Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma.

Area of Science:

• Hematology
• Oncology
• Biostatistics

Background:

• Multiple myeloma (MM) is a complex blood cancer with variable prognoses.
• Current staging tools are limited as they were developed for newly diagnosed patients.
• A need exists for risk assessment tools at the second-line treatment stage.

Purpose of the Study:

• To develop and validate a risk stratification algorithm (RSA) for patients with multiple myeloma at second-line treatment.
• To evaluate the algorithm's ability to predict overall survival (OS) and stratify patients into distinct risk categories.

Main Methods:

• Developed a risk stratification algorithm (RSA) using data from patients with MM at second-line treatment.
• Utilized Cox models to derive hazard ratios for overall survival (OS) predictors.
• Employed k-adaptive partitioning for survival to stratify patients into risk groups.

Main Results:

• The RSA successfully stratified patients into four risk groups (1-4) with significantly different median OSs (61.6, 29.6, 14.2, and 5.9 months).
• Relative risk of mortality doubled with each ascending risk group.
• The algorithm's performance was validated on an external dataset, showing similar stratification.

Conclusions:

• A validated RSA has been developed to quantify risk in multiple myeloma patients at second-line treatment.
• The RSA effectively stratifies patients into groups with markedly different survival expectations.
• This tool can assess total risk, frailty risk, and disease aggressiveness risk for personalized treatment strategies.