Evidence map›Paper›PMID 24884605›Full record

ArticleBMC health services research2014

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

Nareg H Roubinian, Edward L Murphy, Bix E Swain, Marla N Gardner, Vincent Liu, Gabriel J Escobar, NHLBI Recipient Epidemiology and Donor Evaluation Study-III (REDS-III), Northern California Kaiser Permanente DOR Systems Research Initiative

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMC health services research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03891303 (Allogenic Blood Transfusion During Elective Open Abdominal Aortic Surgery and Its Predictors), which is not on this map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
1.5field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03891303 completednot on this map

Allogenic Blood Transfusion During Elective Open Abdominal Aortic Surgery and Its Predictors: a Retrospective Database Study at a Tertiary Hospital in Croatia

TypeobservationalSponsorClinic for Cardiovascular Diseases MagdalenaRan2011 to 2018Enrolled426ConditionsBlood TransfusionArmsAllogenic blood transfusion (ABT)
3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
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  9. Preoperative anemia is a risk factor for poor perioperative outcomes in ventral hernia repair.Hernia : the journal of hernias and abdominal wall surgery · 2022
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  11. Blood transfusion trends by disease category in the United States, 2000 to 2014.Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Nareg H RoubinianBlood Systems Research Institute, 270 Masonic Avenue, San Francisco, CA 94118, USA. nroubinian@bloodsystems.org.
Edward L Murphy
Bix E Swain
Marla N Gardner
Vincent Liu
Gabriel J Escobar
NHLBI Recipient Epidemiology and Donor Evaluation Study-III (REDS-III)
Northern California Kaiser Permanente DOR Systems Research Initiative
Kaiser Permanente · USUniversity of California, San Francisco · US

Funding

Health Outcomes in Blood Donors and RecipientsK24HL075036 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MURPHY, EDWARD L · 2004 to 2013
$1.6M
NHLBI NIH HHS K24-HL-75036PHS HHS HHSN2682011000005IPHS HHS HHSN268201100004I
6 · The paper itself

Abstract

backgroundRandomized 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.

methodsWe 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.

resultsThe 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.

conclusionsData 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.

Indexed as

ComorbidityErythrocyte TransfusionHospitalizationPredictive Value of TestsSeverity of Illness IndexAgedAged, 80 and overElectronic Health RecordsFemaleHemoglobinsHumansIntensive Care UnitsMaleMiddle AgedRetrospective StudiesHemoglobins

Identifiers

PMID24884605
PMCPMC4101854
OpenAlexW2050837299

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.