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A clinical staging system for multiple myeloma correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival

Brian G.M. Durie, Sydney E. Salmon

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Abstract

The presenting clinical features of 71 patients with multiple myeloma were correlated with myeloma cell mass (myeloma cells X 10(12)/m2 of body surface area) determined from measurements of monoclonal immunoglobulin (M-component) synthesis and metabolism. Bivariate correlation and multivariate regression analyses showed that myeloma cell mass could be accurately predicted from A) extent of bone lesions, B) hemoglobin level, C) serum calcium level, and D) M-component levels in serum and urine. Analyses of response to chemotherapy and survival indicated significant correlation with measured myeloma cell burden. The results were synthesized to produce a very reliable and useful clinical staging system with three tumor cell mass levels (Table 7). For clinical research purposes, multivariate regression equations were developed to predict optimally the exact myeloma cell mass. Thus, initial staging can be quantitatively related to followup using tumor cell mass changes calculated from changes in M-component production. Use of the clinical staging system sould provide better initial assessment and followup of individual patients, and should lead to improved study design and analysis in large clinical trials of therapy for multiple myeloma.

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What this paper is about

The presenting clinical features of 71 patients with multiple myeloma were correlated with myeloma cell mass (myeloma cells X 10(12)/m2 of body surface area) determined from measurements of monoclonal immunoglobulin (M-component) synthesis and metabolism. Bivariate correlation and multivariate regression analyses showed that myeloma cell mass could be accurately predicted from A) extent of bone lesions, B) hemoglobin level, C) serum calcium level, and D) M-component levels in serum and urine. Analyses of response to chemotherapy and survival indicated significant correlation with measured myeloma cell burden. The results were synthesized to produce a very reliable and useful clinical staging system with three tumor cell mass levels (Table 7). For clinical research purposes, multivariate regression equations were developed to predict optimally the exact myeloma cell mass. Thus, initial staging can be quantitatively related to followup using tumor cell mass changes calculated from changes in M-component production. Use of the clinical staging system sould provide better initial assessment and followup of individual patients, and should lead to improved study design and analysis in large clinical trials of therapy for multiple myeloma.

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Available abstract

The presenting clinical features of 71 patients with multiple myeloma were correlated with myeloma cell mass (myeloma cells X 10(12)/m2 of body surface area) determined from measurements of monoclonal immunoglobulin (M-component) synthesis and metabolism. Bivariate correlation and multivariate regression analyses showed that myeloma cell mass could be accurately predicted from A) extent of bone lesions, B) hemoglobin level, C) serum calcium level, and D) M-component levels in serum and urine. Analyses of response to chemotherapy and survival indicated significant correlation with measured myeloma cell burden. The results were synthesized to produce a very reliable and useful clinical staging system with three tumor cell mass levels (Table 7). For clinical research purposes, multivariate regression equations were developed to predict optimally the exact myeloma cell mass. Thus, initial staging can be quantitatively related to followup using tumor cell mass changes calculated from changes in M-component production. Use of the clinical staging system sould provide better initial assessment and followup of individual patients, and should lead to improved study design and analysis in large clinical trials of therapy for multiple myeloma.

Key concepts: Multiple myeloma, Medicine, Oncology, Internal medicine, Bone marrow, Myeloma protein, Multivariate analysis

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