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Identification of distinct fatigue trajectories in patients with breast cancer undergoing adjuvant chemotherapy

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Abstract

Purpose

The goal of this study was to characterize changes in daily fatigue in women undergoing chemotherapy for breast cancer. We examined whether there are subgroups of patients with distinct fatigue trajectories and explored potential psychosocial and biomedical predictors of these subgroups.

Methods

Participants were 77 women with breast cancer receiving adjuvant chemotherapy with AC-T (2-week cycle) and TC or TCH (3-week cycle) regimens. They completed 28 daily ratings online using an adapted version of the Patient-Reported Outcomes Measurement Information System (PROMIS®) fatigue instrument.

Results

Both regimens followed an “inverted-U-shaped” fatigue pattern over approximately 2 weeks. Growth mixture modeling identified three patient subgroups with distinct trajectories. Fatigue scores in the “low fatigue” group (23 %) increased following the infusion and quickly abated. The “transient fatigue” (27 %) group had a very pronounced increase. Patients in the “high fatigue” (50 %) group reported consistently elevated fatigue with a relatively small increase. Demographic and medical variables were not associated with fatigue trajectory. Patients in the “high fatigue” group reported significantly poorer physical, emotional, and social functioning, poorer general health, and more depressed mood than patients in the “low fatigue” group. The “transient fatigue” group reported significantly better physical and social functioning than the “high fatigue” group, but emotional distress and depression similar to the “high fatigue” group.

Conclusions

The identification of patient subgroups with distinct fatigue trajectories during chemotherapy is an essential step for developing preventative strategies and tailored interventions. Our results suggest that different trajectories are associated with patients’ psychosocial and general health.

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Notes

  1. The equation used is as follows: y = β 0 + β 1 sin (2πt/14) + β 2 cos (2πt/14), where t represents the day of the cycle, β 0 is an intercept (representing the “overall” fatigue level), and β 1 and β 2 express amplitude (magnitude of increase and subsequent decrease) and phase shift (the “timing”) of sinusoidal change.

References

  1. Mock V, Atkinson A, Barsevick A, Cella D, Cimprich B, Cleeland C et al (2000) NCCN Practice Guidelines for Cancer-Related Fatigue. Oncology 14:151–161

    CAS  PubMed  Google Scholar 

  2. 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

    Article  CAS  PubMed  Google Scholar 

  3. Tiedtke C, de Rijk A, Dierckx de Casterle B, Christiaens MR, Donceel P (2010) Experiences and concerns about ‘returning to work’ for women breast cancer survivors: a literature review. Psychooncology 19:677–683

    Article  PubMed  Google Scholar 

  4. Gupta D, Lis CG, Grutsch JF (2007) The relationship between cancer-related fatigue and patient satisfaction with quality of life in cancer. J Pain Symptom Manag 34:40–47

    Article  Google Scholar 

  5. Servaes P, Verhagen C, Bleijenberg G (2002) Fatigue in cancer patients during and after treatment: prevalence, correlates and interventions. Eur J Cancer 38:27–43

    Article  CAS  PubMed  Google Scholar 

  6. Tiesinga LJ, Dassen TW, Halfens RJ (1996) Fatigue: a summary of the definitions, dimensions, and indicators. Nurs Diagn 7:51–62

    Article  CAS  PubMed  Google Scholar 

  7. Magnusson K, Moller A, Ekman T, Wallgren A (1999) A qualitative study to explore the experience of fatigue in cancer patients. Eur J Cancer Care 8:224–232

    Article  CAS  Google Scholar 

  8. Jacobsen PB, Hann DM, Azzarello LM, Horton J, Balducci L, Lyman GH (1999) Fatigue in women receiving adjuvant chemotherapy for breast cancer: characteristics, course, and correlates. J Pain Symptom Manag 18:233–242

    Article  CAS  Google Scholar 

  9. Greene D, Nail LM, Fieler VK, Dudgeon D, Jones LS (1994) A comparison of patient-reported side effects among three chemotherapy regimens for breast cancer. Cancer Pract 2:57–62

    CAS  PubMed  Google Scholar 

  10. Andrykowski MA, Schmidt JE, Salsman JM, Beacham AO, Jacobsen PB (2005) Use of a case definition approach to identify cancer-related fatigue in women undergoing adjuvant therapy for breast cancer. J Clin Oncol 23:6613–6622

    Article  PubMed Central  PubMed  Google Scholar 

  11. Lawrence DP, Kupelnick B, Miller K, Devine D, Lau J (2004) Evidence report on the occurrence, assessment, and treatment of fatigue in cancer patients. J Nat Cancer Inst Monog 40-50.

  12. Berger AM, Higginbotham P (2000) Correlates of fatigue during and following adjuvant breast cancer chemotherapy: a pilot study. Oncol Nurs Forum 27:1443–1448

    CAS  PubMed  Google Scholar 

  13. de Jong N, Kester ADM, Schouten HC, Abu-Saad HH, Courtens AM (2006) Course of fatigue between two cycles of adjuvant chemotherapy in breast cancer patients. Cancer Nurs 29:467–477

    Article  PubMed  Google Scholar 

  14. Badr H, Basen-Engquist K, Carmack Taylor CL, De Moor C (2006) Mood states associated with transitory physical symptoms among breast and ovarian cancer survivors. J Behav Med 29:461–475

    Article  PubMed  Google Scholar 

  15. Jim HSL, Small B, Faul LA, Franzen J, Apte S, Jacobsen PB (2011) Fatigue, depression, sleep, and activity during chemotherapy: daily and intraday variation and relationships among symptom changes. Ann Behav Med 42:321–333

    Article  PubMed Central  PubMed  Google Scholar 

  16. Passik SD, Kirsh KL, Donaghy K, Holtsclaw E, Theobald D, Cella D et al (2002) Patient-related barriers to fatigue communication: initial validation of the fatigue management barriers questionnaire. J Pain Symptom Manag 24:481–493

    Article  Google Scholar 

  17. Hilarius DL, Kloeg PH, van der Wall E, Komen M, Gundy CM, Aaronson NK (2011) Cancer-related fatigue: clinical practice versus practice guidelines. Support Care Cancer 19:531–538

    Article  PubMed Central  PubMed  Google Scholar 

  18. Koornstra RH, Peters M, Donofrio S, van den Borne B, de Jong FA (2014) Management of fatigue in patients with cancer—a practical overview. Cancer Treat Rev 40:791–799

    Article  PubMed  Google Scholar 

  19. de Jong N, Courtens AM, Abu-Saad HH, Schouten HC (2002) Fatigue in patients with breast cancer receiving adjuvant chemotherapy: a review of the literature. Cancer Nurs 25:283–297

    Article  PubMed  Google Scholar 

  20. Jacobsen PB (2004) Assessment of fatigue in cancer patients. J Natl Cancer Inst Monogr 32:93–97

    Article  PubMed  Google Scholar 

  21. Prue G, Rankin J, Allen J, Gracey J, Cramp F (2006) Cancer-related fatigue: a critical appraisal. Eur J Cancer 42:846–863

    Article  CAS  PubMed  Google Scholar 

  22. Watson T, Mock V (2004) Exercise as an intervention for cancer-related fatigue. Phys Ther 84:736–743

    PubMed  Google Scholar 

  23. Christodoulou C, Schneider S, Junghaenel DU, Broderick JE, Stone AA (2014) Measuring daily fatigue using a brief scale adapted from the Patient-Reported Outcomes Measurement Information System (PROMIS(R)). Qual Life Res 23:1245–1253

    Article  PubMed  Google Scholar 

  24. Christodoulou C, Junghaenel DU, DeWalt DA, Rothrock N, Stone AA (2008) Cognitive interviewing in the evaluation of fatigue items: results from the patient-reported outcomes measurement information system (PROMIS). Qual Life Res 17:1239–1246

    Article  PubMed Central  PubMed  Google Scholar 

  25. Riley WT, Rothrock N, Bruce B, Christodoulou C, Cook K, Hahn EA et al (2010) Patient-reported outcomes measurement information system (PROMIS) domain names and definitions revisions: further evaluation of content validity in IRT-derived item banks. Qual Life Res 19:1311–1321

    Article  PubMed Central  PubMed  Google Scholar 

  26. DeWalt DA, Rothrock N, Yount S, Stone AA (2007) Evaluation of item candidates: the PROMIS qualitative item review. Med Care 45:S12–S21

    Article  PubMed Central  PubMed  Google Scholar 

  27. Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA et al (2007) Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care 45:S22–S31

    Article  PubMed  Google Scholar 

  28. Lai JS, Cella D, Choi S, Junghaenel DU, Christodoulou C, Gershon R et al (2011) How item banks and their application can influence measurement practice in rehabilitation medicine: a PROMIS fatigue item bank example. Arch Phys Med Rehabil 92:S20–S27

    Article  PubMed Central  PubMed  Google Scholar 

  29. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S et al (2010) The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol 63:1179–1194

    Article  PubMed Central  PubMed  Google Scholar 

  30. Cook KF, Bamer AM, Amtmann D, Molton IR, Jensen MP (2012) Six patient-reported outcome measurement information system short form measures have negligible age- or diagnosis-related differential item functioning in individuals with disabilities. Arch Phys Med Rehabil 93:1289–1291

    Article  PubMed  Google Scholar 

  31. Rothrock NE, Hays RD, Spritzer K, Yount SE, Riley W, Cella D (2010) Relative to the general US population, chronic diseases are associated with poorer health-related quality of life as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS). J Clin Epidemiol 63:1195–1204

    Article  PubMed Central  PubMed  Google Scholar 

  32. Junghaenel DU, Christodoulou C, Lai JS, Stone AA (2011) Demographic correlates of fatigue in the US general population: results from the Patient-Reported Outcomes Measurement Information System (PROMISA) initiative. J Psychosom Res 71:117–123

    Article  PubMed Central  PubMed  Google Scholar 

  33. Schneider S, Choi SW, Junghaenel DU, Schwartz JE, Stone AA (2013) Psychometric characteristics of daily diaries for the Patient-Reported Outcomes Measurement Information System (PROMIS(R)): a preliminary investigation. Qual Life Res 22:1859–1869

    Article  PubMed Central  PubMed  Google Scholar 

  34. Hays RD, Bjorner JB, Revicki DA, Spritzer KL, Cella D (2009) Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Qual Life Res 18:873–880

    Article  PubMed Central  PubMed  Google Scholar 

  35. Revicki DA, Kawata AK, Harnam N, Chen WH, Hays RD, Cella D (2009) Predicting EuroQol (EQ-5D) scores from the patient-reported outcomes measurement information system (PROMIS) global items and domain item banks in a United States sample. Qual Life Res 18:783–791

    Article  PubMed Central  PubMed  Google Scholar 

  36. Jung T, Wickrama KAS (2008) An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Compass 2:302–317

    Article  Google Scholar 

  37. Muthén B, Muthén LK (2000) Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 24:882–891

    Article  PubMed  Google Scholar 

  38. Reinecke J, Seddig D (2011) Growth mixture models in longitudinal research. Asta-Adv Stat Anal 95:415–434

    Article  Google Scholar 

  39. McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York

    Book  Google Scholar 

  40. Schafer JL, Graham JW (2002) Missing data: our view of the state of the art. Psychol Methods 7:147–177

    Article  PubMed  Google Scholar 

  41. Wang CP, Brown CH, Bandeen-Roche K (2005) Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. J Am Stat Assoc 100:1054–1076

    Article  CAS  Google Scholar 

  42. Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York

    Book  Google Scholar 

  43. Muthén LK, Muthén BO (1998-2012) Mplus user’s guide, 7th ed. Los Angeles, CA: Muthén & Muthén

  44. Bower JE, Ganz PA, Desmond KA, Rowland JH, Meyerowitz BE, Belin TR (2000) Fatigue in breast cancer survivors: Occurrence, correlates, and impact on quality of life. J Clin Oncol 18:743–753

    CAS  PubMed  Google Scholar 

  45. Liu LQ, Fiorentino L, Natarajan L, Parker BA, Mills PJ, Sadler GR et al (2009) Pretreatment symptom cluster in breast cancer patients is associated with worse sleep, fatigue and depression during chemotherapy. Psychooncology 18:187–194

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

This research was supported by a grant from the National Institutes of Health (NIH 1-U01AR057948-01). We thank our participants and Gim Yen Toh, Laura Wolff, Lauren Cody, and Linda Mahler for their assistance. PROMIS® was funded with cooperative agreements from the NIH Common Fund Initiative (U54AR057951, U01AR052177, U54AR057943, U54AR057926, U01AR057948, U01AR052170, U01AR057954, U01AR052171, U01AR052181, U01AR057956, U01AR052158, U01AR057929, U01AR057936, U01AR052155, U01AR057971, U01AR057940, U01AR057967, U01AR052186). The contents of this article use data developed under PROMIS. These contents do not necessarily represent an endorsement by the US Federal Government or PROMIS (see www.nihpromis.org for additional information).

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Authors and Affiliations

  1. Dornsife Center for Self-Report Science and Center for Economic and Social Research, University of Southern California, Verna & Peter Dauterive Hall, 635 Downey Way, Los Angeles, CA, 90089-3332, USA

    Doerte U. Junghaenel, Stefan Schneider & Joan E. Broderick

  2. School of Medicine, Stony Brook University, Stony Brook, NY, USA

    Jules Cohen & Anu R. Neerukonda

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  1. Doerte U. Junghaenel
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  2. Jules Cohen
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  3. Stefan Schneider
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  5. Joan E. Broderick
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Correspondence to Doerte U. Junghaenel.

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Junghaenel, D.U., Cohen, J., Schneider, S. et al. Identification of distinct fatigue trajectories in patients with breast cancer undergoing adjuvant chemotherapy. Support Care Cancer 23, 2579–2587 (2015). https://doi.org/10.1007/s00520-015-2616-x

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  • DOI: https://doi.org/10.1007/s00520-015-2616-x

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