title: Incremental predictive value of red cell distribution width for 12-month clinical outcome after acute myocardial infarction.
abstract: BACKGROUND
  The incremental predictive value of red cell distribution width (RDW) for major adverse cardiac events (MACEs) has not been fully investigated in patients with acute myocardial infarction (AMI).
  
  
  OBJECTIVE
  The aim of this study was to determine the incremental value of RDW to the established risk factors in predicting clinical outcomes after AMI.
  
  
  METHODS
  Between November 2005 and January 2010, 1596 patients with AMI (1070 male; mean age, 64.5 ± 11.9 years) were analyzed in this study. Baseline levels of RDW were measured at the time of admission. The 12-month MACEs were defined as death and nonfatal MI.
  
  
  RESULTS
  The RDW levels were significantly higher in patients with 12-month MACEs (13.8 ± 1.3% vs 13.3 ± 1.2%, P < 0.001). In a Cox proportional hazards model, RDW (hazard ratio [HR]: 1.19, P = 0.016) was an independent predictor for 12-month MACEs. Adding RDW to established risk factors and hemoglobin levels significantly improved prediction for 12-month MACEs, as shown by the net reclassification improvement (0.297; P = 0.012) and integrated discrimination improvement (0.0143; P = 0.042). The likelihood ratio test showed that RDW added incremental predictive value to the combination of hemoglobin and established risk factors (P = 0.005). Patients were categorized into 4 groups according to quartiles of RDW at baseline. Adjusted HRs for 12-month MACEs were 1 (RDW ≤12.6%, reference), 4.24 (RDW 12.7%-13.1%, P = 0.01), 4.36 (RDW 13.2%-13.9%, P = 0.008), and 6.18 (RDW 13.2%-13.9%, P = 0.001), respectively.
  
  
  CONCLUSIONS
  In post-myocardial infarction patients, baseline RDW levels at admission could provide incremental predictive value to established risk factors for predicting 12-month MACEs.
authors.affiliation: Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
authors.forename: Jang Hoon
authors.initials: JH
authors.lastname: Lee
authors.forename: Dong Heon
authors.initials: DH
authors.lastname: Yang
authors.forename: Se Yong
authors.initials: SY
authors.lastname: Jang
authors.forename: Won Suk
authors.initials: WS
authors.lastname: Choi
authors.forename: Kyun Hee
authors.initials: KH
authors.lastname: Kim
authors.forename: Won Kee
authors.initials: WK
authors.lastname: Lee
authors.forename: Myung Hwan
authors.initials: MH
authors.lastname: Bae
authors.forename: Hun Sik
authors.initials: HS
authors.lastname: Park
authors.forename: Yongkeun
authors.initials: Y
authors.lastname: Cho
authors.forename: Shung Chull
authors.initials: SC
authors.lastname: Chae
mesh_terms: D000368:Aged; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D004909:Erythrocyte Indices; D005260:Female; D006454:Hemoglobins / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000097:blood* / Q000401:mortality; D010343:Patient Admission; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012042:Registries; D056910:Republic of Korea; D012189:Retrospective Studies; D012307:Risk Factors; D013997:Time Factors
chemical_list: D015415:Biomarkers; D006454:Hemoglobins