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Body surface area: a predictor of response to red blood cell transfusion

Overview of attention for article published in Journal of Blood Medicine, September 2016
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Title
Body surface area: a predictor of response to red blood cell transfusion
Published in
Journal of Blood Medicine, September 2016
DOI 10.2147/jbm.s105063
Pubmed ID
Authors

Louise Man, H. Raymond Tahhan

Abstract

A current focus of transfusion medicine is a judicious strategy in transfusion of blood products. Unfortunately, our ability to predict hemoglobin (Hgb) response to transfusion has been limited. The objective of this study was to determine variability of response to red blood cell transfusion and to predict which patients will have an Hgb rise higher or lower than that predicted by the long-standing convention of "one and three". This was a retrospective chart review in a single hospital. Data for 167 consecutive patient encounters were reviewed. The dataset was randomly divided into derivation and validation subsets with no significant differences in characteristics. DeltaHgb was defined as posttransfusion Hgb minus pre-transfusion Hgb per red blood cell unit. We classified all the patients in both the subsets as "high responders" (DeltaHgb >1 g/dL) or as "low responders" (DeltaHgb ≤1 g/dL). In univariate analysis, age, sex, body weight, estimated blood volume, and body surface area were significantly associated with response category (P<0.05). Different multivariate regression models were tested using the derivation subset. The probability of being a high responder was best calculated using the logarithmic formula e(H) / (1 + e(H)), where H is B0 + (B1 × variable 1) + (B2 × variable 2). Bis are coefficients of the models. On validation, the model H=6.5-(3.3 × body surface area), with the cutoff probability of 0.5, was found to correctly classify patients into high and low responders in 69% of cases (sensitivity 84.6%, specificity 43.8%). This model may equip clinicians to make more appropriate transfusion decisions and serve as a springboard for further research in transfusion medicine.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 2 29%
Student > Ph. D. Student 2 29%
Professor 1 14%
Researcher 1 14%
Unspecified 1 14%
Other 0 0%
Readers by discipline Count As %
Medicine and Dentistry 4 57%
Agricultural and Biological Sciences 1 14%
Unspecified 1 14%
Unknown 1 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 September 2016.
All research outputs
#7,297,864
of 8,446,126 outputs
Outputs from Journal of Blood Medicine
#68
of 93 outputs
Outputs of similar age
#207,375
of 252,951 outputs
Outputs of similar age from Journal of Blood Medicine
#2
of 3 outputs
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So far Altmetric has tracked 93 research outputs from this source. They receive a mean Attention Score of 2.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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