Skip to main content

Advertisement

Log in

A multi-state model approach for prediction in chronic myeloid leukaemia

  • Original Article
  • Published:
Annals of Hematology Aims and scope Submit manuscript

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).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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

References

  1. O’Brien SG, Guilhot F, Larson RA, Gathmann I, Baccarani M, Cervantes F, Cornelissen JJ, Fischer T, Hochhaus A, Hughes T, Lechner K, Nielsen JL, Rousselot P, Reiffers J, Saglio G, Shepherd J, Simonsson B, Gratwohl A, Goldman JM, Kantarjian H, Taylor K, Verhoef G, Bolton AE, Capdeville R, Druker BJ (2003) Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N Engl J Med 348(11):994–1004

    Article  PubMed  Google Scholar 

  2. Druker BJ, Guilhot F, O’Brien SG, Gathmann I, Kantarjian H, Gattermann N, Deininger MWN, Silver RT, Goldman JM, Stone RM, Cervantes F, Hochhaus A, Powell BL, Gabrilove JL, Rousselot P, Reiffers J, Cornelissen JJ, Hughes T, Agis H, Fischer T, Verhoef G, Shepherd J, Saglio G, Gratwohl A, Nielsen JL, Radich JP, Simonsson B, Taylor K, Baccarani M, So C, Letvak L, Larson RA (2006) Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med 355(23):2408–2417

    Article  CAS  PubMed  Google Scholar 

  3. Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B Methodol 34(2):187–220

    Google Scholar 

  4. Pintilie M (2007) Analysing and interpreting competing risk data. Stat Med 26(6):1360–1367

    Article  PubMed  Google Scholar 

  5. Latouche A, Beyersmann J, Fine JP (2007) Comments on ‘Analysing and interpreting competing risk data’ by M. Pintilie, Statistics in Medicine 2006. Stat Med 26(19):3676–3679. doi:10.1002/sim.2655

    Article  CAS  PubMed  Google Scholar 

  6. Aalen OO, Johansen S (1978) An empirical transition matrix for non-homogeneous Markov chains based on censored observations. Scand J Stat 5(3):141–150

    Google Scholar 

  7. Baccarani M, Deininger MW, Rosti G, Hochhaus A, Soverini S, Apperley JF, Cervantes F, Clark RE, Cortes JE, Guilhot F, Hjorth-Hansen H, Hughes TP, Kantarjian HM, Kim D-W, Larson RA, Lipton JH, Mahon FX, Martinelli G, Mayer J, Müller MC, Niederwieser D, Pane F, Radich JP, Rousselot P, Saglio G, Saußele S, Schiffer C, Silver R, Simonsson B, Steegmann J-L, Goldman JM, Hehlmann R (2013) European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood 122(6):872–884

    Article  CAS  PubMed  Google Scholar 

  8. Hehlmann R, Lauseker M, Jung-Munkwitz S, Leitner A, Müller MC, Pletsch N, Proetel U, Haferlach C, Schlegelberger B, Balleisen L, Hänel M, Pfirrmann M, Krause SW, Nerl C, Pralle H, Gratwohl A, Hossfeld DK, Hasford J, Hochhaus A, Saußele S (2011) Tolerability-adapted imatinib 800 mg/d versus 400 mg/d versus 400 mg/d plus interferon-alpha in newly diagnosed chronic myeloid leukemia. J Clin Oncol 29(12):1634–1642

    Article  CAS  PubMed  Google Scholar 

  9. Anderson JR, Cain KC, Gelber RD (2008) Analysis of survival by tumor response and other comparisons of time-to-event by outcome variables. J Clin Oncol 26(24):3913–3915

    Article  PubMed  Google Scholar 

  10. Anderson JR, Cain KC, Gelber RD (1983) Analysis of survival by tumor response. J Clin Oncol 1(11):710–719

    CAS  PubMed  Google Scholar 

  11. Putter H, Fiocco M, Geskus RB (2007) Tutorial in biostatistics: competing risks and multi-state models. Stat Med 26(11):2389–2430

    Article  CAS  PubMed  Google Scholar 

  12. Pavlik T, Janousova E, Pospisil Z, Muzik J, Zackova D, Racil Z, Klamova H, Cetkovsky P, Trneny M, Mayer J, Dusek L (2011) Estimation of current cumulative incidence of leukaemia-free patients and current leukaemia-free survival in chronic myeloid leukaemia in the era of modern pharmacotherapy. BMC Med Res Methodol 11:140. doi:10.1186/1471-2288-11-140.).

  13. Fiocco M, Putter H, van Houwelingen HC (2008) Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Stat Med 27(21):4340–4358

    Article  PubMed  Google Scholar 

  14. Touraine C, Helmer C, Joly P (2013) Predictions in an illness-death model. Stat Methods Med Res. [Epub ahead of print].

  15. de Wreede LC, Fiocco M, Putter H (2010) The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Comput Methods Prog Biomed 99(3):261–274

    Article  Google Scholar 

  16. Hehlmann R, Muller MC, Lauseker M, Hanfstein B, Fabarius A, Schreiber A, Proetel U, Pletsch N, Pfirrmann M, Haferlach C, Schnittger S, Einsele H, Dengler J, Falge C, Kanz L, Neubauer A, Kneba M, Stegelmann F, Pfreundschuh M, Waller CF, Spiekermann K, Baerlocher GM, Ehninger G, Heim D, Heimpel H, Nerl C, Krause SW, Hossfeld DK, Kolb HJ, Hasford J, Saussele S, Hochhaus A (2014) Deep molecular response is reached by the majority of patients treated with imatinib, predicts survival, and is achieved more quickly by optimized high-dose imatinib: results from the randomized CML-study IV. J Clin Oncol 32(5):415–423

    Article  PubMed  Google Scholar 

  17. Cross NC, Hughes TP, Hochhaus A, Goldman JM (2008) International standardisation of quantitative real-time RT-PCR for BCR-ABL. Leuk Res 32(3):505–506

    Article  CAS  PubMed  Google Scholar 

  18. Müller MC, Cross NCP, Erben P, Schenk T, Hanfstein B, Ernst T, Hehlmann R, Branford S, Saglio G, Hochhaus A (2009) Harmonization of molecular monitoring of CML therapy in Europe. Leukemia 23(11):1957–1963

    Article  PubMed  Google Scholar 

  19. Hasford J, Baccarani M, Hoffmann V, Guilhot J, Saussele S, Rosti G, Guilhot F, Porkka K, Ossenkoppele G, Lindoerfer D, Simonsson B, Pfirrmann M, Hehlmann R (2011) Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score. Blood 118(3):686–692

    Article  CAS  PubMed  Google Scholar 

  20. Klein JP, Shu Y (2002) Multi-state models for bone marrow transplantation studies. Stat Methods Med Res 11(2):117–139

    Article  PubMed  Google Scholar 

  21. Keiding N, Klein JP, Horowitz MM (2001) Multi-state models and outcome prediction in bone marrow transplantation. Stat Med 20(12):1871–1885

    Article  CAS  PubMed  Google Scholar 

  22. Zeidner JF, Zahurak M, Rosner GL, Gocke CD, Jones RJ, Smith BD (2014) The evolution of treatment strategies for patients with chronic myeloid leukemia relapsing after allogeneic bone marrow transplantation: can tyrosine kinase inhibitors replace donor lymphocyte infusions? Leuk Lymphoma 1–7 [Epub ahead of print].

  23. Al-Kali A, Kantarjian H, Shan J, Bassett R, Quintás-Cardama A, Borthakur G, Jabbour E, Verstovsek S, O’Brien S, Cortes J (2010) Current event-free survival after sequential tyrosine kinase inhibitor therapy for chronic myeloid leukemia. Cancer 117(2):327–335

    Article  PubMed Central  PubMed  Google Scholar 

  24. Chevret S, Leporrier M, Chastang C (2000) Measures of treatment effectiveness on tumour response and survival: a multi-state model approach. Stat Med 19(6):837–848

    Article  CAS  PubMed  Google Scholar 

  25. Cailliod R, Quantin C, Carli PM, Jooste V, Teuff GL, Binquet C, Maynadie M (2005) A population-based assessment of the prognostic value of the CD19 positive lymphocyte count in B-cell chronic lymphocytic leukemia using Cox and Markov models. Eur J Epidemiol 20(12):993–1001

    Article  CAS  PubMed  Google Scholar 

  26. Petzer AL, Dominic F, Thomas L, Irina D, Zvenyslava M, Andrija B, Laimonas G, Sandra L, Stefan G, Liana G, Aleksandar S, Dontcho P, Nikolay T, Rasa G, Atanas S, Thomas G, Marthin K, Peter S, Guenther G, Dominik W (2012) High-dose imatinib induction followed by standard-dose maintenance in pre-treated chronic phase chronic myeloid leukemia patients—final analysis of a randomized, multicenter, phase III trial. Haematologica 97(10):1562–1569

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Gafter-Gvili A, Leader A, Gurion R, Vidal L, Ram R, Shacham-Abulafia A, Ben-Bassat I, Lishner M, Shpilberg O, Raanani P (2011) High-dose imatinib for newly diagnosed chronic phase chronic myeloid leukemia patients—systematic review and meta-analysis. Am J Hematol 86(8):657–662

    Article  CAS  PubMed  Google Scholar 

  28. Gratwohl A, Hermans J, Goldman JM, Arcese W, Carreras E, Devergie A, Frassoni F, Gahrton G, Kolb HJ, Niederwieser D, Ruutu T, Vernant JP, de Witte T, Apperley J (1998) Risk assessment for patients with chronic myeloid leukaemia before allogeneic blood or marrow transplantation. Lancet 352(9134):1087–1092

    Article  CAS  PubMed  Google Scholar 

  29. Rohrbacher M, Berger U, Hochhaus A, Metzgeroth G, Adam K, Lahaye T, Saussele S, Muller MC, Hasford J, Heimpel H, Hehlmann R (2009) Clinical trials underestimate the age of chronic myeloid leukemia (CML) patients. Incidence and median age of Ph/BCR-ABL-positive CML and other chronic myeloproliferative disorders in a representative area in Germany. Leukemia 23(3):602–604

    Article  CAS  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Consortia

Corresponding author

Correspondence to Michael Lauseker.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00277-014-2246-2

Keywords

Navigation