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A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit

Overview of attention for article published in Critical Care, November 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)

Mentioned by

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19 tweeters

Citations

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13 Dimensions

Readers on

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63 Mendeley
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Title
A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
Published in
Critical Care, November 2017
DOI 10.1186/s13054-017-1874-z
Pubmed ID
Authors

Cristhian Potes, Bryan Conroy, Minnan Xu-Wilson, Christopher Newth, David Inwald, Joseph Frassica

Abstract

Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration. The model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom. The PICU clinical information system database (Intellivue Clinical Information Portfolio, Philips, UK) was interrogated to collect physiological and laboratory data. The model was trained using a variant of AdaBoost, which learned a set of low-dimensional classifiers, each of which was age adjusted. A total of 7052 patients admitted to the US PICU was used for training the model, and a total of 970 patients admitted to the UK PICU was used for validation. On the training/validation datasets, the model showed better prediction of hemodynamic intervention (area under the receiver operating characteristic (AUROC) = 0.81/0.81) than systolic blood pressure-based (AUCROC = 0.58/0.67) or shock index-based (AUCROC = 0.63/0.65) models. Both of these models were age adjusted using the same classifier. The proposed model reliably predicted the need for hemodynamic intervention in PICU patients and provides better classification performance when compared to systolic blood pressure-based or shock index-based models alone. This model could readily be built into a clinical information system to identify patients at risk of hemodynamic instability.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 17%
Other 8 13%
Student > Master 6 10%
Student > Ph. D. Student 5 8%
Student > Bachelor 4 6%
Other 10 16%
Unknown 19 30%
Readers by discipline Count As %
Medicine and Dentistry 17 27%
Nursing and Health Professions 6 10%
Engineering 6 10%
Computer Science 4 6%
Psychology 2 3%
Other 6 10%
Unknown 22 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 10 December 2017.
All research outputs
#2,693,347
of 21,323,497 outputs
Outputs from Critical Care
#2,220
of 5,801 outputs
Outputs of similar age
#73,702
of 445,316 outputs
Outputs of similar age from Critical Care
#166
of 219 outputs
Altmetric has tracked 21,323,497 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,801 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has gotten more attention than average, scoring higher than 61% 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 445,316 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 219 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.