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Editorials
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A post-transplant optimized transplant-specific risk score in myelodysplastic syndromes
Mahasweta Gooptu and John Koreth
Division of Hematologic Malignancies, Dana-Farber Cancer Institute, Boston, MA, USA E-mail: MAHASWETA GOOPTU - mahasweta_gooptu@dfci.harvard.edu
doi:10.3324/haematol.2018.214452
Allogeneic hematopoietic stem-cell transplantation (HSCT) remains the only potentially curative ther- apy for myelodysplastic syndromes (MDS), but treatment risks include relapse and non-relapse mortality (NRM). Whereas relapse following HSCT is typically dic- tated by disease-related factors, NRM is more influenced by patient- (performance status, co-morbidity, etc.) and transplant-related factors (donor type, conditioning inten- sity, graft-versus-host disease prophylaxis regimen, etc.). In order to improve transplant decision-making for the individual MDS patient, better prediction of HSCT out- comes, by including both relapse and NRM predictors in a comprehensive individualized and dynamic risk model, would be optimal. So where do we stand currently?
The prognosis of MDS has historically been based on the International Prognostic Scoring System (IPSS). For transplant decision-making, Markov models based on the IPSS have documented that MDS patients with low- and intermediate-1-risk MDS have better survival outcomes without transplant, whereas transplantation results in better survival outcomes for patients with intermediate- 2- and high-risk MDS.1,2 The Revised International Prognostic Scoring System (R-IPSS), a refinement of the IPSS, is used to prognosticate MDS at diagnosis, particu- larly the risk for transformation to acute myeloid leukemia,3 and is often used as part of the decision to pro- ceed to transplantation or not.4
While the IPSS and R-IPSS focus on disease features, they do not consider patient- and transplant related fac- tors relevant to HSCT outcome. Attempts have, there- fore, been made to develop MDS transplant-specific risk scores to predict survival better. These scores include the transplantation risk index developed by the Gruppo Italiano Trapianto di Midollo Osseo (GITMO)4 registry using 519 patients as well as a risk score from the Center for International Blood and Marrow Transplant Research (CIBMTR)5, using 1,519 patients. Both of these indices identified similar prognostic variables (including the R- IPSS), dividing MDS transplant recipients into four risk
groups with overall survival rates ranging from 5-76%. However, these indices have not been universally adopt- ed in current practice. While the GITMO index has not been externally validated, the CIBMTR index was vali- dated on a distinct subset of patients from within the CIBMTR database. Gagelmann et al. now report on another composite risk score with better predictive abili- ty than the existing indices.6
The authors compiled a cohort of 1,059 adult patients (≥18 years) with MDS from the European Society for Blood and Marrow Transplantation (EBMT) registry who underwent HLA-matched HSCT from a related or unre- lated donor between 2000 to 2014. Using a Cox propor- tional hazards model they identified seven variables with significant impact on overall survival: age >50 years, matched unrelated donor, Karnofsky Performance Status <90%, very poor cytogenetics or monosomal karyotype, positive cytomegalovirus status of the recipient, peripher- al blood blasts >1% and platelet count ≤50 x 109/L. Of these, age and cytogenetic risk were the strongest predic- tors of survival, based on hazard ratios for death, and given more weight than the other factors in the final score. Four prognostic groups were identified (low, inter- mediate, high and very-high risk) with overall survival rates of 68.7%, 43.2%, 26.6% and 9.5%, respectively.
How does the EBMT score described in the paper by Gagelmann et al. compare to the prior CIBMTR and GITMO scores as well as the R-IPSS itself? One approach would be to compare the concordance or c-statistic (mea- sured as area under the receiver operating curve) of the different indices. The c-statistic is used to compare the goodness of fit of logistic regression models with values that range from 0.5 to 1.0. A c-statistic of 0.5 indicates the predictive ability of the index is no better than chance while a c-statistic in the 0.7-0.8 range has reasonable dis- criminatory power. Looking at the c-statistic following cross- validation, the EBMT transplant risk index scored 0.609 (95% confidence interval: 0.588 to 0.629), which was better than the CIBMTR (0.555) and GITMO (0.579)
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