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Determination of Fibrinogen Ratio Cutoff Limits Using Indirect Reference Interval Methodology.

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Clinicians use fibrinogen antigen-to-activity ratios to detect dysfibrinogenemia. A new algorithm using large datasets accurately estimated ratio cutoffs, but overestimated reference interval limits for fibrinogen activity and antigen.

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Area of Science:

  • Clinical Chemistry
  • Hematology

Background:

  • Discordant fibrinogen antigen-to-activity ratios are key indicators for diagnosing dysfibrinogenemia.
  • Current laboratory cutoffs for these ratios are often based on limited validation studies.
  • There is a need for more robust methods to establish accurate diagnostic criteria.

Purpose of the Study:

  • To apply indirect reference interval (RI) methodology using large clinical datasets to determine reliable fibrinogen ratio cutoffs.
  • To evaluate the efficacy of the RefineR algorithm in establishing these diagnostic parameters.
  • To compare algorithm-derived cutoffs with existing validated laboratory standards.

Main Methods:

  • Analysis of two large datasets (n=3693 and n=2192) of patient results including fibrinogen antigen and activity measurements.
  • Utilized radial immunodiffusion (RID) and turbidimetry immunoassay for antigen quantification.
  • Applied the RefineR algorithm to estimate reference intervals and ratio cutoffs for fibrinogen.

Main Results:

  • Estimated fibrinogen antigen/activity ratio cutoffs closely approximated validated laboratory cutoffs (within 8% difference).
  • The RefineR algorithm significantly overestimated upper reference interval limits for both fibrinogen antigen and activity (38.4%-57.2%).
  • Estimated reference intervals were substantially wider than validated limits (41.8%-80%).

Conclusions:

  • The RefineR algorithm offers a viable method for determining fibrinogen ratio cutoffs using extensive clinical data.
  • The algorithm's inability to distinguish acute-phase reactants and its overestimation of RI limits require further refinement.
  • This approach enhances the potential for more accurate dysfibrinogenemia detection through large-scale data analysis.