title: A systematic review of validated methods for identifying transfusion-related sepsis using administrative and claims data.
abstract: OBJECTIVE
  To systematically review algorithms to identify transfusion-related sepsis or septicemia in administrative data, with a focus on studies that have examined the validity of the algorithms.
  
  
  METHODS
  A literature search was conducted using PubMed, the database of the Iowa Drug Information Service (IDIS/Web), and Embase. A Google Scholar search was conducted because of difficulty identifying relevant studies. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada, because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources.
  
  
  RESULTS
  No studies that were identified that used administrative data to identify sepsis or septicemia related to transfusion of blood products. Thus, four studies that studied the validity of algorithms to identify sepsis and two that studied algorithms to identify allogeneic blood transfusion are described in this review. Two studies found acceptable positive predictive values of 80% and 89% for algorithms to identify sepsis in hospitalized patients. One study reported a negative predictive value of 80% in hospitalized patients, and another, a sensitivity of 75%. One study of veterans receiving surgery reported much worse performance characteristics. Two studies reported near-perfect specificity of codes for allogeneic red blood cell transfusion, but sensitivity ranged from 21% to 83%.
  
  
  CONCLUSIONS
  There is no information to assess the validity of algorithms to identify transfusion-related sepsis or septicemia. Codes to identify sepsis performed well in most studies. Algorithms to identify transfusions need further research that includes a broader range of transfusion types.
authors.affiliation: The University of Iowa College of Public Health, Department of Epidemiology, Iowa City, IA 52242, USA. ryan-carnahan@uiowa.edu
authors.forename: Ryan M
authors.initials: RM
authors.lastname: Carnahan
authors.forename: Ronald A
authors.initials: RA
authors.lastname: Herman
authors.forename: Kevin G
authors.initials: KG
authors.lastname: Moores
mesh_terms: D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology; D065227:Transfusion Reaction; D014481:United States / Q000453:epidemiology; D054928:Validation Studies as Topic