%
Computer Science 2 2%
Other 8 10%
Unknown 19 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 48. 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 08 March 2020.
All research outputs
#424,752
of 14,773,252 outputs
Outputs from BMJ Open
#848
of 13,553 outputs
Outputs of similar age
#15,528
of 275,227 outputs
Outputs of similar age from BMJ Open
#41
of 614 outputs
Altmetric has tracked 14,773,252 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,553 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.5. This one has done particularly well, scoring higher than 93% 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 275,227 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 94% of its contemporaries.
We're also able to compare this research output to 614 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 93% of its contemporaries.