Cancer related fatigue and the additive effect of treatment in the context of lymphoma:
An analysis of the Lymphoma Coalition’s 2022 Global Patient Survey
Running Title
CRF in Lymphoma Patients and Survivors
Authors and affiliations
Steve E. Kalloger1,2,3
, Amanda Watson1
, Shawn Sajkowski1
, Lorna Warwick1
- Lymphoma Coalition, Mississauga, ON Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada
- School of Population and Public Health, University of British Columbia, Vancouver Canada
Corresponding Author Contact Information
Steve E. Kalloger
10024 Harpoon Circle
Las Vegas, NV 89117
steve@lymphomacoalition.org
702-849-9996
Financial Support
This study was supported by funding from: AbbVie (Allergan), Bristol-Myers Squibb
(BMS), Allergan | Pharmacyclics, Roche (Roche Holding), AstraZeneca United States,
Eli Lilly and Company (Lilly), Incyte (Incyte Corp), Gilead | Kite Pharma, Novartis
(Novartis AG). None of the funders were involved with the data collection, study design,
analysis nor reporting of the study findings.
Conflict of Interest Statement
Steve E. Kalloger reports a stipend from Roche Canada unrelated to the presented
work. All remaining authors report no conflict of interest.
Abstract
Cancer related fatigue (CRF) continues to be a challenging phenomenon that is often
under-reported and poorly understood. With etiologies in both disease and treatment
manifesting as a symptom and a side-effect respectively, CRF is highly incident and
presents a significant clinical problem that impacts survivorship. We conducted a survey
to ascertain the patient reported incidence of symptoms and side-effects for people with
lymphoma or chronic lymphocytic leukemia (CLL). We found that CRF was enhanced
in those who received more intense therapies that coincided with more aggressive
lymphoma subtypes. This data illuminates an unmet need amongst patients with
lymphoma and provides an opportunity to further refine treatment regimens in order to
reduce the burden of CRF in this vulnerable population.
Significance Statement
Cancer related fatigue is a highly incident phenomenon in lymphoma that can be
ascribed to a combination of causes. We have demonstrated substantial variability
across various subtypes of lymphoma and have estimated that nearly half of the
reported fatigue comes from treatment. Increased screening for and monitoring of
fatigue will yield favorable health related quality of life that will benefit health technology
assessment activities and yield improved outcomes for patients.
Introduction
The impact of a cancer diagnosis on a patient presents a challenge from multiple
aspects. Aside from the disease, treatments add an additional layer of complexity to the
cancer journey that manifest in both physical and psychological detriments. (1) A highly
prevalent phenomenon amongst patients with hematologic malignancies is cancer
related fatigue (CRF). (2) The etiology of CRF can be attributed to both the disease and
its treatment. (3) Previous work has identified various clinical parameters that are
associated with fatigue which include: decreased performance status, use of
pharmaceutical pain management, gastrointestinal upset, lack of sleep, and abnormal
hemoglobin and albumin levels.(2,3). Other research has focused upon whether
psychological aspects drive or result in CRF. (4) Unfortunately, the directionality
remains elusive.
As evidenced by the inclusion of fatigue as a discrete domain of the recently developed
QLU-C10D, the importance of CRF in the health-related quality of life (HRQoL) of the
patient is gaining traction and will have direct impact on health technology assessment
activities. (5,6) Several studies have provided a variety of potential interventions for
those suffering from CRF. (7) Therefore, it is no longer considered to be an
unavoidable consequence of the cancer journey. However, identifying those at risk of
fatigue remains elusive. In this study, we sought to explore the prevalence of fatigue as
both a symptom and a side-effect amongst a large group of patients with lymphoma or
chronic lymphocytic leukemia (CLL). Our goal was to identify which lymphoma
subtypes had the highest levels of fatigue and to gain insights into the attributable
fraction that treatment adds to the mosaic of CRF.
Methods
Data Acquisition
The Lymphoma Coalition conducted a cross-sectional global survey on patient reported
outcomes (PRO’s) and patient reported experience measures (PREM’s). As part of the
survey, patients were asked to report on the incidence of symptoms of their disease and
the side-effects associated with treatment. Fatigue was a specific domain in both
questions. Additionally, patients provided demographic information, their lymphoma
subtype (where known), and if they received a medical intervention as part of the care
for their lymphoma or CLL. A full factorial approach was used to categorize patients
into those who reported fatigue as a symptom, side-effect or both (Figure 1).
Statistical Modelling
The symptom/side-effect strata for each subtype was subjected to nominal logistic
regression to investigate if age and biological sex were significantly associated with
fatigue A p-value of <0.05 was considered statistically significant. All analyses were
computed with JMP Pro v17.2 (SAS Institute, Cary NC, USA).
Estimation of CRF Attributable to Treatment
The fraction of fatigue that is amplified by treatment (fSE) was estimated by the following
equation:
Equation 1.
𝑓𝑆𝐸 = (𝐶+𝐷)/(𝐴+𝐵+𝐶+𝐷)
Where A is the number of people who did not report fatigue, B those who reported
fatigue as a symptom only, C are those who reported fatigue as a side-effect only, and
D those who reported fatigue as both a symptom and a side-effect.
Data Availability
The data generated in this study are available upon request from the corresponding
author.
Results
Evaluable responses were received from 71 countries with France, The United States,
China, Italy, Australia and The United Kingdom of Great Britain and Northern Ireland.
Accounting for 65% of the cohort. Of the 6758 survey respondents with evaluable data,
13 subtypes were represented with two categories for other indolent or aggressive
lymphomas respectively. The highest number of responses came from those with CLL
(N = 1222), follicular lymphoma (N= 1071) and diffuse large B-cell lymphoma (DLBCL)
(N = 906). Demographics for the symptom/side-effect strata are displayed in Table 1.
The majority of respondents for all but one subtype category reported fatigue as both a
symptom and a side-effect of treatment (33% – 49%). Table S1 provides a summary of
the distribution of responses across the symptom/side-effect strata. Approximately 24%
of the cohort was excluded from the calculation of treatment related fatigue, because
these patients did not receive treatment.
Covariate associations with fatigue
Utilizing the three strata that received treatment and reported an incidence of fatigue,
nominal logistic regression analysis revealed that the combination of predictors age and
biological sex yielded a significant predictive effect for DLBCL, follicular lymphoma and
mantle cell lymphoma (p <= 0.02). All other subtypes failed to achieve statistical
significance for this set of predictors. Examining the predictive effect of each predictor
in each subtype demonstrated that females are more likely to experience fatigue as a
symptom and side-effect relative to males in DLCBL (Figure 2A and B) (p<0.0001).
This finding was also shown in follicular lymphoma but just fell short of statistical
significance (Figure 2C and D) (p = 0.05). Increasing age was also found to be
predictive of respondents reporting fatigue as both a symptom and side-effect in
follicular lymphoma (p = 0.03) and mantle cell lymphoma (p = 0.004). The effect of age
in mantle cell was amplified relative to the other subtypes with respect to the tradeoff
between fatigue reported as a side-effect only in younger patients and as both a
symptom and side-effect in older patients. As an example, a 30-year-old male would
have a probability of reporting fatigue as a side-effect only of 0.59 where a 60 year old
male would see a probability of reporting fatigue as a side-effect only of 0.13 and an
inflation of fatigue as both a symptom and side-effect to 0.73. This is an increase in
probability of 0.4 (Figure 2E and F).
Amplification of fatigue with treatment
Across all subtypes, the mean percentage of fatigue attributable to treatment was 34%
and ranged from 0% – 45% (Figure 3). Hodgkin, DLBCL, breast implant associated
analplastic large cell, mantle cell, peripheral T-Cell, and follicular lymphomas are
estimated to have over 40% of CRF attributed to treatment.
Discussion
The data in this study indicates that CRF is a highly prevalent phenomenon in
lymphoma and CLL. The main issue regarding the drivers of CRF remains elusive due
to the fact that it exists both as a symptom of disease and a side-effect of treatment.
While it can be argued that if CRF is a symptom of disease and that an efficacious
treatment should relieve CRF as a symptom, the side-effects of treatment have the
potential to supplant and possibly overshadow any symptom relief. To address this
issue more directly, we will be changing our question format in the Lymphoma
Coalition’s 2024 Global Patient Survey for symptoms and side-effects to scalar
measures which may help to identify if CRF intensifies or declines with treatment.
Surprisingly, lymphoma subtypes that are more aggressive tended to have higher levels
of CRF both as a symptom and as a side-effect. This gives credence to the theory that
those who are experiencing CRF may be predisposed to experiencing CRF as a sideeffect. While the exact etiology of CRF remains a subject of debate, it undoubtedly
encompasses both biological and psychological causal mechanisms. Our data has
demonstrated that females tend to experience CRF as both a symptom and side-effect
more frequently and that increasing age is a predictor of the same in some lymphoma
subtypes. While these findings reflect those found in previous studies, the majority of
lymphoma subtypes represented in our study suggest that other causal mechanisms
are at play and clearly illustrates the importance of accounting for subtype in
PRO/PREM based studies. A limitation of this study is related to the argument that
CRF attributed to treatment may be transitory in nature. Due to our study design, we
are unable to follow patients longitudinally. However, a study by Rüffer et al. has
demonstrated that patients with Hodgkin lymphoma, who have encountered treatment
continue to experience significant CRF several years after treatment while in remission.
(8) This finding provides evidence that CRF is unlikely to be a transitory phenomenon
in Hodgkin lymphoma and further research would serve to confirm if this is the case for
other subtypes.
With approximately two-thirds of our survey respondents reporting CRF, it can be
concluded that CRF is a significant unmet need that impacts survivorship for those with
lymphoma or CLL. The impact of CRF negatively affects several physical and
psychological domains quantified in health related quality of life (HRQoL) studies.(9,10)
In prospective studies evaluating the novel therapeutics in lymphoma, it will be
imperative to identify and provide treatment to those who experience CRF. The
guidelines provided by the National Comprehensive Cancer Network (NCCN) are a
place to start, as they recognize three discrete stages of the cancer journey: treatment,
recovery, and end of life. (11) Although the suggested interventions differ only slightly
between the stages, they have all proven to be effective. Implementation of these
guidelines with continued additions and refinements will not only provide the optimal
generation of evidence for health technology assessment activities but also improve the
lives of patients with the disease.
In summary, the data contained in this study suggest that CRF should be continuously
assessed in patients with lymphoma and CLL and that once identified, they should be
treated with one or more of many efficacious options.(12) We advocate for the
expansion of the definition of precision medicine to include not only the provision of
optimal anti-cancer therapy to the right patients but to also ensure that patients receive
the appropriate supportive care. To do so will ensure that patients with lymphoma have
the best experience possible during their treatment, recovery, and rehabilitation.
Acknowledgements
We would like to thank our community of patients and their advocates for taking the time
to complete the Global Patient Survey. It is only through your eyes can we better
understand the experiences of patients. Additionally, we would like to thank the
reviewers for their insightful comments that helped to improve the quality of this
manuscript. All errors and omissions are our own.
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