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This study developed a reliable algorithm to assess multiple myeloma treatment response using real-world data (RWD). The derived response (dR) algorithm accurately reflects clinical trial results and shows significant association with overall survival in RWD.

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Area of Science:

  • Hematology
  • Oncology
  • Data Science

Background:

  • Real-world data (RWD) are crucial for understanding multiple myeloma (MM) treatment patterns.
  • Assessing treatment response in MM using RWD is challenging due to incomplete laboratory measurements.

Purpose of the Study:

  • To develop and validate a rule-based algorithm for deriving treatment response (dR) from incomplete RWD in multiple myeloma.
  • To evaluate the accuracy and utility of the dR algorithm in a clinical trial setting and a real-world patient cohort.

Main Methods:

  • A rule-based algorithm adapted from International Myeloma Working Group criteria was developed to derive response (dR) from RWD.
  • The dR algorithm was validated against independent review committee (IRC) assessments from the BELLINI trial.
  • Sensitivity analyses simulated missing data; associations with overall survival were evaluated in a real-world electronic health record database.

Main Results:

  • The dR algorithm demonstrated high concordance with IRC assessments (Cohen's Kappa 0.83) and replicated trial findings on response rates.
  • The algorithm maintained accuracy (Cohen's Kappa 0.75) even with simulated 50-75% reductions in laboratory data.
  • dR was significantly associated with overall survival in the RWD cohort at both individual and treatment levels.

Conclusions:

  • Deriving accurate treatment response from RWD using the dR algorithm is feasible.
  • The dR algorithm shows potential as a reliable endpoint for real-world studies and a comparator in single-arm clinical trials for multiple myeloma.