I Get Tired for No Reason: A Single Item Screening for Cancer-Related Fatigue

Transcription

1 Vol. 22 No. 5 November 2001 Journal of Pain and Symptom Management 931 Original Article I Get Tired for No Reason: A Single Item Screening for Cancer-Related Fatigue Kenneth L. Kirsh, MS, Steven Passik, PhD, Elizabeth Holtsclaw, BA, Kathleen Donaghy, PhD, and Dale Theobald, PhD, MD Indiana University Purdue University Indianapolis (K.L.K), and Oncology Symptom Control & Research (S.P., E.H., K.D., D.T.), Community Cancer Care, Indianapolis, Indiana, USA Abstract This study examined the criterion validity and sensitivity and specificity of a single item to rapidly screen patients in ambulatory oncology clinics for cancer-related fatigue. In an effort to expand the utility of the Zung Self-Rating Depression Scale (ZSDS) as a screen for other symptoms, the utility of the single fatigue item was examined. The fatigue item reads I get tired for no reason and is rated on a four-point scale ranging from none or a little of the time to most or all of the time. Fifty-two subjects were administered the Zung, the Functional Assessment of Cancer Therapy-Anemia (FACT-An) scale, and the Fatigue Symptom Inventory (FSI). The Zung item was highly correlated with the ZSDS (r 0.63, p ) and the FACT-An (r 0.70, p ), as well as to the individual items of the FSI, ranging from 0.41 (p 0.003) to 0.71 (p ). All 10 subjects considered to be depressed based on the ZSDS were also considered to fatigued on the FACT-An. Setting the ZSDS item cutoff point at level 3 A good part of the time yielded a sensitivity of 78.95% and a specificity of 87.88%. It is concluded that a single item can be a fast and accurate way of screening cancer patients for fatigue to trigger additional follow-up, thus expanding the utility of a depression screening tool for problems other than the purely psychiatric. J Pain Symptom Manage 2001:22: U.S. Cancer Pain Relief Committee, Key Words Cancer, fatigue, assessment Introduction Fatigue is the most common symptom reported by cancer patients, 1 3 prevalence rates ranging from 61% to 90% in various oncology samples. 3 5 A recent study by Ashbury and colleagues 6 found that oncology patients with CRF were more likely to have impairment in performing normal daily activities and to more Address reprint requests to: Steven D. Passik, PhD, Community Cancer Care, Inc., Oncology Symptom Control Research, 115 W. 19th Street, Indianapolis, IN 46202, USA. Accepted for publication: January 26, frequently use healthcare services than their non-fatigued counterparts. To date, the mechanisms of CRF are not fully understood, nor is there consensus on how to best identify clinically relevant fatigue. 7 A recent survey of nearly 200 oncologists 3 found that 80% feel that fatigue is either neglected or undertreated in cancer patients. In addition, they found that most patients (74%) believed fatigue to be a symptom that one had to endure as a normal part of cancer and its accompanying treatment. Passik and colleagues 8 examined barriers to fatigue communication to better understand why patients don t discuss fatigue more given its deleterious impact on U.S. Cancer Pain Relief Committee, /01/$ see front matter Published by Elsevier, New York, New York PII S (01)

2 932 Kirsh et al. Vol. 22 No. 5 November 2001 quality of life. The most frequently cited patient-reported barrier was the failure of the physician to inquire about this symptom. A sense of futility about the likelihood that fatigue will be successfully treated is also an impediment. Identifying patients with cancer-related fatigue (CRF) is the key first step in improving fatigue management. There is a need for quick and efficient screens to identify CRF. Large batteries of tests and assessment instruments are simply not practical for many oncology settings, due to cost and time pressures. With the aforementioned prevalence of nearly 70% for fatigue, 9 the use of brief self-report screens or even single items are the only way a large number of patients can be identified. Indeed, Chochinov and colleagues 10 established the use of a single-item screen for depression in cancer patients and found it to be nearly as accurate as longer questionnaires or visual analog scales. To be useful, the measure or item must be deemed to have an acceptable level of sensitivity and specificity for the setting in which it is employed. Specificity refers to the ability of the screen to correctly identify those patients who do not meet criterion, while sensitivity refers to the ability of the test to accurately identify those patients who meet criterion. 11 Screening for fatigue could then trigger further assessment, work-up of etiology, and then treatment. CRF is a multidimensional problem involving chronic exhaustion and diminished capacity for physical activity that is not alleviated by rest. 3,12 The etiology may be physical, psychological, or spiritual. 13,14 Fatigue level may be influenced by a variety of factors, including treatment, changes in sleeping patterns, nutrition, inactivity, symptom distress, mood disturbance, low hemoglobin levels, or poor performance status. 4,5,12,15 These multiple factors would have to be assessed and targeted for specific interventions. Varied treatments have shown benefit in alleviating CRF arising from multiple etiologies. For instance, self-care activities and education for the patient and family can be useful. 16 Kalman and Vilani 17 have shown that attempts to maintain nutritional balance can result in the prevention or decrease of some oncologyrelated fatigue. Two recent studies 18,19 have shown that aerobic exercise can help to improve the physical performance of cancer patients. Finally, epoetin alfa has been shown to increase hemoglobin levels and to reduce fatigue in anemic cancer patients, 20 and to increase patients perceived energy levels, functional status, and overall quality of life independent of response to primary oncologic therapy. 21 Our group routinely administers the ZSDS to screen patients for depression. In an effort to expand the utility of the scale as a screen for other symptoms (while simultaneously ruling depression in or out), we were interested in examining the utility of single items, such as the fatigue item, as a separate screen. In the current study, we attempted to employ item #10 from the ZSDS ( I get tired for no reason ) as a single-item screen for fatigue in a population of ambulatory oncology patients. To examine validity and specificity/sensitivity issues, subjects were also administered the Functional Assessment of Cancer Therapy-Anemia (FACT-An) scale and the Fatigue Symptom Inventory (FSI). Methods An a priori simplified power analysis was conducted to determine the sample size requirement for the correlational analyses of this study. Assuming an alpha of 0.05, a medium effect size (ES r 0.50), and a power of 0.80, 50 total subjects would be required. 22 A convenience sample was drawn from patients who entered one of 31 Community Cancer Care Inc., clinics in urban and rural areas throughout Indiana until a total of 52 subjects was reached. Any patient undergoing treatment for malignancy was eligible to participate if they were able to read and write English, were over 18 years of age, and did not give evidence of cognitive impairment severe enough to preclude giving informed consent. Measures The Zung Self-Rating Depression Scale (ZSDS) 23,24 is a 20-item self-report measure of the symptoms of depression. Subjects rate each item regarding how they felt during the preceding week using a 4-point Likert scale, with 4 representing the most unfavorable response. The sum of the 20 items, after correcting for the 10 items that are reverse-scored, produces

3 Vol. 22 No. 5 November 2001 Single Item Fatigue Screening 933 a raw score that can be converted into a depression score (termed the SDS index). The scores can be categorized into 4 levels to offer a global clinical impression: 1: within normal range, no significant psychopathology (raw score 40); 2: presence of minimal to mild depression (raw score 40 47); 3: presence of moderate to marked depression (raw score 48 55); and 4: presence of severe to extreme depression (raw score 55). Scores are not meant to offer strict diagnostic guidelines but rather denote levels of depressive symptomatology that may be of clinical significance. Overall, the ZSDS has been shown to be relatively valid and to have high internal consistency, exhibiting an alpha coefficient of ,26 Passik and colleagues 27 described the factor structure of the ZSDS in cancer patients, identifying a cognitive symptom factor, a manifest depressed mood factor, an eating-related somatic factor, and a non-eating related somatic factor. In this study, scores greater than 47 (indicating moderate-to-severe depression above) are considered to be in the depressed range. The Functional Assessment of Cancer Therapy Anemia (FACT-An) 28 is a 47-item self-administered questionnaire covering the quality of life domains of physical, social and family, emotional and functional well-being, as well as overall fatigue. Items are rated on a 5 point Likert scale, from 0 (not at all) to 4 (very much) for how true each statement has been for the patient in the past seven days. After accounting for reverse-scored items, questions are summed across the subscales as well as added for a total score, with higher scores indicative of greater overall quality of life. The instrument is easy to use, reliable, and valid The measure has been shown to yield adequate to high internal consistency, exhibiting coefficient alphas ranging from 0.63 to 0.86 on the subscales and 0.90 to 0.95 for the total scale. 30,31 The scale has also shown high testretest reliability (r 0.87). 28 Currently in the fourth version, the measure has been deemed to be appropriate for use with all cancer patients. Based upon the data in this sample, a cutoff score of 115 on the FACT-An was employed to determine whether or not a subject was fatigued. This was deemed to be an acceptable cutoff in part based on prior research, 20 in which a sample of 2,370 patients who were identified as fatigued (as evidenced by hemoglobin counts less than 11) had a mean score of on the FACT-An at baseline assessment. The Fatigue Symptom Inventory (FSI) 33 is a 14-item self-report measure designed to tap various aspects of fatigue and how it impacts quality of life. It measures the daily pattern of fatigue as well as the intensity and frequency of fatigue. Four of the items are rated along an 11-point scale, ranging from 0 ( Not at all fatigued ) to 10 ( As fatigued as I could be ). Seven of the items are rated along on an 11-point scale, ranging from 0 ( No interference ) to 10 ( Extreme interference ). An additional item, asking for the number of days the person has been fatigued, ranges from 0 days to 7 days. It contains an item that asks respondents to indicate how much of the day on average they felt fatigued in the past week, ranging from 0 ( none ) to 10 ( the entire day ). The final item asks for the daily pattern of fatigue, and is designed to be a qualitative item only, having four choices. While all of the items are designed to stand alone as individual scales, items 5 11 can be summed to generate a Disruption Index. Statistical Analyses A series of descriptive statistics, Pearson correlations, and sensitivity and specificity statistics at several cutoff points were calculated. Specificity refers to a statistic designed to elucidate the number of correctly classified subjects without fatigue, via subthreshold scores on the ZSDS fatigue item, divided by the total number of subjects without fatigue (as measured by the FACT-An) multiplied by 100. Thus, the statistic offers the percentage of cases wherein the ZSDS fatigue item correctly identified people who are not fatigued (i.e., its ability to identify true negatives). Sensitivity refers to a statistic calculated by the number of correctly classified subjects with fatigue, via high scores on the ZSDS fatigue item, divided by the total number of true subjects with fatigue (as measured by the FACT-An) multiplied by 100. Thus, the statistic yields the percentage of cases wherein the ZSDS fatigue item correctly identified people who are fatigued (i.e., its ability to identify true positives).

4 934 Kirsh et al. Vol. 22 No. 5 November 2001 Results The average age of the sample was 62.8 years (SD 14.8) and was comprised of 36 women (69.2%) and 16 men (30.8%). The majority (55.1%, n 27) were homemakers, followed by those who were working part-time (16.3%, n 8), full time (10.2%, n 5), or who were on disability (10.2%, n 5). The type of tumor varied among the population, with breast (21.6%, n 11) and lung cancer (15.7%, n 8) followed by leukemia (13.7%, n 7) as the most prevalent. The majority (44.0%, n 22) had active disease, an additional 20% (n 10) were stable and another 16% (n 8) were disease-free. Gender was significantly related to the fatigue item (r.29, p 0.05), with women reporting more fatigue (mean 2.64, SD 1.05, n 36) than men (mean 2.0, SD 0.82, n 16), (t 2.16, p 0.05). Table 1 presents the means, ranges, and standard deviations for each of the measures. There was ample variation among subjects noted on the various measures. Scores on the FACT-An ranged from 49 to 181 (mean , SD 33.07). Raw scores on the ZSDS ranged from 22 to 58 (mean 38.58, SD 9.89). The Disruption Index of the FSI ranged from 0 to 58 (mean 19.52, SD 16.94). Table 1 Mean, Range, and Standard Deviation Information for the ZSDS, FACT-An and FSI Instrument Mean Standard Deviation Range Zung item # ZSDS FACT-An FACT-An Physical Subscale FACT-An Social Subscale FACT-An Emotional Subscale FACT-An Functional Subscale FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI # FSI Disruption Index Statistics were calculated to examine whether or not the single fatigue item from the ZSDS correlated with the total ZSDS score as well as the two measures of fatigue. The single item, as expected, was significantly correlated to the overall score on the ZSDS (r 0.63, p ) (eliminating the fatigue item from the calculation). The ZSDS fatigue item was significantly correlated to fatigue as measured by the FACT-An total score (r 0.70, p ) as well as to all of the subscales of the FACT-An. The ZSDS item related to the physical subscale (r 0.67, p ), social subscale (r 0.40, r 0.003), emotional subscale (r 0.40, p 0.003), and functional subscale (r 0.40, p 0.004) as well as to the FACT-G (r 0.57, p ). Similarly, the ZSDS fatigue item was significantly correlated to the thirteen quantitative items of the FSI. Correlations ranged from a low of 0.41 (p 0.003) for Item 9 ( Rate how much, in the past week, fatigue interfered with your relations to other people ) to a high of 0.71 (p ) for Item 4 ( Rate your level of fatigue right now ). Overall, it was significantly related to the Disruption Index (r 0.59, p ). It was also of interest to explore the relationship between depression, as measured by the ZSDS, and fatigue, as measured by the FACT- An. Of the 10 subjects who were depressed according to the ZSDS (using raw score 47, which has been shown to have acceptable sensitivity and specificity for detecting the diagnosis 34 ), all 10 were also considered to be fatigued, with scores on the FACT-An ranging from 49 to 114 (mean 83.8, SD 19.70). Scores on the FACT-An for the remaining 42 subjects ranged from 86 to 181 (mean 139.0, SD 25.98), with only 9 subjects considered to be fatigued (FACT-An 115). Results of a Chisquare analysis indicated that subjects who are depressed are more likely to be fatigued than their non-depressed counterparts ( , p ). The false negative rate, or the number of ZSDS item 10 results indicating no fatigue when the patient actually was fatigued according to the FACT-An threshold, was determined for a variety of cutoff points. When the some of the time cutoff range (raw score 1) was used, the single-item Zung yielded 0 false negatives (n 19). When the good part of the

5 Vol. 22 No. 5 November 2001 Single Item Fatigue Screening 935 time cutoff (raw score 2) was used, false negatives increased to 4/19 on the ZSDS Item 10. Finally, when the all of the time cutoff (raw score 3) was used, false negatives increased further to 9/19 on the ZSDS Item 10. The false positive rate, or the number of ZSDS item 10 results indicating fatigue when the patient did not score above threshold on the FACT-An, was also determined. When the all of the time cutoff (raw score 3) was used, 2 false positives (n 33) were evident. Further, when the good part of the time cutoff (raw score 2) was utilized, the ZSDS Item 10 score yielded 4/33 false positives. The use of a more liberal cutoff score, the some of the time cutoff (raw score 1), yielded 25/33 false positives on the ZSDS Item 10. Specificity and sensitivity statistics were then calculated for the ZSDS Item 10. The specificity of the ZSDS item 10 was assessed in several ways (see Table 2). The item exhibited specificity of 93.9% when the all of the time cutoff was used, dropped to 87.9% when using the good part of the time cutoff, and was 24.2% at the some of the time cutoff. The sensitivity of the ZSDS was also approached in several ways. Sensitivity ranged from 100% for the some of the time cutoff to 79.0% for the good part of the time cutoff point and 52.6% for the all of the time cutoff. Discussion Fatigue is a highly prevalent, multidimensional problem for cancer patients. It is distressing for the patient, but is treatable through rehabilitative, psychological, and medical intervention. It has become necessary to pursue a quick and efficient means to identify patients with CRF, given that without screening they are unlikely to spontaneously report this problem. In this study, we attempted to begin the process of establishing the utility of a single item screen for identifying clinically relevant fatigue in cancer patients. A single item screen could be useful to trigger referral, work-up, interventions or clinical trials. We found that, when using a score greater than 2 on the ZSDS item as the cutoff, 87.9% of patients who were not fatigued were correctly ruled out while 79.0% of those with clinically relevant fatigue were correctly identified. It is hoped that further research with larger samples can replicate and even show improvement on these findings. The ZSDS Item 10 was significantly correlated to the FACT-An and it subscales, as well as to the 13 quantitative FSI items and the Disruption Index, indicating that it has merit as a screen for fatigue. However, it was also correlated to the ZSDS, indicating that there is expected overlap between depression and fatigue. Indeed, this was suggested by the observation that all 10 patients who were diagnosed as depressed were also considered to be fatigued. Therefore, the ZSDS might prove useful by giving information on both problems simultaneously and prioritizing interventions. The ZSDS Item 10 is useful for identifying fatigue and can be used as a universal screen before deciding to administer longer scales such as the FACT-An or FSI for a more detailed work-up. The ZSDS Item 10 can identify potential patients, but the issue becomes what level of fatigue is clinically relevant and in need of treatment. Further research will need to explore what defines clinically relevant fatigue and what treatments should be initiated for different levels. FACT-An Outcome Table 2 Specificity and Sensitivity of the Zung Fatigue Item for Predicting Fatigue on the FACT-An Single Zung item for fatigue (raw score) True Negatives (FACT-An) Correctly Identified Negatives False Positives Specificity True Positives (FACT-An) Correctly Identified Positives False Negatives Sensitivity Fatigued (raw score above 115) Fatigued (raw score above 115) Fatigued (raw score above 115) 1 some of the time 2 good part of the time 3 all of the time only % % % % % % 2 9

6 936 Kirsh et al. Vol. 22 No. 5 November 2001 References 1. Cella D, Peterman A, Passik SD, et al. Progress toward guidelines for the management of fatigue. Oncology 1998; 12: Glaus A, Crow R, Hammond S. A qualitative study to explore the concept of fatigue/tiredness in cancer patients and in healthy individuals. Support Care Cancer 1996; 4: Vogelzang NJ, Breitbart W, Cella D, et al. Patient, caregiver, and oncologist perceptions of cancer-related fatigue: results of a tripart assessment survey. Semin Hematol 1997; 34: Irvine D, Vincent L, Graydon J, et al. The prevalence and correlates of fatigue in patients receiving treatment with chemotherapy and radiotherapy. A comparison with the fatigue experienced by healthy individuals. Cancer Nurs 1994; 17: Richardson A, Ream E. The experience of fatigue and other symptoms in patients receiving chemotherapy. Eur J Cancer Care 1996; 5: Ashbury FD, Findlay H, Reynolds B, McKerracher K. A canadian survey of cancer patients experiences: are their needs being met? J Pain Symptom Manage 1998; 16: Richardson A, Ream E, Wilson-Barnett J. Fatigue in patients receiving chemotherapy: patterns of change. Cancer Nurs 1998; 21: Passik S, Kirsh K, Donaghy K, et al. Patientrelated barriers to fatigue communication: initial validation of the fatigue management barriers questionnaire (in press). 9. Smets EM, Garssen B, Schuster-Uitterhoeve AL, et al. Fatigue in cancer patients. Br J Cancer 1993; 68: Chochinov HM, Wilson KG, Enns M, Lander S. Are you depressed?: screening for depression in the terminally ill. Am J Psychiatry 1997; 154: Greenhalgh T. How to read a paper: papers that report diagnostic or screening tests. Brit Med J 1997; 315: Cella D. Factors influencing quality of life in cancer patients: anemia and fatigue. Semin Oncol 1998; 25: Gall H. The basis of cancer fatigue: where does it come from? Eur J Cancer Care 1996; 5: Kirk J, Douglass R, Nelson E, et al. Chief complaint of fatigue: a prospective study. J Fam Pract 1990; 30: Pater JL, Zee B, Palmer M, et al. Fatigue in patients with cancer: results with national cancer institute of canada clinical trials group studies employing the EORTC QLQ-30. Support Care Cancer 1997; 5: Yarbro CH. Interventions for fatigue. Eur J Cancer Care 1996; 5: Kalman D, Villani L. Nutritional aspects of cancer-related fatigue. J Am Diet Assoc 1997; 97: Dimeo FC, Rumberger BG, Keul J. Aerobic exercise as therapy for cancer fatigue. Med Sci Sports Exerc 1998; 30: Dimeo FC, Tilmann MH, Bertz H, et al. Aerobic exercise of cancer patients after high dose chemotherapy and autologous peripheral stem cell transplantation. Cancer 1997; 79: Demetri GD, Kris M, Wade J, et al. Quality of life benefit in chemotherapy patients treated with epoetin alfa is independent of disease response or tumor type: results from a prospective community oncology study. J Clin Oncol 1998; 16: Glaspy J. The impact of epoetin alfa on quality of life during cancer chemotherapy: a fresh look at an old problem. Semin Hematol 1997; 34: Cohen J. A power primer. Psych Bull 1992;112: Zung W. Depression in the normal aged. Psychosomatics 1967; 7: Zung, W. Factors influencing the self-rating depression scale. Archives of General Psychiatry 1967; 16: Dugan W, McDonald M, Passik S, et al. Use of the zung self-rating depression scale in cancer patients: Feasibility as a screening tool. Psycho-Oncology 1998; 7: Tate D, Forchheimer M, Maynard F, et al. Comparing two measures of depression in spinal cord injury. Rehabilitation Psychology 1993; 38: Passik SD, Lundberg JC, Rosenfeld B, et al. Factor analysis of the zung self-rating depression scale in a large ambulatory oncology sample. Psychosomatics 2000; 41: Yellen S, Cella D, Webster K, et al. Measuring fatigue and other anemia-related symptoms with the functional assessment of cancer therapy (FACT-G) measurement system. J Pain Symptom Manage 1997; 13: Winstead-Fry P, Schultz A. Psychometric analysis of the functional assessment of cancer therapy-general (FACT-G) scale in a rural sample. Cancer 1997; 79: Brady M, Cella D, Mo, F, et al. Reliability and validity of the functional assessment of cancer therapy breast quality of life instrument. J Clin Oncol 1997; 15: Cella D, Bonomi A, Lloyd S, et al. Reliability and validity of the functional assessment of cancer therapy-lung (FACT-L) quality of life instrument. Lung Cancer 1995;12: Cella D, Tulsky D, Gray G, et al. The functional assessment of cancer therapy scale: Development and validation. J Clin Oncol 1993; 11: Hann D, Jacobsen P, Azzarello L, et al. Measure-

7 Vol. 22 No. 5 November 2001 Single Item Fatigue Screening 937 ment of fatigue in cancer patients: Development and validation of the Fatigue Symptom Inventory. Qual Life Research 1998; 7: Passik SD, Kirsh KL, Donaghy KB, et al. An attempt to employ the Zung self-rating depression scale as a lab test to trigger follow-up in ambulatory oncology clinics: criterion validity and detection. J Pain Symptom Manage, 2001; 21(4):