Patterns of relapse and progression in multiple myeloma patients after auto-SCT: implications for patients’ monitoring after transplantation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-03

AUTHORS

D Zamarin, S Giralt, H Landau, N Lendvai, A Lesokhin, D Chung, G Koehne, D Chimento, S M Devlin, E Riedel, M Bhutani, D Babu, H Hassoun

ABSTRACT

Auto-SCT (ASCT) is widely used in first-line treatment of multiple myeloma (MM). However, most patients eventually relapse or have progression of disease (R/POD). Although precise knowledge of R/POD patterns would be important to generate evidence-based surveillance recommendations after ASCT, such data is limited in the literature, especially after introduction of the free light chain assay (FLCA). This retrospective study examined the patterns of R/POD after first-line ASCT in 273 patients, using established criteria. At the time of R/POD, only 2% of patients had no associated serological evidence of R/POD. A total of 85% had asymptomatic R/POD, first detected by serological testing, whereas 15% had symptomatic R/POD with aggressive disease, early R/POD and short survival, with poor cytogenetics and younger age identified as risk factors. Although occult skeletal lesions were found in 40% of asymptomatic patients tested following serological R/POD, yearly skeletal surveys and urine testing were poor at heralding R/POD. We found a consistent association between paraprotein types at diagnosis and R/POD, allowing informed recommendations for appropriate serological monitoring and propose a new needed criterion using FLCA for patients relapsing by FLC only. Our findings provide important evidence-based recommendations that strengthen current monitoring guidelines after first-line ASCT in MM. More... »

PAGES

419

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/bmt.2012.151

DOI

http://dx.doi.org/10.1038/bmt.2012.151

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007256716

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22890289


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RDF/XML is a standard XML format for linked data.

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