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The effect of sleep disturbance on quality of life in women with ovarian cancer
Gynecologic Oncology 123 (2011) 351–355
Contents lists available at ScienceDirect
Gynecologic Oncology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y g y n o
The effect of sleep disturbance on quality of life in women with ovarian cancer☆ Samith Sandadi a, Heidi E. Frasure a, Meredith J. Broderick b, Steven E. Waggoner a, Jacqualin A. Miller c, Vivian E. von Gruenigen c,⁎ a b c
Department of Obstetrics and Gynecology, University Hospitals Case Medical Center, Cleveland, OH 44106, USA Department of Neurology, University Hospitals Case Medical Center, Cleveland, OH 44106, USA Department of Obstetrics and Gynecology, SUMMA Akron City Hospitals, Akron, OH 44309, USA
a r t i c l e
i n f o
Article history: Received 6 May 2011 Accepted 18 July 2011 Keywords: Depression Ovarian cancer Sleep Quality of life
a b s t r a c t Objective. To estimate the prevalence of sleep disturbances, and to determine if there is an association between sleep disturbances with quality of life (QOL), depression or clinical demographic variables. Methods. Patients diagnosed with ovarian, fallopian tube or primary peritoneal cancer during the last 5 years completed questionnaires regarding sleep patterns and disturbances [Pittsburgh Sleep Quality Index (PSQI)], depression [Beck Depression inventory (BDI)], and QOL [The Functional Assessment of Cancer Therapy-Ovarian (FACT-O), fatigue module (− F)]. Data were analyzed by Student's t-test or Pearson correlation coefficient to determine if there were differences between PSQI score with QOL, depression or clinical demographic variables. Results. 86/275 (31% response) of patients returned the surveys. Mean age was 58.1 (SD = 14.6) years and 70% had advanced disease at diagnosis. Thirty-six percent had current disease of which 81% were receiving chemotherapy. Sixty-seven percent of patients had a PSQI score ≥ 5 corresponding to overall poor sleep quality and 46% of patients reported using sleep medication at least once during the prior month. PSQI score was significantly inversely correlated with all QOL domains (physical: r = −.599, p b .001, functional: r = −.692, p b .001, social: r = −.212, p b .001, emotional: r = −.379, p b .001, fatigue; r = −.655 p b .001) and with depression (r = .539, p b .001). PSQI was not correlated with age, time since diagnosis, number of previous chemotherapy regimens. PSQI score did not differ by current disease or chemotherapy status. Conclusions. Sleep disturbances reduce QOL, a prognostic indicator for survival, in ovarian cancer patients. These patients should undergo routine screening and would benefit from interventions that aim to promote restful sleep. © 2011 Elsevier Inc. All rights reserved.
Introduction Ovarian cancer is a devastating disease. It is the leading cause of gynecological cancer deaths and ranks as the fifth most frequent cause of cancer in women. In the United States alone, an estimated 22,000 new cases are diagnosed annually, and each year more than 13,000 patients die from the disease [1]. The majority of women are diagnosed with advanced disease (FIGO stages III/IV). Upon diagnosis patients are treated with extensive cytoreductive surgery followed by combination based taxane/platinum chemotherapy [2–4]. Patient's quality of life (QOL) markedly decreases after surgery with a slow improvement during adjuvant chemotherapy specifically in the physical, functional, and fatigue domains [5]. Adverse side effects of ovarian cancer treatment include fatigue, nausea, vomiting,
☆ Presentation at Meeting: The Society of Gynecologic Oncologists Annual Meeting, San Francisco, CA, March 14-17, 2010. ⁎ Corresponding author at: Summa Akron City Hospital, 525 East Market Med II, POB #2090, Akron, OH 44309, USA. Fax: + 1 330 375 7813. E-mail address: [email protected] (V.E. von Gruenigen). 0090-8258/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ygyno.2011.07.028
peripheral neuropathy and sleep disruption [6,7]. In ovarian cancer survivors, the greatest problems relating to QOL include decreased physical activity, pain, depression, sexuality, sleep disruption and individual characteristics [8,9]. Sleep disturbance is a well documented problem in cancer patients with sleep difficulties reported by 30–50% of newly diagnosed cancer patients [10–12]. Insufficient sleep contributes to decreasing functional status, fatigue, pain, wound healing, immune function, clinical anxiety and depression [13–17]. In breast cancer patients, sleep disruption has been shown to be a chronic problem causing severe distress, resulting in greater QOL deficits [18–21]. Similarly, sleep disturbances have been shown to adversely affect the QOL in patients with lung cancer [22]. QOL measured during treatment has been found to be a prognostic indicator for overall survival (OS) in women receiving chemotherapy for ovarian cancer [23]. Thus, improving QOL, which is affected by overall health, may signal an increased ability to tolerate chemotherapy and may influence survival. A recent ancillary analysis, conducted by the Gynecologic Oncology Group, of QOL data in ovarian cancer patients receiving adjuvant chemotherapy identified women whose
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Methods
properties (test–retest reliability r = .93, internal consistency α = .91) and assessment of symptoms is in accordance with the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders [28]. Additional demographic characteristics, medical characteristics and sleep patterns were collected using a hospital based questionnaire developed by the co-investigators. Hospital charts and gynecologic oncology division charts were reviewed by the PI and co-investigators to ascertain important patient demographic and clinical data. The variables collected included: age, body mass index, race/ethnicity, time since surgery, stage of disease, grade, chemotherapy type and treatment, recurrence and date of recurrence.
Study design and patient recruitment
Statistical analysis
This study was a prospective trial of patients with ovarian, peritoneal or fallopian tube cancer assessing their sleep behaviors, QOL and potential depression. Eligibility included: patients with a minimum age of eighteen, diagnosed with ovary, fallopian tube or primary peritoneal cancer during the last 5 years. Patients with any stage of disease and therapy were included in this study. All patients were approved for contact by their treating gynecologic oncologist before being sent any materials. The study was approved by the institutional review boards of University Hospital Case Medical Center and Summa Health Systems. Demographic data were collected through medical chart review and tumor registry records. Eligible patients were mailed two copies of the consent along with the survey and a cover letter explaining the study and purpose. Participants were asked to complete and return one copy of the consent form and questionnaires in pre-paid reply envelopes. Nonresponders were sent a second mailing after approximately 30 days.
Analyses in the study sample included determining the presence of sleep disturbances, and correlation of sleep disturbances with QOL, depression and clinical/demographic variables. Mean, standard deviations, and ranges of PSI, QOL and depression scores were obtained in this sample of patients. The Pearson or Spearman correlation coefficient (r) was calculated for FACT-G domain, PSQI and BDI scores and demographic and clinical variables. Student's t-test was used to compare scores based on disease and chemotherapy treatment status. Participants were divided into two categories according to PSQI score. Differences in QOL domains and depression scores were compared between patients having a PSQI score ≥5 versus those who had a score b 5 by use of independent samples t-test. Effect sizes (d) were calculated for the difference in means as d= (Mean1 − Mean2)/ SDpooled. Effect sizes of d =0.2 are considered small, d =0.5 moderate and d= 0.8 large [29]. We estimated that 30% of patients would return the surveys thus providing approximately 82 completed surveys. This sample size would allow a 95% confidence interval of ±0.10 around the point estimate obtained when estimating the proportion of patients with sleep disturbances. In addition, this sample size would allow for comparisons of 2–3 points in individual QOL domain scores and 5–7 points for total FACT-G between groups. These differences are considered meaningful and clinically relevant [25].
total QOL score was in the lowest quartile reported more problems with sleep (such as not sleeping well and forced to spend time in bed) as compared to women having QOL scores in the upper three quartiles [24]. The primary objective of this study was to estimate the prevalence of sleep disturbances, and to determine if there was a correlation between sleep disturbances with QOL, depression or clinical demographic variables. We hypothesized that ovarian cancer patients would have significant sleep disruption and that there would be an association with depression and patient characteristics.
Measures QOL was assessed with the Functional Assessment of Cancer TherapyGeneral (FACT-G) Version 4, a 27-item core questionnaire evaluating various domains of QOL including, physical, functional, family–social, and emotional domains [25]. The FACT-Ovarian subscale is a module used in patients to address ovarian cancer specific issues and contains 12 questions. An additional fatigue (−F) module with 13 items was used to assess how sleep disturbances and patterns are related to fatigue [26]. The FACT measures are rated on a 5-point Likert scale from 0 (not at all) to 5 (very much) with acceptable reliability, validity, and sensitivity to change over time [25]. Sleep was assessed by The Pittsburgh Sleep Quality Index (PSQI), a questionnaire developed to measure sleep quality during the previous month and to discriminate between good and poor sleepers. The PSQI is comprised of 19 self-rated questions to calculate 7 dimensions of sleep which include: subjective sleep quality, sleep latency (number of minutes to fall asleep), sleep duration (number of hours asleep per night), habitual sleep efficiency (length of time asleep as a percentage of the total time in bed), sleep disturbances, use of sleep medications (number of times sleep medication was used in the previous month, rated from 0 to ≥ 3), and daytime dysfunction (number of times subject reported trouble staying awake during daytime activities in previous month, rated from 0 to ≥ 3). A PSQI score of ≥ 5 is equal to poor sleep. The questions are brief and easy for most adolescents and adults to understand. This tool was designed to provide a reliable, valid, and standardized measure of sleep quality and has clinical value in oncologic research activities [27]. Depression was evaluated using the Beck Depression inventory (BDI), a 21-item, Likert-scaled instrument of depressive symptoms that is well-validated and frequently used in lifestyle research studies [28]. Each item is rated on a 4-point scale ranging from 0 to 3 (higher scores are associated with greater symptoms) and completion time takes approximately 10 min. The instrument has good psychometric
Results A total of 275 patients were identified and sent questionnaires. No patients were excluded based on obtaining physician permission to contact. Eighty-six (31% response) patients returned the surveys. The mean age of the respondents was 58.1 (SD = 14.6) years. Seventy percent had advanced disease (stage 3 or 4) at diagnosis and median time from diagnosis was 26.4 months. The majority of patients were Caucasian. At the time of the survey, 36% had current disease of which 81% were receiving chemotherapy (Table 1). Patient characteristics (age, race, stage, time since diagnosis, and treating physician) did not differ between those patients who did not respond to the survey versus those who returned the questionnaires. The majority of ovarian cancer patients had sleep disruption. A score of ≥ 5 on the PSQI corresponds to overall poor quality; the mean in all patients was 8.1 (SD = 4.7). Fifty-eight or 67% (95% confidence interval 56.4% to 76.9%) of patients had a PSQI score ≥ 5 corresponding to overall poor sleep quality. Thirty-three percent of respondents rated their sleep quality as fairly/very bad, the majority (72%) had sleep latency and 59% reported sleeping b 7 hours per night (Table 2). Thirty-seven (43%) patients reported using sleep medication at least once during the prior month and the majority had daytime dysfunction as the result of poor sleep. There was a relationship between poor sleep, and QOL. Significant differences in mean QOL domain scores were observed when comparing patients who had a PSQI score ≥ 5 to those with a score b 5 (Table 3). Differences for FACT-G domains wereN 2 points for all domains except
S. Sandadi et al. / Gynecologic Oncology 123 (2011) 351–355 Table 1 Patient demographics and clinical characteristics.
Table 3 QOL and depression score by PSQI categories.
Age, mean (SD) years Range Race Caucasian African–American Asian Unknown Selected co-morbidities Hypertension Diabetes mellitus Hypothyroid CHD Asthma Depression Stage of disease I–II III IV Time since diagnosis, months Median, range Prior chemotherapeutic regimens None 1 2 3 or more Diagnosed with recurrence in past Current disease Currently receiving chemotherapy
58.1 (14.6) 19–89 81 (94.2%) 2 (2.3%) 1 (1.2%) 2 (2.3%) 26 (30.2%) 4 (4.6%) 16 (18.6%) 3 (3.5%) 9 (10.5%) 16 (18.6%) 26 (30.2%) 46 (53.5%) 14 (16.3%)
9 (10.5%) 46 (53.5%) 19 (22.1%) 12 (13.9%) 35 (40.7%) 31 (36.0%) 25 (80.6%)
Table 2 PSQI sleep parameters (n = 86).
Sleep quality Very/fairly good Very/fairly bad Sleep latency, min b 15 min ≥ 15 min Sleep duration, hours ≥7 b7 Sleep efficiency, % N 85 ≤ 85 Sleep medication None N 1 a month Daytime dysfunction None ≥ 1 a month
Physical Functional Social Emotional Fatigue FACT-G, total BDI
PSQI b 5 (n=28)
Poor sleep quality Mean difference, PSQI ≥5 (n=58) 95% CI
p valuea Effect size (d)b
25.2 (2.8) 24.9 (2.8) 18.8 (2.8) 19.6 (3.4) 44.8 (6.5) 88.5 (8.1)
20.3 18.1 17.2 16.9 32.1 72.4
b.001 b.001 b.001 b.001 b.001 b.001
0.80 1.17 0.59 0.61 1.02 1.06
4.6 (3.9)
12.2 (8.8)
−7.6 (−11.3, −4.0) b.001
0.91
(6.6) (5.7) (2.6) (4.8) (12.6) (15.4)
4.8 (2.3, 7.4) 6.9 (4.6, 9.1) 1.6 (.42, 2.9) 2.7 (.73, 4.7) 12.7 (7.6, 17.7) 16.1 (9.9, 22.2)
Mean (SD). a Independent sample t-test used for comparison between PSQI b 5 and PSQI N 5 groups. b Effect size (d) calculated as the mean difference between PSQI categories divided by the pooled SD.
26.4 (1.3–60)
social (corresponding to minimally important differences), and all were statistically significant. The effect sizes for difference in means were moderate to large for all differences (range 0.59–1.17). PSQI score was significantly inversely correlated with all QOL domains (physical: r = −.599, p b .001, functional: r = −.692, p b .001, social: r = −.212, p b .001, emotional: r = −.379, p b .001, fatigue; r = −.655, p b .001) and PSQI score was significantly correlated with BDI (r = .539, p b .001). The more significant the sleep disruption the higher the depression score. There was a direct correlation between the PSQI and BDI. Fig. 1 is a scatter plot illustrating the linear relationship (R Sq Linear = 0.292). BDI was also inversely correlated with all QOL domains [physical, r = −.570, p b .001; social, r = −.370, p b .001; emotional, r = −.640, p b .001; functional, r = −.711, p b .001; fatigue, r = −.653, p b .001]. PSQI was not significantly correlated with age (r= .009, p = .942), time since diagnosis (r = .004, p = .977) or number of previous chemotherapy regimens (r = −.031, p = .816). Mean PSQI score in
Parameter
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n (%) 58 (67.4%) 28 (32.6%) 24 (27.9%) 62 (72.1%) 35 (40.7%) 51 (59.3%) 46 (49%) 47 (51%) 49 (57%) 37 (43%) 22 (25.6%) 64 (74.4%)
those with current disease was 8.1 (SD = 5.5) versus 8.9 (SD = 4.6) in those with no evidence of disease, p = .537 and was 8.7 (SD = 6.0) in those currently receiving chemotherapy versus 8.5 (SD = 4.5) in those not on chemotherapy at the time of the survey, p = .909. Discussion This study found that sleep disturbances are prevalent in ovarian cancer patients. In our sampled population, the majority of the patients reported poor sleep quality as evidenced by a score of ≥ 5 using the Pittsburgh Sleep Quality Index. These results are similar to previous studies investigating sleep disturbances in other cancer patients, however, the prevalence in this study was higher [21,30]. The results of this study and others will increase awareness among physicians taking care of ovarian cancer patients and aid with treatment strategies. Many cancer patients have sleep disruption; however the nature of the sleep disruption has not been well described. It is likely that cancer patients of any type would share some common causes of sleep disturbances including secondary effects of surgery, chemotherapy, pain medications, decreased mobility and depression [12]. However, there are likely to be factors that may be specific to cancer type. In ovarian cancer patients, numerous surgeries, sequential chemotherapy regimens (intraperitoneal and intravenous) and postmenopausal status may increase the incidence in the population [5–7]. The presence of depression may predispose to behaviorally mediated sleep disturbances for which sleep hygiene, medications, and/or cognitive behavioral therapy would be needed. Alternatively, in cancers affecting lung volumes or breathing mechanics, identification and treatment of sleep disordered breathing may be needed [11,22]. QOL is a prognostic indicator for OS in women receiving chemotherapy for ovarian cancer [23]. A recent meta-analysis using 30 randomized controlled trials from the European Organization for Research and Treatment of Cancer (EORTC) which included survival data for over 10,000 patients with 11 different cancer sites found that QOL was predictive for survival in patients with cancer [31]. This present study is important as QOL was significantly decreased in patients with sleep disturbances and all domains were affected. This is of particular interest when devising individualized treatment. Interventions studies have begun to target QOL in order to improve OS in cancer patients [13,18,21,32]. The medical literature is well documented with the multi-faceted relationship between sleep disturbance and depression. Recent research has shown poor sleep increases the risk of depression onset, impedes response to depression treatment, and increases relapse [33]. However, what is unclear is if the poor sleep or sleep disturbance precedes the onset of depression, comes after, is unrelated, or a co-morbidity [16,20,33]. In our research, it appears that the sleep disruption is more
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Fig. 1. Scatter plot for depression (BDI score) versus PSQI score.
prevalent in ovarian cancer patients than depression or anxiety indicating that treatment would theoretically be directed at the sleep disturbance when it occurs in isolation or at both when a sleep disturbance and depression are present [6,24]. A new anti-depressant called agomelatine, which is a melatonin MT1/MT2 agonist and 5-HT (2C) antagonist has been shown to improve sleep disturbances in patients with depression suggesting it may have promise for patients with ovarian cancer also suffering from sleep disturbances and depression [34]. This study did not find any significant correlation between certain patient characteristics and sleep disruption including: age, time since diagnosis, number of previous chemotherapy regimens, current disease or chemotherapy status [12,16,20]. In regards to age, our findings are different from recent results published which demonstrated that younger cancer patients (age b 50 years) reported a greater symptom burden when compared to older patients (age N 64 years) [20]. Palesh et al.'s paper included 11 different primary cancer sites including gynecology. This difference in findings may be due to our lower power or the differences among cancer populations. Limitations of the study include those inherent with surveys including response and recall bias; however, our response rate was reasonable. In addition, the response rate, while reasonable compared to the literature may also introduce bias. Patient characteristics of those who responded versus those who did not respond did not differ; however there may be unknown or unmeasured characteristics (i.e. socioeconomic status) that may have affected response. Medications to decrease side effects are routinely given with chemotherapy. Even though we found no difference is sleep patterns between those on and off chemotherapy there remains the potential as we did not have access to the premedication chemotherapy lists. As this study was cross-sectional in nature, there was no assessment of baseline sleep status. Therefore we were unable to accurately assess the impact of sleep disturbance on quality of life and whether there is an increase in the incidence of disturbances in this population. Rather, results from this study may be seen as hypothesis-generating and used to develop future studies on this topic. Sleep disturbances are prevalent in ovarian cancer patients and associated with significantly decreased QOL. When devising treatment strategies, various sleep parameters must be explored including: sleep quality, disrupted initiation and maintenance of sleep,
nighttime awakening, restless sleep and excessive daytime sleepiness [10]. Future interventional studies could include screening for specific sleep disorders (in relation to the different stages of cancer and consequent treatments), referral to sleep clinics, or medical treatments in order to improve QOL and thereby potentially improving OS. Other, more specific, options for further research include determination if the sleep disruption is due to abnormal breathing or impaired mobility due to patient co-morbidities. Clearly, this has been an unmet need in a vulnerable population that deserves further study. Conflict of interest statement The authors have no conflicts of interest to disclose relating to the submitted manuscript.
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Contents lists available at ScienceDirect
Gynecologic Oncology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y g y n o
The effect of sleep disturbance on quality of life in women with ovarian cancer☆ Samith Sandadi a, Heidi E. Frasure a, Meredith J. Broderick b, Steven E. Waggoner a, Jacqualin A. Miller c, Vivian E. von Gruenigen c,⁎ a b c
Department of Obstetrics and Gynecology, University Hospitals Case Medical Center, Cleveland, OH 44106, USA Department of Neurology, University Hospitals Case Medical Center, Cleveland, OH 44106, USA Department of Obstetrics and Gynecology, SUMMA Akron City Hospitals, Akron, OH 44309, USA
a r t i c l e
i n f o
Article history: Received 6 May 2011 Accepted 18 July 2011 Keywords: Depression Ovarian cancer Sleep Quality of life
a b s t r a c t Objective. To estimate the prevalence of sleep disturbances, and to determine if there is an association between sleep disturbances with quality of life (QOL), depression or clinical demographic variables. Methods. Patients diagnosed with ovarian, fallopian tube or primary peritoneal cancer during the last 5 years completed questionnaires regarding sleep patterns and disturbances [Pittsburgh Sleep Quality Index (PSQI)], depression [Beck Depression inventory (BDI)], and QOL [The Functional Assessment of Cancer Therapy-Ovarian (FACT-O), fatigue module (− F)]. Data were analyzed by Student's t-test or Pearson correlation coefficient to determine if there were differences between PSQI score with QOL, depression or clinical demographic variables. Results. 86/275 (31% response) of patients returned the surveys. Mean age was 58.1 (SD = 14.6) years and 70% had advanced disease at diagnosis. Thirty-six percent had current disease of which 81% were receiving chemotherapy. Sixty-seven percent of patients had a PSQI score ≥ 5 corresponding to overall poor sleep quality and 46% of patients reported using sleep medication at least once during the prior month. PSQI score was significantly inversely correlated with all QOL domains (physical: r = −.599, p b .001, functional: r = −.692, p b .001, social: r = −.212, p b .001, emotional: r = −.379, p b .001, fatigue; r = −.655 p b .001) and with depression (r = .539, p b .001). PSQI was not correlated with age, time since diagnosis, number of previous chemotherapy regimens. PSQI score did not differ by current disease or chemotherapy status. Conclusions. Sleep disturbances reduce QOL, a prognostic indicator for survival, in ovarian cancer patients. These patients should undergo routine screening and would benefit from interventions that aim to promote restful sleep. © 2011 Elsevier Inc. All rights reserved.
Introduction Ovarian cancer is a devastating disease. It is the leading cause of gynecological cancer deaths and ranks as the fifth most frequent cause of cancer in women. In the United States alone, an estimated 22,000 new cases are diagnosed annually, and each year more than 13,000 patients die from the disease [1]. The majority of women are diagnosed with advanced disease (FIGO stages III/IV). Upon diagnosis patients are treated with extensive cytoreductive surgery followed by combination based taxane/platinum chemotherapy [2–4]. Patient's quality of life (QOL) markedly decreases after surgery with a slow improvement during adjuvant chemotherapy specifically in the physical, functional, and fatigue domains [5]. Adverse side effects of ovarian cancer treatment include fatigue, nausea, vomiting,
☆ Presentation at Meeting: The Society of Gynecologic Oncologists Annual Meeting, San Francisco, CA, March 14-17, 2010. ⁎ Corresponding author at: Summa Akron City Hospital, 525 East Market Med II, POB #2090, Akron, OH 44309, USA. Fax: + 1 330 375 7813. E-mail address: [email protected] (V.E. von Gruenigen). 0090-8258/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ygyno.2011.07.028
peripheral neuropathy and sleep disruption [6,7]. In ovarian cancer survivors, the greatest problems relating to QOL include decreased physical activity, pain, depression, sexuality, sleep disruption and individual characteristics [8,9]. Sleep disturbance is a well documented problem in cancer patients with sleep difficulties reported by 30–50% of newly diagnosed cancer patients [10–12]. Insufficient sleep contributes to decreasing functional status, fatigue, pain, wound healing, immune function, clinical anxiety and depression [13–17]. In breast cancer patients, sleep disruption has been shown to be a chronic problem causing severe distress, resulting in greater QOL deficits [18–21]. Similarly, sleep disturbances have been shown to adversely affect the QOL in patients with lung cancer [22]. QOL measured during treatment has been found to be a prognostic indicator for overall survival (OS) in women receiving chemotherapy for ovarian cancer [23]. Thus, improving QOL, which is affected by overall health, may signal an increased ability to tolerate chemotherapy and may influence survival. A recent ancillary analysis, conducted by the Gynecologic Oncology Group, of QOL data in ovarian cancer patients receiving adjuvant chemotherapy identified women whose
352
S. Sandadi et al. / Gynecologic Oncology 123 (2011) 351–355
Methods
properties (test–retest reliability r = .93, internal consistency α = .91) and assessment of symptoms is in accordance with the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders [28]. Additional demographic characteristics, medical characteristics and sleep patterns were collected using a hospital based questionnaire developed by the co-investigators. Hospital charts and gynecologic oncology division charts were reviewed by the PI and co-investigators to ascertain important patient demographic and clinical data. The variables collected included: age, body mass index, race/ethnicity, time since surgery, stage of disease, grade, chemotherapy type and treatment, recurrence and date of recurrence.
Study design and patient recruitment
Statistical analysis
This study was a prospective trial of patients with ovarian, peritoneal or fallopian tube cancer assessing their sleep behaviors, QOL and potential depression. Eligibility included: patients with a minimum age of eighteen, diagnosed with ovary, fallopian tube or primary peritoneal cancer during the last 5 years. Patients with any stage of disease and therapy were included in this study. All patients were approved for contact by their treating gynecologic oncologist before being sent any materials. The study was approved by the institutional review boards of University Hospital Case Medical Center and Summa Health Systems. Demographic data were collected through medical chart review and tumor registry records. Eligible patients were mailed two copies of the consent along with the survey and a cover letter explaining the study and purpose. Participants were asked to complete and return one copy of the consent form and questionnaires in pre-paid reply envelopes. Nonresponders were sent a second mailing after approximately 30 days.
Analyses in the study sample included determining the presence of sleep disturbances, and correlation of sleep disturbances with QOL, depression and clinical/demographic variables. Mean, standard deviations, and ranges of PSI, QOL and depression scores were obtained in this sample of patients. The Pearson or Spearman correlation coefficient (r) was calculated for FACT-G domain, PSQI and BDI scores and demographic and clinical variables. Student's t-test was used to compare scores based on disease and chemotherapy treatment status. Participants were divided into two categories according to PSQI score. Differences in QOL domains and depression scores were compared between patients having a PSQI score ≥5 versus those who had a score b 5 by use of independent samples t-test. Effect sizes (d) were calculated for the difference in means as d= (Mean1 − Mean2)/ SDpooled. Effect sizes of d =0.2 are considered small, d =0.5 moderate and d= 0.8 large [29]. We estimated that 30% of patients would return the surveys thus providing approximately 82 completed surveys. This sample size would allow a 95% confidence interval of ±0.10 around the point estimate obtained when estimating the proportion of patients with sleep disturbances. In addition, this sample size would allow for comparisons of 2–3 points in individual QOL domain scores and 5–7 points for total FACT-G between groups. These differences are considered meaningful and clinically relevant [25].
total QOL score was in the lowest quartile reported more problems with sleep (such as not sleeping well and forced to spend time in bed) as compared to women having QOL scores in the upper three quartiles [24]. The primary objective of this study was to estimate the prevalence of sleep disturbances, and to determine if there was a correlation between sleep disturbances with QOL, depression or clinical demographic variables. We hypothesized that ovarian cancer patients would have significant sleep disruption and that there would be an association with depression and patient characteristics.
Measures QOL was assessed with the Functional Assessment of Cancer TherapyGeneral (FACT-G) Version 4, a 27-item core questionnaire evaluating various domains of QOL including, physical, functional, family–social, and emotional domains [25]. The FACT-Ovarian subscale is a module used in patients to address ovarian cancer specific issues and contains 12 questions. An additional fatigue (−F) module with 13 items was used to assess how sleep disturbances and patterns are related to fatigue [26]. The FACT measures are rated on a 5-point Likert scale from 0 (not at all) to 5 (very much) with acceptable reliability, validity, and sensitivity to change over time [25]. Sleep was assessed by The Pittsburgh Sleep Quality Index (PSQI), a questionnaire developed to measure sleep quality during the previous month and to discriminate between good and poor sleepers. The PSQI is comprised of 19 self-rated questions to calculate 7 dimensions of sleep which include: subjective sleep quality, sleep latency (number of minutes to fall asleep), sleep duration (number of hours asleep per night), habitual sleep efficiency (length of time asleep as a percentage of the total time in bed), sleep disturbances, use of sleep medications (number of times sleep medication was used in the previous month, rated from 0 to ≥ 3), and daytime dysfunction (number of times subject reported trouble staying awake during daytime activities in previous month, rated from 0 to ≥ 3). A PSQI score of ≥ 5 is equal to poor sleep. The questions are brief and easy for most adolescents and adults to understand. This tool was designed to provide a reliable, valid, and standardized measure of sleep quality and has clinical value in oncologic research activities [27]. Depression was evaluated using the Beck Depression inventory (BDI), a 21-item, Likert-scaled instrument of depressive symptoms that is well-validated and frequently used in lifestyle research studies [28]. Each item is rated on a 4-point scale ranging from 0 to 3 (higher scores are associated with greater symptoms) and completion time takes approximately 10 min. The instrument has good psychometric
Results A total of 275 patients were identified and sent questionnaires. No patients were excluded based on obtaining physician permission to contact. Eighty-six (31% response) patients returned the surveys. The mean age of the respondents was 58.1 (SD = 14.6) years. Seventy percent had advanced disease (stage 3 or 4) at diagnosis and median time from diagnosis was 26.4 months. The majority of patients were Caucasian. At the time of the survey, 36% had current disease of which 81% were receiving chemotherapy (Table 1). Patient characteristics (age, race, stage, time since diagnosis, and treating physician) did not differ between those patients who did not respond to the survey versus those who returned the questionnaires. The majority of ovarian cancer patients had sleep disruption. A score of ≥ 5 on the PSQI corresponds to overall poor quality; the mean in all patients was 8.1 (SD = 4.7). Fifty-eight or 67% (95% confidence interval 56.4% to 76.9%) of patients had a PSQI score ≥ 5 corresponding to overall poor sleep quality. Thirty-three percent of respondents rated their sleep quality as fairly/very bad, the majority (72%) had sleep latency and 59% reported sleeping b 7 hours per night (Table 2). Thirty-seven (43%) patients reported using sleep medication at least once during the prior month and the majority had daytime dysfunction as the result of poor sleep. There was a relationship between poor sleep, and QOL. Significant differences in mean QOL domain scores were observed when comparing patients who had a PSQI score ≥ 5 to those with a score b 5 (Table 3). Differences for FACT-G domains wereN 2 points for all domains except
S. Sandadi et al. / Gynecologic Oncology 123 (2011) 351–355 Table 1 Patient demographics and clinical characteristics.
Table 3 QOL and depression score by PSQI categories.
Age, mean (SD) years Range Race Caucasian African–American Asian Unknown Selected co-morbidities Hypertension Diabetes mellitus Hypothyroid CHD Asthma Depression Stage of disease I–II III IV Time since diagnosis, months Median, range Prior chemotherapeutic regimens None 1 2 3 or more Diagnosed with recurrence in past Current disease Currently receiving chemotherapy
58.1 (14.6) 19–89 81 (94.2%) 2 (2.3%) 1 (1.2%) 2 (2.3%) 26 (30.2%) 4 (4.6%) 16 (18.6%) 3 (3.5%) 9 (10.5%) 16 (18.6%) 26 (30.2%) 46 (53.5%) 14 (16.3%)
9 (10.5%) 46 (53.5%) 19 (22.1%) 12 (13.9%) 35 (40.7%) 31 (36.0%) 25 (80.6%)
Table 2 PSQI sleep parameters (n = 86).
Sleep quality Very/fairly good Very/fairly bad Sleep latency, min b 15 min ≥ 15 min Sleep duration, hours ≥7 b7 Sleep efficiency, % N 85 ≤ 85 Sleep medication None N 1 a month Daytime dysfunction None ≥ 1 a month
Physical Functional Social Emotional Fatigue FACT-G, total BDI
PSQI b 5 (n=28)
Poor sleep quality Mean difference, PSQI ≥5 (n=58) 95% CI
p valuea Effect size (d)b
25.2 (2.8) 24.9 (2.8) 18.8 (2.8) 19.6 (3.4) 44.8 (6.5) 88.5 (8.1)
20.3 18.1 17.2 16.9 32.1 72.4
b.001 b.001 b.001 b.001 b.001 b.001
0.80 1.17 0.59 0.61 1.02 1.06
4.6 (3.9)
12.2 (8.8)
−7.6 (−11.3, −4.0) b.001
0.91
(6.6) (5.7) (2.6) (4.8) (12.6) (15.4)
4.8 (2.3, 7.4) 6.9 (4.6, 9.1) 1.6 (.42, 2.9) 2.7 (.73, 4.7) 12.7 (7.6, 17.7) 16.1 (9.9, 22.2)
Mean (SD). a Independent sample t-test used for comparison between PSQI b 5 and PSQI N 5 groups. b Effect size (d) calculated as the mean difference between PSQI categories divided by the pooled SD.
26.4 (1.3–60)
social (corresponding to minimally important differences), and all were statistically significant. The effect sizes for difference in means were moderate to large for all differences (range 0.59–1.17). PSQI score was significantly inversely correlated with all QOL domains (physical: r = −.599, p b .001, functional: r = −.692, p b .001, social: r = −.212, p b .001, emotional: r = −.379, p b .001, fatigue; r = −.655, p b .001) and PSQI score was significantly correlated with BDI (r = .539, p b .001). The more significant the sleep disruption the higher the depression score. There was a direct correlation between the PSQI and BDI. Fig. 1 is a scatter plot illustrating the linear relationship (R Sq Linear = 0.292). BDI was also inversely correlated with all QOL domains [physical, r = −.570, p b .001; social, r = −.370, p b .001; emotional, r = −.640, p b .001; functional, r = −.711, p b .001; fatigue, r = −.653, p b .001]. PSQI was not significantly correlated with age (r= .009, p = .942), time since diagnosis (r = .004, p = .977) or number of previous chemotherapy regimens (r = −.031, p = .816). Mean PSQI score in
Parameter
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n (%) 58 (67.4%) 28 (32.6%) 24 (27.9%) 62 (72.1%) 35 (40.7%) 51 (59.3%) 46 (49%) 47 (51%) 49 (57%) 37 (43%) 22 (25.6%) 64 (74.4%)
those with current disease was 8.1 (SD = 5.5) versus 8.9 (SD = 4.6) in those with no evidence of disease, p = .537 and was 8.7 (SD = 6.0) in those currently receiving chemotherapy versus 8.5 (SD = 4.5) in those not on chemotherapy at the time of the survey, p = .909. Discussion This study found that sleep disturbances are prevalent in ovarian cancer patients. In our sampled population, the majority of the patients reported poor sleep quality as evidenced by a score of ≥ 5 using the Pittsburgh Sleep Quality Index. These results are similar to previous studies investigating sleep disturbances in other cancer patients, however, the prevalence in this study was higher [21,30]. The results of this study and others will increase awareness among physicians taking care of ovarian cancer patients and aid with treatment strategies. Many cancer patients have sleep disruption; however the nature of the sleep disruption has not been well described. It is likely that cancer patients of any type would share some common causes of sleep disturbances including secondary effects of surgery, chemotherapy, pain medications, decreased mobility and depression [12]. However, there are likely to be factors that may be specific to cancer type. In ovarian cancer patients, numerous surgeries, sequential chemotherapy regimens (intraperitoneal and intravenous) and postmenopausal status may increase the incidence in the population [5–7]. The presence of depression may predispose to behaviorally mediated sleep disturbances for which sleep hygiene, medications, and/or cognitive behavioral therapy would be needed. Alternatively, in cancers affecting lung volumes or breathing mechanics, identification and treatment of sleep disordered breathing may be needed [11,22]. QOL is a prognostic indicator for OS in women receiving chemotherapy for ovarian cancer [23]. A recent meta-analysis using 30 randomized controlled trials from the European Organization for Research and Treatment of Cancer (EORTC) which included survival data for over 10,000 patients with 11 different cancer sites found that QOL was predictive for survival in patients with cancer [31]. This present study is important as QOL was significantly decreased in patients with sleep disturbances and all domains were affected. This is of particular interest when devising individualized treatment. Interventions studies have begun to target QOL in order to improve OS in cancer patients [13,18,21,32]. The medical literature is well documented with the multi-faceted relationship between sleep disturbance and depression. Recent research has shown poor sleep increases the risk of depression onset, impedes response to depression treatment, and increases relapse [33]. However, what is unclear is if the poor sleep or sleep disturbance precedes the onset of depression, comes after, is unrelated, or a co-morbidity [16,20,33]. In our research, it appears that the sleep disruption is more
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Fig. 1. Scatter plot for depression (BDI score) versus PSQI score.
prevalent in ovarian cancer patients than depression or anxiety indicating that treatment would theoretically be directed at the sleep disturbance when it occurs in isolation or at both when a sleep disturbance and depression are present [6,24]. A new anti-depressant called agomelatine, which is a melatonin MT1/MT2 agonist and 5-HT (2C) antagonist has been shown to improve sleep disturbances in patients with depression suggesting it may have promise for patients with ovarian cancer also suffering from sleep disturbances and depression [34]. This study did not find any significant correlation between certain patient characteristics and sleep disruption including: age, time since diagnosis, number of previous chemotherapy regimens, current disease or chemotherapy status [12,16,20]. In regards to age, our findings are different from recent results published which demonstrated that younger cancer patients (age b 50 years) reported a greater symptom burden when compared to older patients (age N 64 years) [20]. Palesh et al.'s paper included 11 different primary cancer sites including gynecology. This difference in findings may be due to our lower power or the differences among cancer populations. Limitations of the study include those inherent with surveys including response and recall bias; however, our response rate was reasonable. In addition, the response rate, while reasonable compared to the literature may also introduce bias. Patient characteristics of those who responded versus those who did not respond did not differ; however there may be unknown or unmeasured characteristics (i.e. socioeconomic status) that may have affected response. Medications to decrease side effects are routinely given with chemotherapy. Even though we found no difference is sleep patterns between those on and off chemotherapy there remains the potential as we did not have access to the premedication chemotherapy lists. As this study was cross-sectional in nature, there was no assessment of baseline sleep status. Therefore we were unable to accurately assess the impact of sleep disturbance on quality of life and whether there is an increase in the incidence of disturbances in this population. Rather, results from this study may be seen as hypothesis-generating and used to develop future studies on this topic. Sleep disturbances are prevalent in ovarian cancer patients and associated with significantly decreased QOL. When devising treatment strategies, various sleep parameters must be explored including: sleep quality, disrupted initiation and maintenance of sleep,
nighttime awakening, restless sleep and excessive daytime sleepiness [10]. Future interventional studies could include screening for specific sleep disorders (in relation to the different stages of cancer and consequent treatments), referral to sleep clinics, or medical treatments in order to improve QOL and thereby potentially improving OS. Other, more specific, options for further research include determination if the sleep disruption is due to abnormal breathing or impaired mobility due to patient co-morbidities. Clearly, this has been an unmet need in a vulnerable population that deserves further study. Conflict of interest statement The authors have no conflicts of interest to disclose relating to the submitted manuscript.
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