Real-world data on the survival outcome of patients with newly diagnosed Waldenström macroglobulinemia
- Author:
Jang Ho CHO
1
;
Joon-Ho SHIM
;
Sang Eun YOON
;
Hee-Jin KIM
;
Sun-Hee KIM
;
Young Hyeh KO
;
Seung-Tae LEE
;
Kihyun KIM
;
Won Seog KIM
;
Seok Jin KIM
Author Information
- Publication Type:2
- From:The Korean Journal of Internal Medicine 2021;36(3):668-678
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background/Aims:Waldenström macroglobulinemia (WM) is a rare lymphoproliferative disorder that usually follows an indolent clinical course. However, some patients show an aggressive clinical course leading to death. We explored the risk factors predicting poor prognosis in WM patients.
Methods:We retrospectively analyzed 47 patients diagnosed with WM between 2000 and 2018 to explore risk factors predicting poor prognosis using various clinical and laboratory parameters and risk models including the International Prognostic Staging System for WM (IPSS-WM).
Results:Over a median follow-up duration of 80.4 months, 29 patients died. The main causes of death were disease progression, organ failure related to amyloidosis, and infection. The median overall survival (OS) was 55.1 months, and 14 patients, including three with amyloidosis, died within 2 years. Serum β2-microglobulin level higher than 4 mg/dL was significantly associated with poor OS. Accordingly, the IPSS-WM showed a significant association with poor prognosis compared with other risk models, and the low-risk group had better OS than intermediate- and high-risk groups. In the retrospective analysis using the results of targeted sequencing in two cases representing good and bad prognosis, different patterns of mutation profiles were observed, including mutations of MYD88, TP53, ARID1A, and JAK2 in a refractory case.
Conclusions:Serum β2-microglobulin could be a single biomarker strongly predictive of poor survival of WM patients, and the low-risk group of the IPSS-WM risk model including serum β2-microglobulin has better prognostic value than other risk models. Mutation analysis also might provide additional information to predict high-risk patients.
