Abstract
Multi-state models support prediction in medicine. With different states of disease, chronic myeloid leukaemia (CML) is particularly suited for the application of multi-state models. In this article, we tried to find a model for CML that allows predicting the prevalence of three different states (initial state of disease, remission and progression) in dependence on treatment, adjusted for age, sex and risk score. Based on the German CML Study IV, one of the largest randomised studies in CML, the model was able to represent the known effects of age and risk score on the probabilities of remission and progression. Patients achieving a major molecular remission had a better chance of surviving without progression, but this effect was not significant. Comparing treatments, patient of the high-dose arm had the greatest chance to be in the state “remission” at 5 years but did not seem to have an advantage considering “progression”. The proposed illness-death model can be useful for predicting the course of CML based on the patient’s individual covariates (trial registration: this is an explorative analysis of ClinicalTrials.gov Identifier: NCT00055874).
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Abbreviations
- CML:
-
Chronic myeloid leukaemia
- TKI:
-
Tyrosine kinase inhibitor
- IFN:
-
Interferon
- ELN:
-
European LeukemiaNet
- EBMT:
-
European Group for Blood and Marrow Transplantation
- RQ-PCR:
-
Real-time quantitative polymerase chain reaction
- IS:
-
International scale
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Acknowledgments
We would like to thank Yi Hao, Susanne Benda and Claudia Richter for their help with layout and graphics. This work was supported by the Deutsche Jose-Carreras Leukämie-Stiftung (Grant no. DJCLS R05/23).
Conflict of interest
The authors declare that there is no conflict of interest.
Authors’ contributions
ML, VSH and MP analysed the data.
ML, JH, MCM, RH and MP designed the research study.
MCM and RH provided study materials and patients.
All authors wrote the paper and approved the final version of the paper.
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Lauseker, M., Hasford, J., Hoffmann, V.S. et al. A multi-state model approach for prediction in chronic myeloid leukaemia. Ann Hematol 94, 919–927 (2015). https://doi.org/10.1007/s00277-014-2246-2
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DOI: https://doi.org/10.1007/s00277-014-2246-2