Determining the minimal clinically important difference criteria for the Multidimensional Fatigue Inventory in a radiotherapy population

Abstract

Purpose

The Multidimensional Fatigue Inventory (MFI) is a commonly used cancer-related fatigue assessment tool. Unlike other fatigue assessments, there are no published minimal clinically important difference (MCID) criteria for the MFI in cancer populations. MCID criteria determine the smallest change in scores that can be regarded as important, allowing clinicians and researchers to interpret the meaning of changes in patient’s fatigue scores. This research aims to improve the clinical utility of the MFI by establishing MCID criteria for the MFI sub-scales in a radiotherapy population.

Materials and methods

Two hundred ten patients undergoing radiotherapy were recruited to a single-centre prospective cohort study. Patients were assessed at three time points, at the start of radiotherapy, the end of radiotherapy and 6 weeks after radiotherapy completion. Assessment consisted of four clinically relevant constructs: (1) treatment impact on fatigue, (2) health-related quality of life, (3) performance status and (4) occupational productivity. These constructs were used as external or anchor-based measures to determine MCIDs for each sub-scale of the MFI.

Results

Multiple MCIDs were identified, each from a different perspective based on the constructs cited. Researchers seeking to use a generic MCID may wish to use a two-point reference for each MFI sub-scale as it was consistent across the pre- and post-radiotherapy comparison and occupational productivity anchors.

Conclusions

MCIDs validated in this study allow better interpretation of changes in MFI sub-scale scores and allow effect size calculations for determining sample size in future studies.

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

Fig. 1

Abbreviations

MFI:

Multidimensional Fatigue Inventory

MCID:

minimal clinically important difference

References

  1. 1.

    Abernethy AP, Shelby-James T, Fazekas BS, Woods D, Currow DC (2005) The Australia-modified Karnofsky Performance Status (AKPS) scale: a revised scale for contemporary palliative care clinical practice [ISRCTN81117481]. BMC Palliat Care 4:7. doi:10.1186/1472-684X-4-7

    Article  PubMed  Google Scholar 

  2. 2.

    Ahlberg K, Ekman T, Gaston-Johansson F (2004) Levels of fatigue compared to levels of cytokines and hemoglobin during pelvic radiotherapy: a pilot study. Biol Res Nurs 5:203–210. doi:10.1177/1099800403259500

    Article  PubMed  Google Scholar 

  3. 3.

    Ahlberg K, Ekman T, Gaston-Johansson F (2005) The experience of fatigue, other symptoms and global quality of life during radiotherapy for uterine cancer. Int J Nurs Stud 42:377–386. doi:10.1016/j.ijnurstu.2004.07.008

    Article  PubMed  Google Scholar 

  4. 4.

    Cella D, Eton DT, Lai J-S, Peterman AH, Merkel DE (2002) 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 Manage 24:547–561. doi:10.1016/S0885-3924(02)00529-8

    Article  PubMed  Google Scholar 

  5. 5.

    Crosby RD, Kolotkin RL, Williams GR (2003) Defining clinically meaningful change in health-related quality of life. J Clin Epidemiol 56:395–407. doi:10.1016/S0895-4356(03)00044-1

    Article  PubMed  Google Scholar 

  6. 6.

    Curt GA, Breitbart W, Cella D, Groopman JE, Horning SJ, Itri LM et al (2000) Impact of cancer-related fatigue on the lives of patients: new findings from the Fatigue Coalition. Oncologist 5:353–360. doi:10.1634/theoncologist.5-5-353

    Article  CAS  PubMed  Google Scholar 

  7. 7.

    Drummond M, O’Brien B, Stoddart G, Torrance G (1993) Methods for the economic evaluation of health care programmes. Oxford Medical, New York

    Google Scholar 

  8. 8.

    Ericsson A, Mannerkorpi K (2007) Assessment of fatigue in patients with fibromyalgia and chronic widespread pain. Reliability and validity of the Swedish version of the MFI-20. Disabil Rehabil 29:1665–1670

    Article  PubMed  Google Scholar 

  9. 9.

    Furst CJ, Ahsberg E (2001) Dimensions of fatigue during radiotherapy. An application of the multidimensional fatigue inventory. Support Care Cancer 9:355–360. doi:10.1007/s005200100242

    Article  CAS  PubMed  Google Scholar 

  10. 10.

    Goligher EC, Pouchot J, Brant R, Kherani RB, Avina-Zubieta JA, Lacaille D et al (2008) Minimal clinically important difference for 7 measures of fatigue in patients with systemic lupus erythematosus. J Rheumatol 35:635–642

    PubMed  Google Scholar 

  11. 11.

    Hofman M, Ryan JL, Figueroa-Moseley CD, Jean-Pierre P, Morrow GR (2007) Cancer-related fatigue: the scale of the problem. Oncologist 12:4–10. doi:10.1634/theoncologist.12-S1-4

    Article  PubMed  Google Scholar 

  12. 12.

    Jaeschke R, Singer J, Guyatt GH (1989) Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials 10:407–415. doi:10.1016/0197-2456(89)90005-6

    Article  CAS  PubMed  Google Scholar 

  13. 13.

    Jean-Pierre P, Figueroa-Moseley CD, Kohli S, Fiscella K, Palesh OG, Morrow GR (2007) Assessment of cancer-related fatigue: implications for clinical diagnosis and treatment. Oncologist 12(Suppl 1):11–21. doi:10.1634/theoncologist.12-S1-11

    Article  PubMed  Google Scholar 

  14. 14.

    Jereczek-Fossa BA, Marsiglia HR, Orecchia R (2002) Radiotherapy-related fatigue. Crit Rev Oncol Hematol 41:317–325. doi:10.1016/S1040-8428(01)00143-3

    Article  PubMed  Google Scholar 

  15. 15.

    Meek PM, Nail LM, Barsevick A, Schwartz AL, Stephen S, Whitmer K et al (2000) Psychometric testing of fatigue instruments for use with cancer patients. Nurs Res 49:181–190. doi:10.1097/00006199-200007000-00001

    Article  CAS  PubMed  Google Scholar 

  16. 16.

    Mor V, Laliberte L, Morris JN, Wiemann M (1984) The Karnofsky performance status scale. An examination of its reliability and validity in a research setting. Cancer 53:2002–2007. doi:10.1002/1097-0142(19840501)53:9<2002::AID-CNCR2820530933>3.0.CO;2-W

    Article  CAS  PubMed  Google Scholar 

  17. 17.

    National Comprehensive Cancer Network (2007) Clinical practice guidelines in oncology: cancer-related fatigue 4. http://www.nccn.org/professionals/physician_gls/PDF/fatigue.pdf. Accessed 20 December 2007

  18. 18.

    Pickard AS, Neary MP, Cella D (2007) Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer. Health Qual Life Outcomes 5:70. doi:10.1186/1477-7525-5-70

    Article  PubMed  Google Scholar 

  19. 19.

    Pickard AS, Wilke CT, Lin HW, Lloyd A (2007) Health utilities using the EQ-5D in studies of cancer. Pharmacoeconomics 25:365–384. doi:10.2165/00019053-200725050-00002

    Article  PubMed  Google Scholar 

  20. 20.

    Portney L, Watkins M (2009) Foundations of clinical research. Pearson Prentice Hall, Upper Saddle River, pp 619–658

    Google Scholar 

  21. 21.

    Pouchot J, Kherani RB, Brant R, Lacaille D, Lehman AJ, Ensworth S et al (2008) Determination of the minimal clinically important difference for seven fatigue measures in rheumatoid arthritis. J Clin Epidemiol 61:705–713

    Article  PubMed  Google Scholar 

  22. 22.

    Rabin R, de Charro F (2001) EQ-5D: a measure of health status from the EuroQol Group. Ann Med 33:337–343. doi:10.3109/07853890109002087

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Rejas J, Pardo A, Ruiz MA (2008) Standard error of measurement as a valid alternative to minimally important difference for evaluating the magnitude of changes in patient-reported outcomes measures. J Clin Epidemiol 61:350–356. doi:10.1016/j.jclinepi.2007.05.011

    Article  PubMed  Google Scholar 

  24. 24.

    Sloan J, Symonds T, Vargas-Chanes D, Fridley B (2003) Practical guidelines for assessing significance of health-related quality of life changes in clinical trials. Drug Inf J 37:23–31

    Google Scholar 

  25. 25.

    Smets EM, Garssen B, Bonke B, De Haes JC (1995) The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res 39:315–325. doi:10.1016/0022-3999(94)00125-O

    Article  CAS  PubMed  Google Scholar 

  26. 26.

    Smets E, Visser MR, Willems-Groot AF, Garssen B, Oldenburger F, van Tienhoven G et al (1998) Fatigue and radiotherapy: (A) experience in patients undergoing treatment. Br J Cancer 78:899–906

    CAS  PubMed  Google Scholar 

  27. 27.

    Smets E, Visser MR, Willems-Groot AF, Garssen B, Schuster-Uitterhoeve AL, de Haes JC (1998) Fatigue and radiotherapy: (B) experience in patients 9 months following treatment. Br J Cancer 78:907–912

    CAS  PubMed  Google Scholar 

  28. 28.

    Strauss B, Brix C, Fischer S, Leppert K, Fuller J, Roehrig B et al (2007) The influence of resilience on fatigue in cancer patients undergoing radiation therapy (RT). J Cancer Res Clin Oncol 133:511–518. doi:10.1007/s00432-007-0195-z

    Article  PubMed  Google Scholar 

  29. 29.

    Turriziani A, Mattiucci GC, Montoro C, Ferro M, Maurizi F, Smaniotto D et al (2005) Radiotherapy-related fatigue: incidence and predictive factors. Rays 30:197–203

    PubMed  Google Scholar 

  30. 30.

    van Roijen L, Essink-Bot ML, Koopmanschap MA, Bonsel G, Rutten FF (1996) Labor and health status in economic evaluation of health care. The Health and Labor Questionnaire. Int J Technol Assess Health Care 12:405–415

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the generous funding provided by the Queensland Health Cancer Clinical Network and the Princess Alexandra Hospital Cancer Collaborative Group.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Amanda Purcell.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Purcell, A., Fleming, J., Bennett, S. et al. Determining the minimal clinically important difference criteria for the Multidimensional Fatigue Inventory in a radiotherapy population. Support Care Cancer 18, 307–315 (2010). https://doi.org/10.1007/s00520-009-0653-z

Download citation

Keywords

  • Cancer-related fatigue
  • Minimal clinically important difference
  • Multidimensional fatigue inventory
  • Utility
  • Anchor-based methods
  • Distribution-based methods