The y-axis scale shown in blue indicates range from 0% to 1.2%. The graphs represent the proportion of discharges transfused with a given blood component each year. The data represent weighted estimates for 33 million to 38 million discharges each year in the National Inpatient Sample (1993-2014). The P values for trend shown were calculated by the Cochran-Armitage test for linear trend in transfusion from 2011 through 2014.
Abbreviation: APR-DRG, All Patient Refined Diagnosis Related Groups; aRR, adjusted risk-ratio; RBC, red blood cell.
a The overall weighted sample size was 36 962 415 for 2011 and 35 358 818 for 2014. The sample sizes shown for each covariate may not sum to the overall sample size due to a complete-case analytic approach.
b The multivariable model was adjusted for factors shown to have an association with transfusion between 2011-2014 in univariate analysis (sex, age group, race, APR-DRG risk severity, primary payer, transfer status, elective admission, length of stay, number of diagnoses, number of procedures, hospital bed size, hospital teaching status and location, hospital region, hospital control). There was no multicollinearity observed in the multivariable models.
cP ≤ .001.
d The race categories were predefined by the Healthcare Cost and Utilization Project (HCUP) and included race and ethnicity in one data element. If the source supplied race and ethnicity in separate data elements, ethnicity took precedence over race in setting the HCUP value for race.
eP ≤ .01.
f APR-DRG severity of illness subclass: 0, no class specified; 1, minor loss of function (includes cases with no comorbidity or complications); 2, moderate loss of function; 3, major loss of function; 4, extreme loss of function. There were 45 957 patients with an unknown APR-DRG in 2011 and 29 665 in 2014. APR-DRG subclasses 1 and 2 were considered low risk and subclass 3 and 4 were considered high risk.
gP ≤ .05.
h The hospital's ownership and control category were obtained from the American Hospital Association Annual Survey of Hospitals and included categories for government nonfederal (public), private nonprofit (voluntary), and private investor–owned (proprietary).
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Goel R, Chappidi MR, Patel EU, et al. Trends in Red Blood Cell, Plasma, and Platelet Transfusions in the United States, 1993-2014. JAMA. 2018;319(8):825–827. doi:10.1001/jama.2017.20121
Blood transfusions are one of the most common hospital procedures. Randomized trials have demonstrated the safety of restrictive transfusion strategies.1 Hospitals have subsequently implemented patient blood management programs to facilitate restrictive transfusion practices aimed to improve patient outcomes, reduce costs, and conserve blood. We are unaware of nationally representative studies evaluating temporal trends in red blood cell (RBC), plasma, and platelet transfusions while accounting for the influence of patient-level or hospital-level characteristics.
The National Inpatient Sample uses a stratified probability sample of 20% of all inpatient discharges (representing approximately 96% of the US population).2 Analyses were weighted to account for the sampling design and generate nationally representative estimates. This analysis was deemed exempt from obtaining informed consent by the Weill Cornell Medicine institutional review board.
The unit of analysis was a hospitalization. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes were used to identify transfusion procedures. The primary outcome was percentage of hospitalizations with 1 or more RBC transfusions (ICD-9-CM code, 99.04) because the majority of transfusions are RBCs; secondary outcomes included percentage of hospitalizations with 1 or more plasma (ICD-9-CM code, 99.07) and 1 or more platelet (ICD-9-CM code, 99.05) transfusions.
Transfusion trends of all 3 components were descriptively examined from 1993 to 2014. Because there was an inflection point in RBC transfusion in 2011 as determined by joinpoint analysis, the analysis focused on trends from 2011 to 2014. Multivariable Poisson regression was used to estimate adjusted risk ratios (aRRs) and 95% CIs comparing the risk of transfusion in 2011 vs 2014. Subgroup analyses were conducted to further explore trends in RBC transfusion by testing statistical interactions between time and each covariate using a design-adjusted Wald F test (P < .05). It was hypothesized a priori that decreases in RBC transfusion might vary by admission type.
A 2-sided P value less than .05 was considered significant. Data analysis was performed using Stata/MP (StataCorp), version 14.
For RBC, platelet, and plasma transfusions, the proportion of patients transfused during a hospitalization increased from 1993 to 2011 (Figure).
RBC transfusions decreased from 6.8% (95% CI, 6.4%-7.2%) in 2011 to 5.7% (95% CI, 5.6%-5.9%) in 2014 (aRR, 0.83 [95% CI, 0.78-0.88]). Plasma transfusions decreased from 1.0% (95% CI, 0.93%-1.1%) in 2011 to 0.87% (95% CI, 0.83%-0.91%) in 2014 (aRR, 0.87 [95% CI, 0.80-0.95]). Platelet transfusions remained stable between 2011 and 2014 (aRR, 0.99 [95% CI, 0.89-1.10]).
From 2011 to 2014, statistically significant reductions in RBC transfusions were seen among all sexes, race/ethnicities, patient risk severities, payer types, and admission types (Table). No statistically significant reductions in RBC transfusions were seen in children (aged <18 years) or private investor–owned hospitals. Significant interactions were observed for time and all covariates (P for interaction < .05). A significantly greater decrease in RBC transfusions was seen for elective admissions (aRR, 0.74 [95% CI, 0.67-0.80]) compared with nonelective admissions (aRR, 0.86 [95% CI, 0.81-0.91]; P for interaction < .001).
The observed decreases in RBC and plasma transfusions from 2011 to 2014 may reflect evidence demonstrating the safety of restricting RBC transfusions, patient blood management programs, conservation initiatives (eg, cell salvage, pharmacotherapy, improved surgical techniques), advocacy from medical organizations, and publication of transfusion guidelines.1 No decrease in RBC transfusion was seen in children or platelet transfusion overall, areas for which there is limited evidence to guide clinical practice.1,3
This study has limitations inherent to any retrospective analysis of administrative data. The ICD-9-CM coding is carried out primarily for billing purposes and it is not possible to verify its accuracy, but National Inpatient Sample coding has been validated in other studies. The laboratory data supporting indication for transfusion was unknown. This study was also limited to inpatient transfusions, which might not be generalizable to outpatient settings. Except for the a priori hypothesis for admission type, subgroup analyses were not prespecified and significant interactions should be considered exploratory and tentative.
These data confirm and build upon previous descriptive studies. A statistical brief suggested RBC transfusions may be declining in the United States, but this study excluded children and did not examine trends in plasma or platelet transfusion.2 Preliminary data by the AABB (formerly the American Association of Blood Banks) and the US Centers for Disease Control and Prevention that focused on number of units of blood collected also suggested a decrease in the total number of RBC units transfused that may have begun as early as 2008.4-6 However, in this study the percentage of hospitalized patients receiving RBC transfusions did not decrease until 2011.
Accepted for Publication: December 1, 2017.
Corresponding Author: Aaron Tobian, MD, PhD, Department of Pathology, Johns Hopkins University, Carnegie 437, 600 N Wolfe St, Baltimore, MD 21287 (firstname.lastname@example.org).
Author Contributions: Drs Goel and Tobian had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Goel, Chappidi, Patel, Ness, Frank, Tobian.
Acquisition, analysis, or interpretation of data: Goel, Chappidi, Patel, Cushing, Frank, Tobian.
Drafting of the manuscript: Goel, Chappidi, Patel, Tobian.
Critical revision of the manuscript for important intellectual content: Goel, Chappidi, Patel, Ness, Cushing, Frank.
Statistical analysis: Goel, Chappidi, Patel.
Obtained funding: Tobian.
Administrative, technical, or material support: Ness, Frank, Tobian.
Supervision: Ness, Tobian.
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Ness reported receiving personal fees from Terumo BCT and Haemonetics. Dr Cushing reported receiving personal fees from Octapharma. No other disclosures were reported.
Funding/Support: This work was supported in part by grants NIH 5R01AI120938-02 and 1R01AI128779-01 from the National Institutes of Health (Dr Tobian) and from Weill Cornell Medical College (Dr Goel).
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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