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Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP)

Overview of attention for article published in Hypertension, May 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)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
9 news outlets
twitter
20 tweeters
facebook
2 Facebook pages
wikipedia
1 Wikipedia page

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
57 Mendeley
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Title
Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP)
Published in
Hypertension, May 2016
DOI 10.1161/hypertensionaha.115.07108
Pubmed ID
Authors

James P. Sheppard, Richard Stevens, Paramjit Gill, Una Martin, Marshall Godwin, Janet Hanley, Carl Heneghan, F.D. Richard Hobbs, Jonathan Mant, Brian McKinstry, Martin Myers, David Nunan, Alison Ward, Bryan Williams, Richard J. McManus

Abstract

Patients often have lower (white coat effect) or higher (masked effect) ambulatory/home blood pressure readings compared with clinic measurements, resulting in misdiagnosis of hypertension. The present study assessed whether blood pressure and patient characteristics from a single clinic visit can accurately predict the difference between ambulatory/home and clinic blood pressure readings (the home-clinic difference). A linear regression model predicting the home-clinic blood pressure difference was derived in 2 data sets measuring automated clinic and ambulatory/home blood pressure (n=991) using candidate predictors identified from a literature review. The model was validated in 4 further data sets (n=1172) using area under the receiver operator characteristic curve analysis. A masked effect was associated with male sex, a positive clinic blood pressure change (difference between consecutive measurements during a single visit), and a diagnosis of hypertension. Increasing age, clinic blood pressure level, and pulse pressure were associated with a white coat effect. The model showed good calibration across data sets (Pearson correlation, 0.48-0.80) and performed well-predicting ambulatory hypertension (area under the receiver operator characteristic curve, 0.75; 95% confidence interval, 0.72-0.79 [systolic]; 0.87; 0.85-0.89 [diastolic]). Used as a triaging tool for ambulatory monitoring, the model improved classification of a patient's blood pressure status compared with other guideline recommended approaches (93% [92% to 95%] classified correctly; United States, 73% [70% to 75%]; Canada, 74% [71% to 77%]; United Kingdom, 78% [76% to 81%]). This study demonstrates that patient characteristics from a single clinic visit can accurately predict a patient's ambulatory blood pressure. Usage of this prediction tool for triaging of ambulatory monitoring could result in more accurate diagnosis of hypertension and hence more appropriate treatment.

Twitter Demographics

The data shown below were collected from the profiles of 20 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Croatia 1 2%
Kenya 1 2%
Switzerland 1 2%
Brazil 1 2%
Unknown 53 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 23%
Student > Ph. D. Student 10 18%
Researcher 8 14%
Other 5 9%
Student > Bachelor 4 7%
Other 15 26%
Unknown 2 4%
Readers by discipline Count As %
Medicine and Dentistry 30 53%
Nursing and Health Professions 8 14%
Sports and Recreations 2 4%
Computer Science 2 4%
Psychology 1 2%
Other 5 9%
Unknown 9 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 88. 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 11 May 2020.
All research outputs
#241,744
of 15,629,104 outputs
Outputs from Hypertension
#120
of 5,502 outputs
Outputs of similar age
#7,169
of 267,268 outputs
Outputs of similar age from Hypertension
#2
of 76 outputs
Altmetric has tracked 15,629,104 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 5,502 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. 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 267,268 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 76 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 97% of its contemporaries.