title: Identifying major hemorrhage with automated data: results of the Veterans Affairs study to improve anticoagulation (VARIA).
abstract: BACKGROUND
  Identifying major bleeding is fundamental to assessing the outcomes of anticoagulation therapy. This drives the need for a credible implementation in automated data for the International Society of Thrombosis and Haemostasis (ISTH) definition of major bleeding.
  
  
  METHODS
  We studied 102,395 patients who received 158,511 person-years of warfarin treatment from the Veterans Health Administration (VA) between 10/1/06-9/30/08. We constructed a list of ICD-9-CM codes of "candidate" bleeding events. Each candidate event was identified as a major hemorrhage if it fulfilled one of four criteria: 1) associated with death within 30days; 2) bleeding in a critical anatomic site; 3) associated with a transfusion; or 4) was coded as the event that precipitated or was responsible for the majority of an inpatient hospitalization.
  
  
  RESULTS
  This definition classified 11,240 (15.8%) of 71, 338 candidate events as major hemorrhage. Typically, events more likely to be severe were retained at higher rates than those less likely to be severe. For example, Diverticula of Colon with Hemorrhage (562.12) and Hematuria (599.7) were retained 46% and 4% of the time, respectively. Major, intracranial, and fatal hemorrhage were identified at rates comparable to those found in randomized clinical trials however, higher than those reported in observational studies: 4.73, 1.29, and 0.41 per 100 patient years, respectively.
  
  
  CONCLUSIONS
  We describe here a workable definition for identifying major hemorrhagic events from large automated datasets. This method of identifying major bleeding may have applications for quality measurement, quality improvement, and comparative effectiveness research.
authors.affiliation: Center for Health Quality, Outcomes, and Economic Research, Bedford VA Medical Center, Bedford, MA, USA. guneetk@bu.edu
authors.forename: Guneet K
authors.initials: GK
authors.lastname: Jasuja
authors.forename: Joel I
authors.initials: JI
authors.lastname: Reisman
authors.forename: Donald R
authors.initials: DR
authors.lastname: Miller
authors.forename: Dan R
authors.initials: DR
authors.lastname: Berlowitz
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Hylek
authors.forename: Arlene S
authors.initials: AS
authors.lastname: Ash
authors.forename: Al
authors.initials: A
authors.lastname: Ozonoff
authors.forename: Shibei
authors.initials: S
authors.lastname: Zhao
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Rose
mesh_terms: D000368:Aged; D000925:Anticoagulants / Q000009:adverse effects*; D001331:Automation; D001803:Blood Transfusion; D057225:Data Mining; D005260:Female; D006470:Hemorrhage / Q000139:chemically induced* / Q000401:mortality / Q000628:therapy; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D009626:Terminology as Topic; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs; D014859:Warfarin / Q000009:adverse effects*
chemical_list: D000925:Anticoagulants; D014859:Warfarin