title: Trends in red blood cell transfusion and 30-day mortality among hospitalized patients.
abstract: BACKGROUND
  Blood conservation strategies have been shown to be effective in decreasing red blood cell (RBC) utilization in specific patient groups. However, few data exist describing the extent of RBC transfusion reduction or their impact on transfusion practice and mortality in a diverse inpatient population.
  
  
  METHODS
  We conducted a retrospective cohort study using comprehensive electronic medical record data from 21 medical facilities in Kaiser Permanente Northern California. We examined unadjusted and risk-adjusted RBC transfusion and 30-day mortality coincident with implementation of RBC conservation strategies.
  
  
  RESULTS
  The inpatient study cohort included 391,958 patients who experienced 685,753 hospitalizations. From 2009 to 2013, the incidence of RBC transfusion decreased from 14.0% to 10.8% of hospitalizations; this change coincided with a decline in pretransfusion hemoglobin (Hb) levels from 8.1 to 7.6 g/dL. Decreased RBC utilization affected broad groups of admission diagnoses and was most pronounced in patients with a nadir Hb level between 8 and 9 g/dL (n = 73,057; 50.8% to 19.3%). During the study period, the standard deviation of risk-adjusted RBC transfusion incidence across hospitals decreased by 44% (p < 0.001). Thirty-day mortality did not change significantly with declines in RBC utilization in patient groups previously studied in clinical trials nor in other subgroups.
  
  
  CONCLUSIONS
  After the implementation of blood conservation strategies, RBC transfusion incidence and pretransfusion Hb levels decreased broadly across medical and surgical patients. Variation in RBC transfusion incidence across hospitals decreased from 2010 to 2013. Consistent with clinical trial data, more restrictive transfusion practice did not appear to impact 30-day mortality.
authors.affiliation: Blood Systems Research Institute, San Francisco, California; Kaiser Permanente Northern California Division of Research, Oakland, California; University of California at San Francisco, San Francisco, California.
authors.forename: Nareg H
authors.initials: NH
authors.lastname: Roubinian
authors.forename: Gabriel J
authors.initials: GJ
authors.lastname: Escobar
authors.forename: Vincent
authors.initials: V
authors.lastname: Liu
authors.forename: Bix E
authors.initials: BE
authors.lastname: Swain
authors.forename: Marla N
authors.initials: MN
authors.lastname: Gardner
authors.forename: Patricia
authors.initials: P
authors.lastname: Kipnis
authors.forename: Darrell J
authors.initials: DJ
authors.lastname: Triulzi
authors.forename: Jerome L
authors.initials: JL
authors.lastname: Gottschall
authors.forename: Yan
authors.initials: Y
authors.lastname: Wu
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Carson
authors.forename: Steven H
authors.initials: SH
authors.lastname: Kleinman
authors.forename: Edward L
authors.initials: EL
authors.lastname: Murphy
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D057668:Bloodless Medical and Surgical Procedures / Q000706:statistics & numerical data; D015897:Comorbidity; D017707:Erythrocyte Transfusion / Q000706:statistics & numerical data* / Q000639:trends*; D005260:Female; D006454:Hemoglobins; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D007297:Inpatients / Q000706:statistics & numerical data; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008329:Managed Care Programs / Q000706:statistics & numerical data*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D020379:Risk Adjustment
chemical_list: D006454:Hemoglobins