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Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models

Overview of attention for article published in BMC Medicine, March 2017
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

news
6 news outlets
policy
1 policy source
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5 X users
facebook
3 Facebook pages

Citations

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42 Dimensions

Readers on

mendeley
133 Mendeley
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Title
Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models
Published in
BMC Medicine, March 2017
DOI 10.1186/s12916-017-0827-3
Pubmed ID
Authors

Shakila Thangaratinam, John Allotey, Nadine Marlin, Julie Dodds, Fiona Cheong-See, Peter von Dadelszen, Wessel Ganzevoort, Joost Akkermans, Sally Kerry, Ben W. Mol, Karl G. M. Moons, Richard D. Riley, Khalid S. Khan, for the PREP Collaborative Network

Abstract

Unexpected clinical deterioration before 34 weeks gestation is an undesired course in early-onset pre-eclampsia. To safely prolong preterm gestation, accurate and timely prediction of complications is required. Women with confirmed early onset pre-eclampsia were recruited from 53 maternity units in the UK to a large prospective cohort study (PREP-946) for development of prognostic models for the overall risk of experiencing a complication using logistic regression (PREP-L), and for predicting the time to adverse maternal outcome using a survival model (PREP-S). External validation of the models were carried out in a multinational cohort (PIERS-634) and another cohort from the Netherlands (PETRA-216). Main outcome measures were C-statistics to summarise discrimination of the models and calibration plots and calibration slopes. A total of 169 mothers (18%) in the PREP dataset had adverse outcomes by 48 hours, and 633 (67%) by discharge. The C-statistics of the models for predicting complications by 48 hours and by discharge were 0.84 (95% CI, 0.81-0.87; PREP-S) and 0.82 (0.80-0.84; PREP-L), respectively. The PREP-S model included maternal age, gestation, medical history, systolic blood pressure, deep tendon reflexes, urine protein creatinine ratio, platelets, serum alanine amino transaminase, urea, creatinine, oxygen saturation and treatment with antihypertensives or magnesium sulfate. The PREP-L model included the above except deep tendon reflexes, serum alanine amino transaminase and creatinine. On validation in the external PIERS dataset, the reduced PREP-S model showed reasonable calibration (slope 0.80) and discrimination (C-statistic 0.75) for predicting adverse outcome by 48 hours. Reduced PREP-L model showed excellent calibration (slope: 0.93 PIERS, 0.90 PETRA) and discrimination (0.81 PIERS, 0.75 PETRA) for predicting risk by discharge in the two external datasets. PREP models can be used to obtain predictions of adverse maternal outcome risk, including early preterm delivery, by 48 hours (PREP-S) and by discharge (PREP-L), in women with early onset pre-eclampsia in the context of current care. They have a potential role in triaging high-risk mothers who may need transfer to tertiary units for intensive maternal and neonatal care. ISRCTN40384046 , retrospectively registered.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 <1%
Unknown 132 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 23 17%
Researcher 15 11%
Student > Master 14 11%
Student > Postgraduate 12 9%
Student > Ph. D. Student 10 8%
Other 22 17%
Unknown 37 28%
Readers by discipline Count As %
Medicine and Dentistry 55 41%
Nursing and Health Professions 19 14%
Biochemistry, Genetics and Molecular Biology 2 2%
Arts and Humanities 2 2%
Agricultural and Biological Sciences 2 2%
Other 9 7%
Unknown 44 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 58. 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 25 June 2019.
All research outputs
#628,170
of 22,961,203 outputs
Outputs from BMC Medicine
#455
of 3,448 outputs
Outputs of similar age
#14,508
of 308,953 outputs
Outputs of similar age from BMC Medicine
#10
of 65 outputs
Altmetric has tracked 22,961,203 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 3,448 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one has done well, scoring higher than 86% 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 308,953 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 95% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.