4.7 Article

Assessment and validation of the molecular international prognostic scoring system for myelodysplastic syndromes

Journal

LEUKEMIA
Volume 37, Issue 7, Pages 1530-1539

Publisher

SPRINGERNATURE
DOI: 10.1038/s41375-023-01910-3

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The Molecular International Prognostic Scoring System (IPSS-M) is a new risk stratification model for myelodysplastic syndromes (MDS) that incorporates mutational data to improve prognostic accuracy. In a large cohort of MDS patients, the IPSS-M was validated and found to be accurate in predicting overall survival (OS), leukemia-free survival (LFS), and leukemic transformation. The model also showed validity in therapy-related and hypoplastic MDS. The use of this tool can lead to more accurate prognostic assessment and optimized therapeutic decision-making in MDS.
The Molecular International Prognostic Scoring System (IPSS-M) is a novel risk stratification model for myelodysplastic syndromes (MDS) that builds on the IPSS and IPSS-R by incorporating mutational data. The model showed improved prognostic accuracy over the IPSS-R across three endpoints: overall survival (OS), leukemia-free survival (LFS) and leukemic transformation. This study aimed to validate the findings of the original in a large cohort of MDS patients, as well as assess its validity in therapy-related and hypoplastic MDS. We retrospectively reviewed clinical, cytogenetic and molecular data for 2355 MDS patients treated at the Moffitt Cancer Center. Correlative analysis between IPSS-R and mean IPSS-M scores and outcome predictions was performed on LFS, OS and leukemic transformation. Using the IPSS-M, patients were classified as Very Low (4%), Low (24%), Moderate-Low (14%), Moderate-High (11%), High (19%) and Very-High risk (28%). Median OS was 11.7, 7.1, 4.4, 3.1, 2.3, and 1.3 years from VL to VH risk subgroups. Median LFS was 12.3, 6.9, 3.6, 2.2, 1.4, and 0.5 years respectively. For patients with t-MDS and h-MDS the model retained its prognostic accuracy. Generalized use of this tool will likely result in more accurate prognostic assessment and optimize therapeutic decision-making in MDS.

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