Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Acute Leukemias

Comorbidity and performance status in acute myeloid leukemia patients: a nation-wide population-based cohort study

Leukemia volume 29, pages 548–555 (2015)Cite this article

Subjects

Abstract

As the world population ages, the comorbidity burden in acute myeloid leukemia (AML) patients increases. Evidence on how to integrate comorbidity measures into clinical decision-making is sparse. We determined the prognostic impact of comorbidity and World Health Organization Performance Status (PS) on achievement of complete remission and mortality in all Danish AML patients treated between 2000 and 2012 overall and stratified by age. Comorbidity was measured using a modified version of the Charlson Comorbidity Index, with separate adjustment for pre-leukemic conditions. Of 2792 patients, 1467 (52.5%) were allocated to intensive therapy. Of these patients, 76% did not have any comorbidities, 19% had one comorbid disease and 6% had two or more comorbidities. Low complete remission rates were associated with poor PS but not with comorbidity. Surprisingly, among all intensive therapy patients, presence of comorbidity was not associated with an increased short-term mortality (adjusted 90 day mortality rate (MR)=1.06 (95% confidence interval (CI)=0.76–1.48)) and, if any, only a slight increase in long-term mortality (91 day–3 year adjusted MR=1.18 (95%CI=0.97–1.44). Poor PS was strongly associated with an increased short- and long-term mortality (adjusted 90 day MR, PS⩾2=3.43 (95%CI=2.30–5.13); adjusted 91 day–3 year MR=1.35 (95%CI=1.06–1.74)). We propose that more patients with comorbidity may benefit from intensive chemotherapy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

Similar content being viewed by others

References

  1. United Nations. Department of Economic and Social Affairs. Population Division World population prospects. The 2010 Revision. United Nations: New York, NY, USA, 2011. Report no. ST/ESA/SER.A/313.

  2. Ferrara F . Treatment of unfit patients with acute myeloid leukemia: a still open clinical challenge. Clin Lymphoma Myeloma Leuk 2011; 11: 10–16.

    Article  CAS  PubMed  Google Scholar 

  3. Juliusson G, Antunovic P, Derolf A, Lehmann S, Mollgard L, Stockelberg D et al. Age and acute myeloid leukemia: real world data on decision to treat and outcomes from the Swedish Acute Leukemia Registry. Blood 2009; 113: 4179–4187.

    Article  CAS  PubMed  Google Scholar 

  4. Appelbaum FR, Gundacker H, Head DR, Slovak ML, Willman CL, Godwin JE et al. Age and acute myeloid leukemia. Blood 2006; 107: 3481–3485.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ording AG, Sorensen HT . Concepts of comorbidities, multiple morbidities, complications, and their clinical epidemiologic analogs. Clin Epidemiol 2013; 5: 199–203.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lewis JH, Kilgore ML, Goldman DP, Trimble EL, Kaplan R, Montello MJ et al. Participation of patients 65 years of age or older in cancer clinical trials. J Clin Oncol 2003; 21: 1383–1389.

    Article  PubMed  Google Scholar 

  7. Gangatharan SA, Grove CS, P'ng S, O'Reilly J, Joske D, Leahy MF et al. Acute myeloid leukaemia in Western Australia 1991-2005: a retrospective population-based study of 898 patients regarding epidemiology, cytogenetics, treatment and outcome. Intern Med J 2013; 43: 903–911.

    Article  CAS  PubMed  Google Scholar 

  8. Juliusson G Swedish AML Group. Most 70- to 79-year-old patients with acute myeloid leukemia do benefit from intensive treatment. Blood 2011; 117: 3473–3474.

    Article  CAS  PubMed  Google Scholar 

  9. Ostgard LSG, Norgaard M, Sengelov H, Kjeldsen L, Friis LS, Jensen MK et al. Do results from clinical trials in acute myeloid leukemia reflect clinical reality? a Danish national cohort study of 813 patients. ASH Annu Meet Abstr 2012; 120: 1477.

    Google Scholar 

  10. Population and elections—StatBank Denmark—data and statistics 2012; available at www.statbank.dk.

  11. Østgård L, Nørgaard J, Severinsen M, Friis L, Jensen M, Nielsen O et al. Data quality in the danish national acute leukemia registry: a hematological data resource. Clin Epidemiol 2013; 5: 335–344.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lowenberg B, Pabst T, Vellenga E, van Putten W, Schouten HC, Graux C et al. Cytarabine dose for acute myeloid leukemia. N Engl J Med 2011; 364: 1027–1036.

    Article  PubMed  Google Scholar 

  13. Grimwade D, Hills RK, Moorman AV, Walker H, Chatters S, Goldstone AH et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood 2010; 116: 354–365.

    Article  CAS  PubMed  Google Scholar 

  14. Andersen TF, Madsen M, Jorgensen J, Mellemkjoer L, Olsen JH . The Danish National Hospital Register. A valuable source of data for modern health sciences. Dan Med Bull 1999; 46: 263–268.

    CAS  PubMed  Google Scholar 

  15. Charlson ME, Pompei P, Ales KL, MacKenzie CR . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40: 373–383.

    Article  CAS  PubMed  Google Scholar 

  16. Nguyen-Nielsen M, Norgaard M, Jacobsen JB, Borre M, Thomsen RW, Sogaard M . Comorbidity and survival of Danish prostate cancer patients from 2000-2011: a population-based cohort study. Clin Epidemiol 2013; 5: 47–55.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M . The impact of comorbidity on cancer survival: a review. Clin Epidemiol 2013; 5: 3–29.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ording AG, Cronin-Fenton DP, Jacobsen JB, Norgaard M, Thomsen RW, Christiansen P et al. Comorbidity and survival of Danish breast cancer patients from 2000-2011: a population-based cohort study. Clin Epidemiol 2013; 5: 39–46.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sperr WR, Wimazal F, Kundi M, Baumgartner C, Nosslinger T, Makrai A et al. Comorbidity as prognostic variable in MDS: comparative evaluation of the HCT-CI and CCI in a core dataset of 419 patients of the Austrian MDS Study Group. Ann Oncol 2010; 21: 114–119.

    Article  CAS  PubMed  Google Scholar 

  20. Sorror ML, Maris MB, Storb R, Baron F, Sandmaier BM, Maloney DG et al. Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood 2005; 106: 2912–2919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982; 5: 649–655.

    Article  CAS  PubMed  Google Scholar 

  22. Eastern Cooperative Oncology GroupECOG Performance Status. available at http://www.ecog.org/general/perf_stat.html2006.

  23. Cheson BD, Bennett JM, Kopecky KJ, Buchner T, Willman CL, Estey EH et al. Revised recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol 2003; 21: 4642–4649.

    Article  PubMed  Google Scholar 

  24. Frank L . Epidemiology. When an entire country is a cohort. Science 2000; 287: 2398–2399.

    Article  CAS  PubMed  Google Scholar 

  25. Stata Data Analysis and Statistical SoftwareStataCorp LP. What's new in statistical software 2013; Availible at http://www.stata.com/stata12/multiple-imputation.

  26. White IR, Royston P . Imputing missing covariate values for the Cox model. Stat Med 2009; 28: 1982–1998.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rising B . Multiple imputation. Italian StataCorp LP: Bologa, Romania, 2010.

    Google Scholar 

  28. Graham JW, Olchowski AE, Gilreath TD . How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prev Sci 2007; 8: 206–213.

    Article  PubMed  Google Scholar 

  29. Burnett AK, Russell NH, Hills RK, Hunter AE, Kjeldsen L, Yin J et al. Optimization of chemotherapy for younger patients with acute myeloid leukemia: results of the medical research council AML15 trial. J Clin Oncol 2013; 31: 3360–3368.

    Article  CAS  PubMed  Google Scholar 

  30. Burnett AK, Russell NH, Hills RK, Kell J, Freeman S, Kjeldsen L et al. Addition of gemtuzumab ozogamicin to induction chemotherapy improves survival in older patients with acute myeloid leukemia. J Clin Oncol 2012; 30: 3924–3931.

    Article  CAS  PubMed  Google Scholar 

  31. Working Parties on Leukemia in Adults and ChildrenTrial in acute myeloid leukemia or high risk myelodysplastic syndrome 17 2008; available at http://aml17.cardiff.ac.uk.

  32. Kantarjian H, O'brien S, Cortes J, Giles F, Faderl S, Jabbour E et al. Results of intensive chemotherapy in 998 patients age 65 years or older with acute myeloid leukemia or high-risk myelodysplastic syndrome: predictive prognostic models for outcome. Cancer 2006; 106: 1090–1098.

    Article  PubMed  Google Scholar 

  33. Oran B, Weisdorf DJ . Survival for older patients with acute myeloid leukemia: a population-based study. Haematologica 2012; 97: 1916–1924.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Meyers J, Yu Y, Kaye JA, Davis KL . Medicare fee-for-service enrollees with primary acute myeloid leukemia: an analysis of treatment patterns, survival, and healthcare resource utilization and costs. Appl Health Econ Health Policy 2013; 11: 275–286.

    Article  PubMed  Google Scholar 

  35. Alibhai SM, Leach M, Minden MD, Brandwein J . Outcomes and quality of care in acute myeloid leukemia over 40 years. Cancer 2009; 115: 2903–2911.

    Article  PubMed  Google Scholar 

  36. Giles FJ, Borthakur G, Ravandi F, Faderl S, Verstovsek S, Thomas D et al. The haematopoietic cell transplantation comorbidity index score is predictive of early death and survival in patients over 60 years of age receiving induction therapy for acute myeloid leukaemia. Br J Haematol 2007; 136: 624–627.

    Article  PubMed  Google Scholar 

  37. Malfuson JV, Etienne A, Turlure P, de Revel T, Thomas X, Contentin N et al. Risk factors and decision criteria for intensive chemotherapy in older patients with acute myeloid leukemia. Haematologica 2008; 93: 1806–1813.

    Article  CAS  PubMed  Google Scholar 

  38. Juliusson G . Older patients with acute myeloid leukemia benefit from intensive chemotherapy: an update from the Swedish Acute Leukemia Registry. Clin Lymphoma Myeloma Leuk 2011; 11 (Suppl 1): S54–S59.

    Article  PubMed  Google Scholar 

  39. Bacher U, Haferlach T . The benefit of population-based studies for older patients with acute myeloid leukemia. Haematologica 2012; 97: 1781–1782.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Juliusson G, Lazarevic V, Horstedt AS, Hagberg O, Hoglund M . Acute myeloid leukemia in the real world: why population-based registries are needed. Blood 2012; 119: 3890–3899.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Thygesen SK, Christiansen CF, Christensen S, Lash TL, Sorensen HT . The predictive value of ICD-10 diagnostic coding used to assess Charlson comorbidity index conditions in the population-based Danish National Registry of Patients. BMC Med Res Methodol 2011; 11: 83.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We wish to thank the hematologists who carefully report the available and relevant AML patient data to the DNLR. We appreciate the dedicated work of Secretary Kirsten Hansen, the Aarhus University Hospital, and of the data coordinator, Dr Peter Brown, the Copenhagen University Hospital. Also, we are indebted to Dr Bruno Medeiros, the Stanford University School of Medicine, for his constructive feedback on the manuscript. This study was supported by research funding from the University of Aarhus (Faculty of Health), the Arvid Nilsson Foundation, the Fraenkel Memorial Foundation, the Danish Cancer Society, the A.V Lykfeldt Foundation, the Grocer M Brogaard and Wife Foundation and F Ejner Willumsen’s grant. None of the funding sources contributed to the design, performance, analysis or reporting of this study.

Author information

Authors and Affiliations

  1. Department of Hematology, Aarhus University Hospital, Aarhus, Denmark

    L S G Østgård & J M Nørgaard

  2. Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark

    L S G Østgård & M Nørgaard

  3. Department of Hematology, Rigshospitalet, Copenhagen, Denmark

    H Sengeløv & L S Friis

  4. Department of Hematology, Aalborg University Hospital, Aalborg, Denmark

    M Severinsen

  5. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

    M Severinsen

  6. Department of Hematology, Odense University Hospital, Odense, Denmark

    C W Marcher

  7. Department of Hematology, Herlev University Hospital, Herlev, Denmark

    I H Dufva

Authors
  1. L S G Østgård
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J M Nørgaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H Sengeløv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M Severinsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L S Friis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. C W Marcher
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. I H Dufva
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. M Nørgaard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L S G Østgård.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Østgård, L., Nørgaard, J., Sengeløv, H. et al. Comorbidity and performance status in acute myeloid leukemia patients: a nation-wide population-based cohort study. Leukemia 29, 548–555 (2015). https://doi.org/10.1038/leu.2014.234

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/leu.2014.234

This article is cited by

Search

Advanced search

Quick links