Open Access Research article

Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity

Nareg H Roubinian123*, Edward L Murphy13, Bix E Swain2, Marla N Gardner2, Vincent Liu2, Gabriel J Escobar2 and the NHLBI Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) and the Northern California Kaiser Permanente DOR Systems Research Initiative

Author Affiliations

1 Blood Systems Research Institute, 270 Masonic Avenue, San Francisco, CA 94118, USA

2 Division of Research, Kaiser Permanente Northern California, 2000 Broadway Avenue, Oakland, CA 94612, USA

3 Department of Laboratory Medicine, University of California, 270 Masonic Avenue, San Francisco, CA 94118, USA

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BMC Health Services Research 2014, 14:213  doi:10.1186/1472-6963-14-213

Published: 10 May 2014

Abstract

Background

Randomized controlled trial evidence supports a restrictive strategy of red blood cell (RBC) transfusion, but significant variation in clinical transfusion practice persists. Patient characteristics other than hemoglobin levels may influence the decision to transfuse RBCs and explain some of this variation. Our objective was to evaluate the role of patient comorbidities and severity of illness in predicting inpatient red blood cell transfusion events.

Methods

We developed a predictive model of inpatient RBC transfusion using comprehensive electronic medical record (EMR) data from 21 hospitals over a four year period (2008-2011). Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization. Model predictors included administrative data (age, sex, comorbid conditions, admission type, and admission diagnosis), admission hemoglobin, severity of illness, prior inpatient RBC transfusion, admission ward, and hospital.

Results

The study cohort included 275,874 patients who experienced 444,969 hospitalizations. The 24 hour and overall inpatient RBC transfusion rates were 7.2% and 13.9%, respectively. A predictive model for transfusion within 24 hours of hospital admission had a C-statistic of 0.928 and pseudo-R2 of 0.542; corresponding values for the model examining transfusion through the entire hospitalization were 0.872 and 0.437. Inclusion of the admission hemoglobin resulted in the greatest improvement in model performance relative to patient comorbidities and severity of illness.

Conclusions

Data from electronic medical records at the time of admission predicts with very high likelihood the incidence of red blood transfusion events in the first 24 hours and throughout hospitalization. Patient comorbidities and severity of illness on admission play a small role in predicting the likelihood of RBC transfusion relative to the admission hemoglobin.