communication). Gagelmann et al recently described a clinical-molecular prognostic score
(Myelofibrosis Transplant Scoring System, MTSS) that can predict subsequent OS at the time
of referral to allogenic transplant 28. This model is applicable to both PMF and SMF cases
and considers the presence of ASXL1 mutation and the absence of CALR/MPL as molecular
parameters 28. In such a vast scenario of prognostic models, a practical algorithm has been
proposed for a personalized approach to transplant allocation 29: PMF and SMF patients up
to 70 years and whose survival is less than five years are firstly selected using the most recent
disease-specific prognostic scores, MIPSS70 for PMF and MYSEC-PM for SMF 29.
Alternatively, in case of limited availability to molecular tests, young PMF patients may also
be considered candidates in case of intermediate-1 DIPSS and ASXL1 mutated 29.
Subsequent application of MTSS identifies subjects with better posttransplant OS and reduced
risk of complications 28.
Lastly, since the latest World Health Organization (WHO) classification has recognized pre-fibrotic
myelofibrosis (pre-PMF) as a distinct MPNs subtype 30, several studies have focused
on this entity. The risk of thrombotic events in pre-PMF seems to be accurately predicted by
the IPSET-t score 31. Besides, a multistate model of survival prediction has recently been
proposed 32: because of the heterogeneous nature of pre-PMF, whose clinic sometimes
resembles either ET or PMF, the most relevant prognostic factors appear to be leukocytosis
and the presence of at least one HMR mutation 32.
It is now clear that the way forward to improve and personalize risk prediction in MPNs is
through molecular data, but as long as large collaborative datasets, statistical methods (i.e.,
artificial intelligence models) and clinically relevant end-points are shared by the scientific
community.
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