title: A statistical model for estimating donor postdonation platelet counts after plateletpheresis.
abstract: BACKGROUND
  To avoid the need, in serial apheresis donors, either to delay plateletpheresis until a predonation platelet count is completed or to obtain a postdonation count after each procedure, a statistical model has been developed to predict the postdonation platelet count from the donor predonation platelet count, weight, and hematocrit.
  
  
  METHODS
  Predonation and postdonation platelet counts were measured in two groups of approximately 100 consecutive donors (Group A to test the model and Group B to validate it), and the postdonation counts were calculated with the model. Using stepwise multiple linear regression from donor data, estimated postdonation platelet counts were found to be comparable to the postdonation platelet counts actually measured.
  
  
  RESULTS
  Estimated postdonation platelet counts x 10(9) per L (mean +/- SD) for each group, respectively, were Group A, 195 +/- 35, versus actual platelet counts of 195 +/- 39 (p = 0.43), and Group B, 183 +/- 36, versus actual platelet counts of 189 +/- 34 (p = 0.14). Sensitivity and specificity, respectively, were Group A, 57 and 99 percent and Group B, 62 and 99 percent.
  
  
  CONCLUSIONS
  For most serial apheresis donors, application of this predictor model should preclude the need to obtain an extra postdonation platelet count.
authors.affiliation: DeGowin Blood Center, University of Iowa Hospital & Clinics, University of Iowa College of Medicine, Iowa City.
authors.forename: I
authors.initials: I
authors.lastname: Cabezudo
authors.forename: D K
authors.initials: DK
authors.lastname: Winegarden
authors.forename: M J
authors.initials: MJ
authors.lastname: Randels
authors.forename: R G
authors.initials: RG
authors.lastname: Strauss
mesh_terms: D001782:Blood Donors; D001835:Body Weight; D006400:Hematocrit; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D010976:Platelet Count; D010983:Plateletpheresis; D013997:Time Factors