Download Fatigue in Ambulatory Patients with Advanced Lung Cancer

554
Journal of Pain and Symptom Management
Vol. 22 No. 1 July 2001
Original Article
Fatigue in Ambulatory Patients with Advanced
Lung Cancer: Prevalence, Correlated Factors,
and Screening
Toru Okuyama, MD, PhD, Keiko Tanaka, MD, Tatsuo Akechi, MD, PhD,
Akira Kugaya, MD, PhD, Hitoshi Okamura, MD, PhD, Yutaka Nishiwaki, MD,
Takashi Hosaka, MD, PhD, and Yosuke Uchitomi, MD, PhD
Psycho-Oncology Division (T.O., K.T., T.A., H.O., Y.U.), National Cancer Center Research Institute
East, Kashiwa; Department of Psychiatry and Behavioral Science (T.O., T.H.), Tokai University School
of Medicine Boseidai, Isehara; Thoracic Oncology Division (K.T., Y.N.) and Psychiatry Division (A.K.,
H.O., Y.U.), National Cancer Center Hospital East, Kashiwa; and Psychiatry Division (T.A.),
National Cancer Center Hospital, Tokyo, Japan
Abstract
Although it has been indicated that patients with lung cancer experience higher level of fatigue
than patients with other cancers, few published studies have focused on the characteristics of
this fatigue and how it interferes with daily activities. The purpose of this study was to clarify
fatigue prevalence and the factors correlated with fatigue, and to develop a screening method
for fatigue in patients with advanced lung cancer. One hundred fifty-seven patients completed
two fatigue scales (Cancer Fatigue Scale [CFS], and Fatigue Numerical Scale [FNS]) plus
other measures, along with a self-administered questionnaire asking whether fatigue had
interfered with any of 7 areas of daily activities. Fifty-nine percent of patients had experienced
clinical fatigue, which was defined as fatigue that interfered with any daily activities. Logistic
regression analysis demonstrated that symptoms of dyspnea on walking, appetite loss, and
depression were significant correlated factors. Both CFS and FNS were found to have sufficient
sensitivity and specificity for use as a screening tool. The results indicated that fatigue is a
frequent and important symptom, which is associated with both physical and psychological
distress in this population. The CFS and FNS were confirmed to have sufficient screening
ability. J Pain Symptom Manage 2001;22:554–564. © U.S. Cancer Pain Relief
Committee, 2001.
Key Words
Fatigue, lung cancer, pain, dyspnea, depression, QOL, screening, symptom management,
Cancer Fatigue Scale
Address reprint requests to: Yosuke Uchitomi, MD, PhD,
Psycho-Oncology Division, National Cancer Center
Research Institute East, 6-5-1, Kashiwanoha, Kashiwa, Chiba 277-8577, Japan.
Accepted for publication: October 23, 2000.
© U.S. Cancer Pain Relief Committee, 2001
Published by Elsevier, New York, New York
Introduction
Fatigue has been recognized as one of the
most frequent symptoms experienced by patients with cancer.1–4 In addition, fatigue is also
experienced following various active treat0885-3924/01/$–see front matter
PII S0885-3924(01)00305-0
Vol. 22 No. 1 July 2001
Fatigue in Lung Cancer Patients
ments such as surgery,5 chemotherapy,6 radiotherapy,7 and bone marrow transplantation.8
Activities and quality of life (QOL) in cancer
patients is deleteriously affected by fatigue. Ferrell et al.,9 in a descriptive study investigating
the impact of fatigue on cancer patients, found
that fatigue interfered with physical, psychological, social, and spiritual dimensions of well-being. Vogelzang et al.10 conducted a telephone
survey of 419 patients with a variety of cancers
and reported that the impact of fatigue on daily
routines was rated as significant by 32% of patients and as moderate by 39% of patients. Recently, in a study of patients with various cancers, Ashbury et al.11 reported that fatigue was
the most frequent symptom, and was most likely
to interfere with their normal daily activities.
Lung cancer is the most frequent cause of
cancer death in the world and is one of the
most prevalent cancers.12 In Japan, the incidence of lung cancer is increasing, and was the
cause of death in approximately 51,000 people
in 1998. For all cancer mortality in Japan, lung
cancer is the commonest cause and accounts
for 18% of all cancer deaths.13 Advanced lung
cancer (III, IV) is difficult to cure (survival rate
within 5 years of diagnosis is about 7% and 1%,
respectively14), and it is important to improve
the QOL in this patient population.
Some studies have indicated that fatigue is a
critical problem in lung cancer patients. One
study, looking at the findings from 2390 cancer
patients in 10 clinical trials, has reported that
patients with lung or ovarian cancer experienced greater fatigue than those with other
cancers.15 Other studies have shown that fatigue was one of the most distressing symptoms
in 60 women with advanced lung cancer,16 that
there was a significant correlation between fatigue and QOL in 127 patients with small-cell
lung cancer,17 and that fatigue was associated
with depression in three palliative treatment
trials in 987 lung cancer patients.18 On the
other hand, a small study, conducted in 26
lung cancer patients, reported that although
fatigue was perceived as the highest intensity
symptom, it was ranked as the second lowest in
importance compared with other symptoms.19
Only a few studies have focused on fatigue in
this population. Hickok et al. conducted a retrospective review of medical records in 50 lung
cancer patients receiving radiotherapy, and
found that fatigue is a frequent symptom
555
(78%) and is not correlated with either disease
or treatment variables.20 However, this study
had serious methodological flaws, in that symptoms, including fatigue, were assessed retrospectively and objectively, rather than subjectively using a valid assessment method. In
addition the number of participants was small.
Blesch et al. reported that pain and mood, as
measured by Profile of Mood States, were significantly correlated with fatigue in lung cancer patients,21 although the use of a convenient
and small sample (n 33) limited the interpretation, and symptoms other than pain and
sleep disturbance were not assessed. Furthermore, neither of these studies assessed interference with daily life due to fatigue.
In summary, many patients with lung cancer
experience fatigue as a distressing symptom.
However, no well-designed studies have yet
been conducted in this population investigating the prevalence of fatigue, factors correlated with this symptom, and how fatigue interferes with daily activities. This cross-sectional
study was conducted to determine 1) the prevalence of patients with advanced lung cancer
who had interference with daily activity due to
fatigue, 2) the correlated factors of such patients, and 3) methods to detect such patients.
Fatigue in advanced lung cancer patients was
assessed using two fatigue scales (Cancer Fatigue Scale and Fatigue Numerical Scale)
along with a self-administered questionnaire
asking whether fatigue had interfered with any
of 7 domains of their daily activities. In addition, a broad range of biopsychosocial factors,
including cancer information and previous
anti-cancer treatment history, psychological
distress, and demographical and social support
status were assessed using a review of medical
records, a self-administered questionnaire, and
a structured interview.
Methods
Subjects
The study subjects were consecutive ambulatory patients with advanced lung cancer at the
Thoracic Oncology Division of the National
Cancer Center Hospital East, and the National
Cancer Center Hospital, Japan. The eligibility
criteria for patients were a) to have clinically in
advanced stage of disease (clinical stage; unre-
556
Okuyama et al.
sectable IIIA, IIIB, or IV) or recurrent disease,
b) to have had no active anti-cancer treatment
such as surgery, chemotherapy, or radiotherapy
in the preceding 4 weeks, c) to be informed of
the cancer diagnosis, d) to be well enough to
complete the questionnaire and participate in a
brief interview, and e) not to be suffering from
severe mental or cognitive disorders.
This study was approved by the Institutional
Review Board and the Ethics Committee of the
National Cancer Center, Japan. Full written
consent was obtained from each patient after
being fully informed of the study.
Procedure
Consecutive outpatients who met the criteria
were invited to participate in the study. After informed consent had been obtained, structured
interviews were held to obtain sociodemographic data and information on regular medication. A physical examination, including measurement of height, weight, temperature, heart
rate, respiratory rate, and SpO2 (oxygen-saturated hemoglobin measured by finger pulse
oximeter) was conducted. The patients were
given simple instructions, and were requested to
complete the self-administered questionnaires
at home on the hospital-visit day and to mail
them back by the following day. Returned questionnaires were checked and if any questions
had not been answered, telephone inquiries
were made to obtain the missing answers, as the
participants had been informed already. Subjects received a 500-yen prepaid telephone-card
for participating in this study.
Thirty-seven consecutive patients, selected
from the entire patient sample, participated in
the test on two occasions in order to assess the
reliability of the definition of clinical fatigue
and that of the Fatigue Numerical Scale. These
patients completed the survey by mail with an
average 7.0-day window between the first and
second response (SD 1.27, median 7 days).
Measures
Definition of Clinical Fatigue. Using a questionnaire, patients were asked whether fatigue had
interfered, in the previous 24 hours, with the 7
domains of daily activity: walking ability, sleep,
normal work, mood, relations with other people,
enjoyment of life, and general activities of life.
Each category was scored as ‘present’ or ‘absent.’
Vol. 22 No. 1 July 2001
The 7 domains are those used by Cleeland et al.
in the Brief Pain Inventory,22 and have been investigated in pain research.23 Patients who complained of interference with at least one domain
were defined as having clinical fatigue.
We examined the psychometric properties of
this classification. The test–retest reliability was
investigated calculating Cohen’s kappa coefficient.24 This indicates the stability of distinguishing between case and non-case, and was found
to be sufficient (coefficient 0.68). Discriminant validity was investigated by the Mann–
Whitney test comparing performance status
between the two groups, and a significant difference was found (z 2.87, P 0.004).
Cancer Fatigue Scale (CFS). The CFS is a 15-item
self-rating scale for assessing fatigue in cancer
patients.25 This scale consists of 3 subscales
(physical, affective, and cognitive aspect of fatigue), and can assess the multi-dimensional
nature of fatigue. Patients are asked to circle
the one number that describes their current
state, on a scale of 1 (not at all) to 5 (very
much). The possible response range for each
subscale score is 0 to 28 for the physical, and 0
to 16 for each of the affective and cognitive
subscales. Total fatigue is calculated as the sum
of these aspects. The maximum total score is
60, with the higher scores indicating more severe fatigue. The reliability and validity of this
scale in cancer patients has been established by
a previous study.25
Fatigue Numerical Scale (FNS). The FNS is a 0 to
10 numerical rating scale, ranging from zero
(‘no fatigue’) to 10 (‘fatigue as bad as you can
imagine’). Patients are asked to circle a number
to express the intensity of their fatigue in the
previous 24 hours, thus expressing a global sensation of fatigue. The advantages of using numerical rating scales have been discussed elsewhere.26 The most important characteristic of
FNS is its simplicity, making it easier both to administer to patients and for clinicians to collect
and interpret the data. We investigated the validity and reliability of this scale in the present
study. Pearson’s correlation coefficients between
FNS and CFS total score, which indicates convergent validity, was 0.69 (P 0.001). The test–
retest correlation coefficient was 0.60 (P 0.001).
Vol. 22 No. 1 July 2001
Fatigue in Lung Cancer Patients
Assessment of Pain, Dyspnea and Interference with
Daily Life Activities Caused By These Symptoms.
Pain and dyspnea were compared with fatigue
using similar 0–10 numerical scales, the Pain
Numerical Scale (PNS) and the Dyspnea Numerical Scale (DNS). In addition, interference
with daily activities, due to these symptoms, was
also investigated using the same format as that
for fatigue.
Hospital Anxiety and Depression Scale (HADS).
The HADS, developed by Zigmond et al., consists of a 7-item anxiety subscale and a 7-item
depression subscale. It assesses psychological
distress over the preceding week.27 A characteristic of this scale is that it does not include
questions about physical symptoms. We have
established the reliability and validity of the
Japanese version of this questionnaire in cancer patients.28
Ad-Hoc Self-Administered Questionnaire. An adhoc self-administered questionnaire was used
to investigate physical symptoms in the preceding 24 hours. This included questions on
cough, sputum, dyspnea at rest and while walking, appetite, nausea, constipation, sleep, and
pain. These categories were assessed using the
4-point Likert scale, rating 0 (not at all) to 3
(very much).
Medical information was obtained from the
patients’ medical records. Their laboratory
data were also obtained from the medical
records if they were available within a week.
On the day of the hospital visit, the attending
physician clinically judged the performance
status (PS), as defined by the Eastern Cooperative Oncology Group (ECOG). Each of the attending physicians had been engaged in thoracic oncology for over 5 years.
Statistical Analysis
To compare the prevalence of interference
with daily life activity due to fatigue, dyspnea,
and pain, we conducted a Friedman test in
each domain. If significant difference was
found, further analysis was conducted using
the Wilcoxon ranked test.
A logistic regression analysis was carried out
to clarify the factors that were correlated with
clinical fatigue. The presence or absence of
clinical fatigue was entered into the analysis as a
dependent variable. Initially, univariate analysis
557
was carried out to determine the potential correlated factors. Factors having a P value of less
than 0.05 were retained as independent variables, and subjected to logistic regression analysis. Inter-group comparisons of categorical,
nonparametric, and continuous variables were
examined using the chi-square test, Wilcoxon’s
rank sum test, and unpaired t-test, respectively.
The cut-off point for screening patients with
clinical fatigue, using both the CFS and FNS,
was determined utilizing the Receiver Operating Characteristic Curves (ROC curves). The
optimal cut-off point was determined by two
criteria. Firstly, it was chosen in order to minimize the sum of false-positive (1 sensitivity)
and false-negative (1 specificity) test results.
Secondly, a consideration of the balance between sensitivity and specificity was made in
cases where the sensitivity was considerably
lower than the specificity. Acceptable levels of
sensitivity and specificity depend on the purpose for which a scale is being utilized.29 Since
we considered that over-diagnosis of clinical fatigue is less harmful than missing it completely
by using a cut-off point that is set too high, we
determined it to provide enough sensitivity.
For all analyses, a P value less than .05 was required for significance. All statistical procedures were performed using SPSS 8.0J for Windows statistical software (SPSS Japan Institute
Inc., 1998).
Results
Subjects
One hundred and seventy-one consecutive
outpatients were initially considered as candidates for the study between May and July 1998.
Eight of these patients were ineligible due to
their severe physical ill health and/or cognitive
dysfunction. Two patients (1%) declined because of lack of time or feeling too ill, and four
(2%) were excluded because they failed to
complete the questionnaires. The sociodemographic and clinical characteristics of the remaining 157 patients are shown in Table 1.
About 80% of the subjects had physical impairments, with a performance status of 1 or more.
Non-participants had a worse performance status (z 3.80, P 0.001, Mann–Whitney test)
and were of a higher education level (z 2.06, P 0.04, Mann–Whitney test) when
compared with participants.
558
Okuyama et al.
Vol. 22 No. 1 July 2001
Table 1
Demographical and Clinical Characteristics of
Subjects (n 157)
Sample Characteristics
Age (year)
Sex
Male
Hospital
NCCHEa
NCCHb
Clinical stage
IIIA (unresectable)
IIIB
IV
recurrent
Days after diagnosis
Performance statusc
0
1
2
3
4
n (%)
mean: 63.1 9.4
(range, 27–80)
median: 64
111 (71)
121 (77)
36 (23)
28 (18)
51 (33)
31 (20)
47 (30)
mean: 63 669
(range, 15–3138)
median: 386
30 (19)
118 (75)
7 (5)
1 (1)
1 (1)
aNational
Cancer Center Hospital East, Kashiwa, Japan.
Cancer Center Hospital, Tokyo, Japan.
Cooperative Oncology Group criterion.
bNational
cEastern
Prevalence of Clinical Fatigue and Interference
with Daily Activities; A Comparison with Pain
and Dyspnea
The FNS, DNS and PNS results showed that
81.5% of subjects experienced some degree of
fatigue, 74.5% experienced dyspnea, and
65.0% experienced pain. Figure 1 shows the
percentage of patients who reported interference with each daily life activity due to fatigue,
dyspnea, or pain. About one-third of the patients reported that fatigue had interfered with
physical activities such as walking (36.3%) or
normal work (31.8%). On the other hand,
about one-fifth of patients reported that fatigue had interfered with emotional activities
such as mood (21.7%) or enjoyment of life
(19.1%). There was a significant difference in
the prevalence of interference with activities in
only the walking and work domains for each of
the three symptoms (chi-square 19.26, P 0.001, and chi-square 8.95, P 0.01, respectively, Friedman test). The prevalence of interference due to fatigue was as high as that
caused by dyspnea (37.6%, z 0.29, P 0.77
and 32.5%, z 0.15, P 0.88, Wilcoxon
test), and significantly higher than that caused
Fig. 1. Percentage of patients who reported interference with daily life activities due to fatigue; comparison with that due to pain and dyspnea (n 157).
Percentages of patients who reported interference
with each daily activity, due to fatigue, dyspnea, and
pain, are shown. From this it can be seen how fatigue impinges on various domains of daily activity.
by pain (19.7%, z 3.61, P 0.001 and
21.7%, z 2.60, P 0.01).
About half of the patients were found to
have clinical fatigue (51.3%)—that is, they
complained of interference with at least one
domain of daily living activity. Patients with
clinical fatigue had significantly greater CFS
and FNS scores than those where clinical fatigue was absent (mean CFS score [SD] 25.4
[9.2] and 14.7 [6.9], respectively (Figure 2); t 8.26, P 0.001; mean FNS score [SD] 4.0
[1.9] and 1.1 [1.2], respectively; t 11.1, P 0.001, unpaired t-test).
Factors Correlated with Clinical Fatigue
A comparison of patients’ characteristics between patients with and those without clinical
fatigue is shown in Tables 2 and 3. There was a
significant difference between the two groups
in performance status, symptoms of sputum,
cough, dyspnea, appetite loss, sleep, pain, de-
Vol. 22 No. 1 July 2001
Fatigue in Lung Cancer Patients
559
Fig. 2. Distribution of the Cancer Fatigue Scale scores (n 157). The distributions of the CFS scores are compared between patients with and without clinical fatigue. Patients with clinical fatigue had significantly greater
CFS scores than those where clinical fatigue was absent (mean [SD] 25.4 [9.2], and 14.7 [6.9], respectively; t 8.26, P 0.001; unpaired t-test).
pression, and anxiety. However, no significant
difference was found in the sociodemographic,
disease, and treatment characteristics. Before
these variables were subjected into logistic regression analysis, correlations among independent variables were noted. There were three
correlation coefficients above 0.30: depression
and anxiety; sputum and cough; and appetite
loss and nausea. Only variables with a strong
correlation with other dependent variables
were selected for logistic regression analysis.
The results of this analysis are shown in Table 4.
Patients with clinical fatigue had significantly
more severe dyspnea on walking, more severe
appetite loss, and more severe depression than
those where clinical fatigue was absent. Recent
laboratory data were available in 81 patients (37
patients with clinical fatigue and 44 without it).
There were no significant differences in white
blood cell count, albumin, and hemoglobin between the two groups.
Screening for Clinical Fatigue
Figure 3 shows the ROC curve for screening
for clinical fatigue in this population. For the
CFS, the optimal cut-off point was 18/19, and
this was associated with 71.3% sensitivity and
74.0% specificity. For the FNS, the optimal cutoff point was 2/3, which gave 76.3% sensitivity
and 87.0% specificity. In addition, we investigated the association between fatigue intensity
measured by FNS and the number of interfered domains, similar to Mendoza et al.30 The
optimal cut-off points are associated with a
large increase in the number of interfered domains, since the ideal cut-off point between
each severity group should yield the greater
discrepancy in the number of interfered domains. This result is preliminary because of the
limited accuracy of the data, since there were
only a small number of patients with fatigue
scores of 6 or more (17 patients, 10.8%). However, there are steep slopes between 2 and 3
and also between 4 and 5 (Figure 4). This may
indicate that a cut-off point of 4/5 could be
useful in distinguishing severe clinical fatigue
from mild clinical fatigue.
Discussion
This study investigated the prevalence of
clinical fatigue and correlated factors, and the
560
Okuyama et al.
Vol. 22 No. 1 July 2001
Table 2
Comparison of Demographical and Clinical Characteristics Between Patients With and
Without Clinical Fatigue (n 157)
Sample Characteristics
Sociodemographic
Age (years) 65
Sex (male)
Education (junior high school or less)
Household Size (lives alone)
Marital Status (married)
Working Outside the Home (yes)
Disease and Treatment
Time after Diagnosis (1 year)
Histology of Lung Cancer
small cell
non-small cell
Clinical Stage
IIA (unresectable)
IIIB
IV
recurrent
Prior Treatment
surgery
IV chemotherapy
radiotherapy
Current Steroid Use
Current Opioid Use
Current Anxiolytic Use
Current Home Oxygen Therapy
With
Clinical
Fatigue
(n 80)
Without
Clinical
Fatigue
(n 77)
P
N (%)
36 (45)
51 (64)
38 (48)
6 (8)
66 (83)
15 (19)
N (%)
43 (56)
60 (78)
29 (38)
4 (5)
65 (84)
16 (21)
0.17a
0.05a
0.21a
0.74b
0.75a
0.75a
40 (50)
37 (48)
0.80a
10 (13)
70 (78)
16 (21)
61 (79)
0.16a
11 (14)
27 (34)
14 (18)
28 (35)
17 (22)
24 (31)
17 (22)
19 (25)
0.17c
20 (25)
58 (73)
43 (54)
9 (11)
13 (16)
11 (14)
2 (3)
16 (21)
59 (77)
59 (77)
5 (7)
11 (14)
11 (14)
2 (3)
0.52a
0.55a
0.95a
0.30b
0.73a
0.92a
1.00b
aChi-square
test.
exact test.
cWilcoxon’s rank sum test.
bFisher’s
screening ability of the CFS and FNS in ambulatory patients with advanced lung cancer.
Our results indicated that about half of these
patients suffered from clinical fatigue. The impact on daily activities extended into both
physical and emotional areas. The problem of
fatigue is as great as that of pain and dyspnea.
Health care professionals therefore need to
pay more attention to fatigue and its impact on
patients’ QOL.
Dyspnea on walking, appetite loss, and depression were significantly correlated with clinical fatigue in ambulatory patients with advanced lung
cancer. A greater awareness of these factors may
contribute to better understanding and treatment of fatigue in this population. Stone et al.
also found an association between fatigue and
dyspnea, and suggested that there might be a
similar underlying physiological process.31 We
were unable to find any significant association between fatigue and SpO2. Although dyspnea is as
poorly understood as fatigue, a more detailed
study focusing on this issue is needed. For exam-
ple, other processes such as anemia should be
studied to clarify this phenomenon, although
our preliminary results failed to show any association between fatigue and low hemoglobin levels.
The association between fatigue and appetite loss was also an important finding. This relationship was independent of depression. Appetite loss may cause malnutrition, and so
induce fatigue. Alternatively, both appetite loss
and fatigue may lead to the same biological
process such as cachexia,32 although studies
have failed to show such an association.31,33
Since this study did not include objective measures of malnutrition or cachexia such as the
Body Mass Index and Mid Arm Muscle Circumference, our results are limited, and further research is required.
Depression was also a correlated factor for fatigue. This suggests that the development of fatigue is associated with both physical and psychological factors, and this finding is consistent
with other studies.7,34 In the general population, it is recognized that depression contrib-
Vol. 22 No. 1 July 2001
Fatigue in Lung Cancer Patients
561
Table 3
Comparisons of Physical and Psychological Status Between Patients With and Without
Clinical Fatigue (n 157)
With
Clinical Fatigue
(n 80)
Sample Characteristics
Physical Status
Performance Status (ECOGa)
Body Temperature (C)
0
1
2
3
4
37.0
37.0
Sp02b
Heart Rate
Respiratory Rate
Sputumc
Coughc
Dyspnea at Restc
Dyspnea on Walkingc
Appetitec
Nauseac
Constipationc
Sleepc
Panicc
WBC (102 count/mm3)d
Hemoglobin (g/dl)d
Albumin (g/dl)d
Psychological
Depressione
Anxietye
Without
Clinical Fatigue
(n 77)
P
n
(%)
n
(%)
9
64
6
1
0
70
10
mean
96.2
83.0
18.4
2.1
2.1
1.2
2.1
2.0
1.3
1.6
1.8
1.7
60.3
12.0
3.8
(11)
(80)
(8)
(1)
(0)
(88)
(13)
SD
1.9
13.0
3.0
0.8
0.8
0.5
0.7
0.9
0.6
0.9
0.9
0.8
19.1
2.1
0.5
21
54
1
0
1
63
14
mean
96.4
82.3
18.4
1.8
2.0
1.1
1.6
1.4
1.1
1.4
1.3
1.4
62.8
12.1
3.9
(27)
(70)
(1)
(0)
(1)
(82)
(18)
SD
1.5
13.7
3.1
0.6
0.6
0.3
0.7
0.6
0.3
0.6
0.5
0.6
23.9
2.0
0.8
0.50h
0.76h
0.98h
0.007f
0.14f
0.03f
0.001f
0.001f
0.009f
0.13f
0.001f
0.01f
0.60h
0.85h
0.16h
6.9
5.1
3.8
3.5
3.8
2.9
3.0
2.4
0.001h
0.001h
0.01f
0.32g
aDefined
by Eastern Cooperative Oncology Group.
hemoglobin measured with a finger pulse-oximeter.
were assessed by a 4-point Likert scale—1 not at all to 4 very much.
dLaboratory data were obtained from 81 patients.
eHospital Anxiety and Depression Scale.
fWilcoxon’s rank sum test.
gChi-squared test.
hUnpaired t-test.
bOxygen-saturated
cSymptoms
utes to fatigue.35 Derogatis et al. reported that
about 42% of cancer patients suffer from major
depression or adjustment disorders.36 In some
studies in lung cancer patients, the prevalence
of depression has been reported as to be
15%.37,38 Psychological distress need to be
taken into account when attempting to ameliorate fatigue, since treatment of depression is effective in most cases. On the other hand, 21.5%
of patients in this study complained that fatigue
had interfered with their mood. These results
show the complexity of this association, since
fatigue and depression might partly cause and
partly result from each other, as Stone et al.
have suggested.39 To investigate this complex
phenomenon, further research is required.
Some other physical factors, such as low performance status, sputum, nausea, insomnia,
and pain, appear to be strongly associated with
fatigue. It is possible that the effect of other
symptoms of cancer may promote the development of secondary fatigue, as suggested by
Winningham et al.40 We consider that decreasing these correlated factors might also contribute to ameliorating fatigue in this population.
From a clinical standpoint, it is desirable to establish a method for screening for clinical fatigue. The present study indicated that both the
CFS and FNS had well-accepted screening ability
in patients with advanced lung cancer. Fatigue is
often overlooked and may be underestimated by
both physicians and cancer patients. Vogelzang
et al. reported on the perception of fatigue by
both patients and oncologists in heterogeneous
cancer patients.10 In their study, 32% of cancer
patients reported that fatigue affected their daily
562
Okuyama et al.
Vol. 22 No. 1 July 2001
Table 4
Correlated Factors for Clinical Fatigue—Logistic
Regression Analysis (n 157)
Exp (95% CIe)
Independent Variable Beta SEd
P
Odds
Ratio Lower Higher
Performance Statusa .15 .36 .68 0.86
Sputumb
.52 .31 .10 1.68
Dyspnea on Walkingb
.94 .33 .005 2.56
Dyspnea at Restb
.54 .56 .34 0.58
Appetiteb
.68 .29 .02 1.97
.50 .34 .15 1.64
Insomniab
Painb
.26 .32 .42 1.30
Depressionc
.22 .07 .001 1.24
0.43
0.91
1.34
0.19
1.12
0.84
0.69
1.09
1.75
3.09
4.92
1.76
3.47
3.21
2.45
1.41
aDefined
by Eastern Cooperative Oncology Group.
were assessed by 4-point Likert scale (1 not at all to
4 very much).
cHospital Anxiety and Depression Scale.
dStandard Error.
eConfidence Interval.
bSymptoms
routines significantly, but 74% considered fatigue to be a symptom that had to be endured.
On the other hand, most oncologists (80%) believed that fatigue is generally overlooked or under-treated. Since fatigue may be sufficiently severe to interfere with daily life activities, it is
essential for medical staff to be alert to the possibility of fatigue in these patients.
Recently Mendoza et al. suggested that a
Fig. 4. The association between fatigue intensity
measured by FNS and the number of interfered domains (n 157). A plot of the mean number of interfered domains against the Fatigue Numerical
Scale score is shown.
score of 7 or more on the 0–10 numerical scale
in the Brief Fatigue Inventory (BFI) indicates severe fatigue.30 We were unable to examine the
BFI classification since only 7.0% of participants
in this study met Mendoza’s criteria, probably
because subjects in this study were outpatients.
It may also be questionable whether the number of interfered domains, used to define clinical fatigue, truly reflects the severity of fatigue.
Further studies are required to establish a detailed severity classification for fatigue.
We conclude that fatigue is one of the most
critical problems for ambulatory patients with
advanced lung cancer. Dyspnea on walking, appetite loss, and depression are major factors
that correlate with fatigue. This study has revealed that the CFS and FNS are useful screening tools for clinical fatigue. It is hoped that
these findings will contribute to a better understanding of fatigue in this population, and facilitate earlier detection and treatment.
Acknowledgments
Fig. 3. ROC curves for the Cancer Fatigue Scale and
the Fatigue Numerical Scale to screen for clinical fatigue (n 157). The sensitivity and 1 specificity is
plotted for each value of the Cancer Fatigue Scale
and Fatigue Numerical Scale. The cut-off points to
screen for clinical fatigue are shown on each scale.
ROC receiver operating characteristics.
This work was supported in part by Grants-inAid for Cancer Research (9-31, 9-32) and the
Second Term Comprehensive 10-Year Strategy
for Cancer Control from the Ministry of Health
and Welfare, Japan. Toru Okuyama is an
awardee of a Research Resident Fellowship
from the Foundation for the Promotion of Cancer Research, Japan.
Vol. 22 No. 1 July 2001
Fatigue in Lung Cancer Patients
The authors gratefully acknowledge Charles
S. Cleeland, PhD, Pain Research Group, M.D.
Anderson Cancer Center, Houston, Texas,
USA, for giving us permission to use the concept of ‘Interference,’ which he developed in
the Brief Pain Inventory. We would also like to
express our gratitude to Satoshi Sasaki, MD,
PhD, Epidemiology and Biostatistics Division,
National Cancer Center Research Institute East,
and Tito Mendoza, PhD, Pain Research Group,
M.D. Anderson Cancer Center, Houston, Texas,
USA for supervising the statistical analysis. In addition, the authors would like to acknowledge
the cooperative staff at the National Cancer
Center Hospital East, Kashiwa, Japan.
References
1. Degner LF, Sloan J. Symptom distress in newly
diagnosed ambulatory cancer patients and as a predictor of survival in lung cancer. J Pain Symptom
Manage 1995;10:423–431.
2. Coyle N, Adelhardt J, Foley KM, et al. Character
of terminal illness in the advanced cancer patient:
pain and other symptoms during the last four weeks
of life. J Pain Symptom Manage 1991;5:83–93.
3. Vainio A, Auvinen A, with Members of the
Symptom Prevalence Group. Prevalence of symptoms among patients with advanced cancer: an international collaborative study. J Pain Symptom
Manage 1991;12:3–10.
4. Joly F, Henry-Amar M, Arveux P, et al. Late psychosocial sequelae in Hodgkin’s disease survivors: a
French population-based case-control study. J Clin
Oncol 1996;14:2444–2453.
5. Nail L, Jones L. Fatigue side effects and treatment and quality of life. Qual Life Res 1995;4:8–13.
6. Love RR, Leventhal H, Easterling DV, et al. Side
effects and emotional distress during chemotherapy. Cancer 1989;63:604–612.
7. Smets EMA, Visser MRM, Willems-Groot AFMN,
et al. Fatigue and radiotherapy: experience in patients undergoing treatment. Br J Cancer 1998;78:
899–905.
8. Hann DM, Jacobsen PB, Martin SC, et al. Fatigue in women treated with bone marrow transplantation for breast cancer: a comparison with
women with no history of cancer. Support Care Cancer 1997;5:44–52.
563
survey. The Fatigue Coalition. Semin Hematol 1997;
34(3 Suppl 2):4–12.
11. Ashbury FD, Findlay H, Reynolds B, et al. A Canadian survey of cancer patients’ experiences. Are
their needs being met? J Pain Symptom Manage
1998;16:298–306.
12. Pisani P, Parkin DM, Bray F, et al. Estimates of
the worldwide mortality from 25 cancers in 1990. Int
J Cancer 1999;24:18–29.
13. Statistics and Information Department, Minister’s secretariat, Ministry of Health and Welfare, Japan. Vital statistics of Japan 1998. Tokyo: Ministry of
Health and Welfare, 1998.
14. Ries LA. Influence of extent of disease, histology, and demographic factors on lung cancer survival in the SEER population-based data. Semin
Surg Oncol 1994;10:21–30
15. Pater JL, Zee B, Palmer M. Fatigue in patients
with cancer: results with National Cancer Institute
of Canada Clinical Trials Group studies employing
the EORTC QLQ-C30. Support Care Cancer 1997;5:
410–413.
16. Sarna L, Brecht ML. Dimensions of symptom
distress in women with advanced lung cancer: a factor analysis. Heart Lung 1997;26:23–30.
17. Hurny C, Bernhard J, Joss R, et al. “Fatigue and
malaise” as a quality-of-life indicator in small-cell
lung cancer patients. The Swiss Group for Clinical
Cancer research (SAKK). Support Care Cancer
1993;1:316–320.
18. Hopwood P, Stephens RJ. Depression in patients with lung cancer: prevalence and risk factors
derived from quality-of-life data. J Clin Oncol 2000;
18:893–903.
19. Tishelman C, Degner LF, Mueller B. Measuring
symptom distress in patients with lung cancer. A pilot study of experienced intensity and importance of
symptoms. Cancer Nurs 2000;23:82–90.
20. Hickok JT, Marrow GR, McDonald S, et al. Frequency and correlates of fatigue in lung cancer patients receiving radiation therapy: implication for
management. J Pain Symptom Manage 1996;11:
370–377.
21. Blesch KS, Paise JA, Wickham R, et al. Correlates of fatigue in people with breast or lung cancer.
Oncol Nurs Forum 1991;18:81–87.
22. Cleeland CS. Measurement of pain by subjective
report. In: Chapman CR, Loeser JD eds. Advances in
pain research and therapy, vol.12: Issues in pain
measurement. New York: Raven Press, 1989:391–403.
9. Ferrell BR, Grant M, Dean GE, et al. “Bone
tired”: the experience of fatigue and its impact on
quality of life. Oncol Nurs Forum 1996;23:1539–
1547.
23. Cleeland CS, Nakamura Y, Mendoza TR, et al.
Dimensions of the impact of cancer pain in a four
country sample: new information from multidimensional scaling. Pain 1996;67:267–273.
10. Vogelzang NJ, Breitbart W, Cella D, et al. Patient, caregiver, and oncologist perceptions of cancer-related fatigue: results of a tripart assessment
24. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas 1960;20:37–46.
25. Okuyama T, Akechi T, Kugaya A, et al. Develop-
564
Okuyama et al.
ment and validation of the Cancer Fatigue Scale: a
brief, three-dimensional, self-rating scale for assessment of fatigue in cancer patients. J Pain Symptom
Manage 2000;19:5–14.
26. Cleeland CS. Pain assessment in cancer. In: Osoba D, ed. Effect of cancer on quality of life. Boca
Raton, FL: CRC Press, Inc., 1991:293–305.
27. Zigmond AS, Snaith RP. The Hospital Anxiety
and Depression Scale. Acta Psychiat Scand 1983;67:
361–370.
28. Kugaya A, Akechi T, Okuyama T, et al. Screening for psychological distress in Japanese cancer patients. Jpn J Clin Oncol 1998;28:333–338.
29. Baldessarini RJ, Finklestein S, Arana GW. The
predictive power of diagnostic tests and the effect of
prevalence of illness. Arch Gen Psychiat 1983;40:
569–573
Vol. 22 No. 1 July 2001
33. Bruera E, Brenneis C, Michaud M, et al. Association between asthenia and nutritional status, lean
body mass, anemia, psychological status, and tumor
mass in patients with advanced breast cancer. J Pain
Symptom Manage 1989;4:59–63.
34. Akechi T, Kugaya A, Okamura H, et al. Fatigue
and its associated factors in ambulatory cancer patients—a preliminary study. J Pain Symptom Manage 1999;17:42–48.
35. Pawlikowska T, Chadler T, Hirsch S, et al. A
population based study of fatigue and psychological
distress. Br Med J 1994;308:743–746.
36. Derogatis LR, Morrow GR, Denmann D, et al.
The prevalence of psychiatric disorders among cancer patients. JAMA 1983;249:751–757.
30. Mendoza TR, Wang XS, Cleeland CS, et al: The
rapid assessment of fatigue severity in cancer patients. Cancer 1999;85:1186–1196.
37. Okamura H, Akechi T, Kugaya A, et al. Depression in patients with advanced cancer. In: Eguchi K,
Klastersky J, Feld R, eds. Current perspectives and
future directions in palliative medicine. New York:
Springer, 1998:67–76.
31. Stone P, Hardy J, Broadley K, et al. Fatigue in
advanced cancer: a prospective controlled cross-sectional study. Br J Cancer 1999;79:1479–1486.
38. Hughes JE. Depressive illness and lung cancer.
II. Follow-up of inoperable patients. Eur J Sug Oncol 1985;11:21–24.
32. Billingsley KG, Alexander HR. The pathophysiology of cachexia in advanced cancer and AIDS. In:
Bruera E, Higginson I, eds. Cachexia-anorexia in
cancer patients. New York: Oxford University Press,
1996:1–22.
39. Stone P, Richards M, Hardy J. Fatigue in patients with cancer. Eur J Cancer 1998;34:1670–1676.
40. Winningham ML, Nail LM, Burke MB, et al. Fatigue and the cancer experience: the state of the
knowledge. Oncol Nurs Forum 1994;21:23–36.