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Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma
Author(s) -
Hájek Roman,
Delforge Michel,
Raab Marc S.,
Schoen Paul,
DeCosta Lucy,
Spicka Ivan,
Radocha Jakub,
Pour Ludek,
GonzalezMcQuire Sebastian,
Bouwmeester Walter
Publication year - 2019
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.16105
Subject(s) - risk stratification , medicine , multiple myeloma , proportional hazards model , hazard ratio , disease , risk assessment , malignancy , algorithm , oncology , confidence interval , computer science , computer security
Summary Multiple myeloma (MM) is a malignancy with varying survival outcomes and drivers of disease progression. Existing MM staging tools were developed using data from newly diagnosed patients. As patient characteristics and disease‐related factors change between diagnosis and the initiation of second‐line (2L) treatment, an unmet need exists for a tool that can evaluate risk of death at first relapse. We have developed a risk stratification algorithm (RSA) using data from patients with MM who were at 2L. Hazard ratios for independent predictors of overall survival (OS) were derived from a Cox models, and individual patient scores were calculated for total risk. K‐adaptive partitioning for survival was used to stratify patients into groups based on their scores. Relative risk doubled with ascending risk group; median OSs for patients in group 1 (lowest risk)–4 (highest risk) were 61·6, 29·6, 14·2 and 5·9 months, respectively. Differences in OS between risk groups were significant. Similar stratification was observed when the RSA was applied to an external validation data set. In conclusion, we have developed a validated RSA that can quantify total risk, frailty risk and disease aggressiveness risk, and stratify patients with MM at 2L into groups with profoundly different survival expectations.