Prognostic Models for Patients with Myelodysplastic Syndromes

Chapter
First Online:
Part of the Hematologic Malignancies book series (HEMATOLOGIC)

Abstract

The myelodysplastic syndromes (MDS) are characterized by significant heterogeneity, both at the clinical and molecular levels. This translates in significant differences in terms of survival and response to therapy. A subgroup of patients may never require any intervention for MDS, whereas other patients will succumb to the disease shortly after diagnosis. To overcome this problem and to develop tools to predict survival and response to therapy, a number of groups over the last two decades have worked on the development of prognostic tools for patients with MDS. Since 1997, the standard prognostic tool has been the IPSS. This model has been fundamental for the most recent progress in MDS, including drug development and understanding of the impact of molecular alterations in this group of disorders. But with this progress has come the realization of the limitations of IPSS. This has resulted in the development of several new prognostic systems for patients with MDS, such as the WPSS, the Global MD Anderson model, and the models specific for patients with the so-called lower-risk disease or that incorporate comorbidities. These efforts have recently culminated in the development of the new revised IPSS. This prognostic system is the result of a large effort by multiple investigators in every continent and represents now the accepted standard prognostic system for MDS. In parallel with this effort, we have also witnessed significant advances in our understanding of the molecular basis and, in particular, mutational events in MDS. It is likely that this new molecular data will be rapidly incorporated into these classic clinical models, such as IPSS-R. Finally, we need better models to predict response to specific forms of therapy and outcomes once therapy is instituted.

Keywords

Prognostic Model Bone Marrow Blast Marrow Fibrosis Prognostic System Transfusion Dependency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of LeukemiaThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of LeukemiaUniversity of Texas MD Anderson Cancer CenterHoustonUSA

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