Skip to main content

Thank you for visiting 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.

  • Article
  • Published:

Myelodysplastic syndrome

The IPSS-R more accurately captures fatigue severity of newly diagnosed patients with myelodysplastic syndromes compared with the IPSS index


We aimed to compare fatigue of newly diagnosed patients with myelodysplastic syndromes (MDS) with that of the general population (GP). We also investigated the ability of the IPSS and IPSS-R to capture severity of patient-reported fatigue at diagnostic workup. A sample of 927 newly diagnosed patients with MDS was consecutively enrolled in a large international observational study and all patients completed the FACIT-Fatigue questionnaire at baseline. Fatigue was compared with that of the GP (N = 1075) and a 3-point difference in mean scores was considered as clinically meaningful. Fatigue of MDS patients was on average 4.6 points below the mean of the GP (95% CI, −5.9 to −3.2, p < 0.001), reflecting clinically meaningful worse fatigue. Unlike the IPSS, the IPSS-R identified clearly distinct subgroups with regard to burden of fatigue. Mean scores differences compared with GP ranged from nonclinically relevant for very low risk (Δ = −1.8, 95% CI, −4.0 to 0.5, p = 0.119) to large clinically meaningful differences for very high-risk IPSS-R patients (Δ = −8.2, 95% CI, −10.3 to −6.2, p < 0.001). At diagnostic workup, fatigue of MDS is clinically meaningful worse than that reported by the GP. Compared with the IPSS classification, the IPSS-R provides a better stratification of patients with regard to fatigue severity.

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

Access options

Buy this article

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

Fig. 1: The Functional Assessment of Chronic Illness Therapy (FACIT) Fatigue Questionnaire.
Fig. 2: Cumulative distribution of FACIT-Fatigue scores in patients with MDS and the general adult population.
Fig. 3: Cumulative distribution of FACIT-Fatigue scores in patients with MDS by transfusion dependency and the general adult population.
Fig. 4: Mean differences in FACIT-Fatigue scores between MDS patients and the GP by IPSS-R risk classification.

Similar content being viewed by others


  1. Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127:2391–405.

    Article  CAS  Google Scholar 

  2. Malcovati L, Hellstrom-Lindberg E, Bowen D, Ades L, Cermak J, Del Canizo C, et al. Diagnosis and treatment of primary myelodysplastic syndromes in adults: recommendations from the European LeukemiaNet. Blood. 2013;122:2943–64.

    Article  CAS  Google Scholar 

  3. Steensma DP, Bennett JM. The myelodysplastic syndromes: diagnosis and treatment. Mayo Clin Proc. 2006;81:104–30.

    Article  Google Scholar 

  4. Malcovati L, Germing U, Kuendgen A, Della Porta MG, Pascutto C, Invernizzi R, et al. Time-dependent prognostic scoring system for predicting survival and leukemic evolution in myelodysplastic syndromes. J Clin Oncol. 2007;25:3503–10.

    Article  Google Scholar 

  5. Bejar R. Prognostic models in myelodysplastic syndromes. Hematol Am Soc Hematol Educ Program. 2013;2013:504–10.

    Article  Google Scholar 

  6. Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood 1997;89:2079–88.

    Article  CAS  Google Scholar 

  7. Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120:2454–65.

    Article  CAS  Google Scholar 

  8. Montalban-Bravo G, Garcia-Manero G. Myelodysplastic syndromes: 2018 update on diagnosis, risk-stratification and management. Am J Hematol. 2018;93:129–47.

    Article  Google Scholar 

  9. Cheson BD, Bennett JM, Kantarjian H, Pinto A, Schiffer CA, Nimer SD, et al. Report of an international working group to standardize response criteria for myelodysplastic syndromes. Blood. 2000;96:3671–4.

    CAS  PubMed  Google Scholar 

  10. Abel GA, Efficace F, Buckstein RJ, Tinsley S, Jurcic JG, Martins Y, et al. Prospective international validation of the Quality of Life in Myelodysplasia Scale (QUALMS). Haematologica. 2016;101:781–8.

    Article  CAS  Google Scholar 

  11. Stauder R, Yu G, Koinig KA, Bagguley T, Fenaux P, Symeonidis A, et al. Health-related quality of life in lower-risk MDS patients compared with age- and sex-matched reference populations: a European LeukemiaNet study. Leukemia. 2018;32:1380–92.

    Article  Google Scholar 

  12. Sekeres MA, Gore SD, Stablein DM, DiFronzo N, Abel GA, DeZern AE, et al. The National MDS Natural History Study: design of an integrated data and sample biorepository to promote research studies in myelodysplastic syndromes. Leuk Lymphoma. 2019;60:3161–71.

  13. Efficace F, Gaidano G, Breccia M, Criscuolo M, Cottone F, Caocci G, et al. Prevalence, severity and correlates of fatigue in newly diagnosed patients with myelodysplastic syndromes. Br J Haematol. 2015;168:361–70.

    Article  Google Scholar 

  14. Steensma DP, Heptinstall KV, Johnson VM, Novotny PJ, Sloan JA, Camoriano JK, et al. Common troublesome symptoms and their impact on quality of life in patients with myelodysplastic syndromes (MDS): results of a large internet-based survey. Leuk Res. 2008;32:691–8.

    Article  Google Scholar 

  15. Troy JD, de Castro CM, Pupa MR, Samsa GP, Abernethy AP, LeBlanc TW. Patient-reported distress in myelodysplastic syndromes and its association with clinical outcomes: a retrospective cohort study. J Natl Compr Canc Netw. 2018;16:267–73.

    Article  Google Scholar 

  16. Efficace F, Gaidano G, Breccia M, Voso MT, Cottone F, Angelucci E, et al. Prognostic value of self-reported fatigue on overall survival in patients with myelodysplastic syndromes: a multicentre, prospective, observational, cohort study. Lancet Oncol. 2015;16:1506–14.

    Article  Google Scholar 

  17. Platzbecker U. Treatment of MDS. Blood. 2019;133:1096–107.

    Article  CAS  Google Scholar 

  18. Fenaux P, Platzbecker U, Ades L. How we manage adults with myelodysplastic syndrome. Br J Haematol. 2019. [E-pub ahead of print].

  19. Vardiman JW, Harris NL, Brunning RD. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood. 2002;100:2292–302.

    Article  CAS  Google Scholar 

  20. Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009;114:937–51.

    Article  CAS  Google Scholar 

  21. Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85:365–76.

    Article  CAS  Google Scholar 

  22. Yellen SB, Cella DF, Webster K, Blendowski C, Kaplan E. Measuring fatigue and other anemia-related symptoms with the functional assessment of cancer therapy (FACT) measurement system. J Pain Symptom Manag. 1997;13:63–74.

    Article  CAS  Google Scholar 

  23. Cella D, Zagari MJ, Vandoros C, Gagnon DD, Hurtz HJ, Nortier JW. Epoetin alfa treatment results in clinically significant improvements in quality of life in anemic cancer patients when referenced to the general population. J Clin Oncol. 2003;21:366–73.

    Article  Google Scholar 

  24. Cella D. The Functional Assessment of Cancer Therapy-Anemia (FACT-An) Scale: a new tool for the assessment of outcomes in cancer anemia and fatigue. Semin Hematol. 1997;34(3 Suppl 2):13–9.

    CAS  PubMed  Google Scholar 

  25. Cella D, Eton DT, Lai JS, Peterman AH, Merkel DE. Combining anchor and distribution-based methods to derive minimal clinically important differences on the functional assessment of cancer therapy (FACT) anemia and fatigue scales. J Pain Symptom Manag. 2002;24:547–61.

    Article  Google Scholar 

  26. Pfeilstocker M, Tuechler H, Sanz G, Schanz J, Garcia-Manero G, Sole F, et al. Time-dependent changes in mortality and transformation risk in MDS. Blood. 2016;128:902–10.

    Article  Google Scholar 

  27. 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;106:2912–9.

    Article  CAS  Google Scholar 

  28. Escalante CP, Chisolm S, Song J, Richardson M, Salkeld E, Aoki E, et al. Fatigue, symptom burden, and health-related quality of life in patients with myelodysplastic syndrome, aplastic anemia, and paroxysmal nocturnal hemoglobinuria. Cancer Med. 2019;8:543–53.

    Article  Google Scholar 

  29. Abel GA, Buckstein R. Integrating frailty, comorbidity, and quality of life in the management of myelodysplastic syndromes. Am Soc Clin Oncol Educ Book. 2016;35:e337–44.

    Article  Google Scholar 

  30. Santini V. Treatment of low-risk myelodysplastic syndromes. Hematol Am Soc Hematol Educ Program. 2016;2016:462–9.

    Article  Google Scholar 

Download references


The authors gratefully acknowledge all patients who participated in GIMEMA-PROMYS Study for dedicating their time in completing quality of life questionnaires and having contributed in advancing knowledge in this research area. We also acknowledge the important contribution over the years to the coordination of the Data Management activities of Dr Francesco Sparano. LBO institutional affiliation has since changed to Health Outcomes and Behavior Program, Moffitt Cancer Center, Tampa, FL, USA.


Author LBO was funded by National Institutes of Health, National Cancer Institute training grant T32-CA-193193.

Author contributions

Conception and design: FE, FC, LBO, and DC. Data analysis and interpretation: all authors. Statistical analysis: FC, FE. Manuscript writing: FE, FC, LBO, and DC. Final approval of manuscript: all authors.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Fabio Efficace.

Ethics declarations

Conflict of interest

FE: Consultancy: Bristol-Myers Squibb, Amgen, Orsenix, Incyte, Takeda; Research funding to his insitution: Amgen. DC: President, AP: Advisory board: Sanofi, Amgen, Novartis. GAP: Honoraria: Jannsen, Novartis, Celgene. ML: Consultancy: Abbvie, Gilead Sci, Novartis, Daiichi Sankyo, MSD, Sanofi; Honoraria for speaking: Gilead Sci, Novartis. MB: Honoraria: Novartis, Pfizer, Incyte, Celgene. RS: Honoraria, Membership on an entity’s Board of Directors or advisory committees: Novartis, Celgene; Research funding: Teva. UP: Research funding and Honoraria: Celgene, Amgen, Janssen, Novartis. MV: personal fees: Pfizer, Amgen, Novartis. DP: received support for active participation on congresses from Roche and Sanofi Genzyme.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Efficace, F., Cottone, F., Oswald, L.B. et al. The IPSS-R more accurately captures fatigue severity of newly diagnosed patients with myelodysplastic syndromes compared with the IPSS index. Leukemia 34, 2451–2459 (2020).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links