Psychometric properties of a single-item scale to assess sleep quality among individuals with fibromyalgia

<p>Abstract</p> <p>Background</p> <p>Sleep disturbances are a common and bothersome symptom of fibromyalgia (FM). This study reports psychometric properties of a single-item scale to assess sleep quality among individuals with FM.</p> <p>Methods</p> <p>Analyses were based on data from two randomized, double-blind, placebo-controlled trials of pregabalin (studies 1056 and 1077). In a daily diary, patients reported the quality of their sleep on a numeric rating scale ranging from 0 ("best possible sleep") to 10 ("worst possible sleep"). Test re-test reliability of the Sleep Quality Scale was evaluated by computing intraclass correlation coefficients. Pearson correlation coefficients were computed between baseline Sleep Quality scores and baseline pain diary and Medical Outcomes Study (MOS) Sleep scores. Responsiveness to treatment was evaluated by standardized effect sizes computed as the difference between least squares mean changes in Sleep Quality scores in the pregabalin and placebo groups divided by the standard deviation of Sleep Quality scores across all patients at baseline.</p> <p>Results</p> <p>Studies 1056 and 1077 included 748 and 745 patients, respectively. Most patients were female (study 1056: 94.4%; study 1077: 94.5%) and white (study 1056: 90.2%; study 1077: 91.0%). Mean ages were 48.8 years (study 1056) and 50.1 years (study 1077). Test re-test reliability coefficients of the Sleep Quality Scale were 0.91 and 0.90 in the 1056 and 1077 studies, respectively. Pearson correlation coefficients between baseline Sleep Quality scores and baseline pain diary scores were 0.64 (p < 0.001) and 0.58 (p < 0.001) in the 1056 and 1077 studies, respectively. Correlations between the Sleep Quality Scale and the MOS Sleep subscales were statistically significant (p < 0.01), except for the MOS Snoring subscale. Across both studies, standardized effect sizes were generally moderate (0.46 to 0.52) for the 300 mg group and moderate (0.59) or moderate-to-large (0.70) for the 450 mg group. In study 1056, the effect size for the 600 mg group was moderate-to-large (0.73). In study 1077, the effect size for the 600 mg group was large (0.82).</p> <p>Conclusion</p> <p>These results provide evidence of the reproducibility, convergent validity, and responsiveness to treatment of the Sleep Quality Scale and provide a foundation for its further use and evaluation in FM patients.</p

Prevalence of menopausal symptoms among mid-life women: findings from electronic medical records

BACKGROUND: To assess the prevalence of menopausal symptoms among women prescribed hormone therapy (HT) using electronic medical record data from a regional healthcare organization. METHODS: Retrospective data from the Reliant Medical Group from 1/1/2006-12/31/2011 were assessed for 102 randomly-selected patients. Study eligibility criteria included: females aged 45 to 65; prescribed oral or transdermal HT; no history of breast cancer, venous thromboembolism, stroke, gynecological cancer, or hysterectomy; continuously enrolled in the health plan for 1 year before and after the first observed HT prescription. Prevalence of menopause-related symptoms was analyzed descriptively at both the patient and visit levels. RESULTS: Mean age of patients was 54 years. The most common menopausal symptoms were: hot flushes (40%), night sweats (17%), insomnia (16%), vaginal dryness (13%), mood disorders (12%), and weight gain (12%). Among the 102 patients, 163 individual visits listing menopausal symptoms were identified, of which hot flushes (71 visits) were the most common symptom identified. CONCLUSION: Our findings provide recent data on the types of menopausal symptoms experienced by mid-life women prescribed HT. Electronic medical records may be a rich source of data for future studies of menopausal symptoms in this population

Relationships between changes in pain severity and other patient-reported outcomes: an analysis in patients with posttraumatic peripheral neuropathic pain

<p>Abstract</p> <p>Background</p> <p>The objective of this study is to use the pain numeric rating scale (NRS) to evaluate associations between change in pain severity and changes in sleep, function, and mood assessed via patient-reported outcomes (PROs) in patients with posttraumatic pain.</p> <p>Methods</p> <p>This is a secondary analysis of a clinical trial evaluating pregabalin in patients with posttraumatic peripheral neuropathic pain (N = 254). Regression models were used to determine associations between changes in pain (0-10 NRS) as the predictor and scores on the following PRO measures as the outcome: Pain Interference Index; Hospital Anxiety and Depression Scale anxiety and depression subscales; Medical Outcomes Study-Sleep Scale 9-item Sleep Problems Index and Sleep Disturbance subscale; and Daily Sleep Interference Scale (0-10 NRS).</p> <p>Results</p> <p>Change in pain severity showed clear, direct relationships with changes in function, anxiety, depression, and sleep PROs, all of which were statistically significant (<it>P </it><.001). Results from subgroup analyses (≥30% or ≥50% pain responders, pregabalin or placebo treatment, age ≤ 51 years or > 51 years) tended to be consistent with results from the overall sample.</p> <p>Conclusions</p> <p>Overall, a direct relationship exists between pain and various aspects of patient's well-being and functioning, which can provide a quantitative assessment of how improvements in pain may be expected to relate to other patient outcomes. (<url>http://ClinicalTrials.gov</url> Identifier number NCT00292188; EudraCT #2005-003048-78).</p

Pain and inflammation as mediators of tofacitinib treatment effect on fatigue in patients with ankylosing spondylitis: a mediation analysis

Introduction: Tofacitinib is an oral Janus kinase inhibitor for treatment of ankylosing spondylitis (AS). Using mediation modelling, we describe interrelationships between fatigue, pain, morning stiffness, C-reactive protein (CRP) and tofacitinib treatment in patients with AS. Methods: Data from phase 2 (NCT01786668)/phase 3 (NCT03502616) studies of patients receiving tofacitinib 5 mg twice daily (BID) or placebo were used. Initial models included treatment as the independent binary variable (tofacitinib 5 mg BID versus placebo); fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F; model A] or Bath AS Disease Activity Index [BASDAI] Q1 [model B]) as the dependent variable; and pain (total back pain/nocturnal spinal pain [model A] or pain measured by BASDAI Q2/3 [model B]), morning stiffness (BASDAI Q5/6) and CRP as mediator variables. Results: Pooled data from 370/371 patients were included in models A/B. Initial models demonstrated that tofacitinib treatment affects fatigue mainly indirectly via pain and morning stiffness. As a result, initial models were respecified to exclude direct treatment effect and the indirect effect via CRP. For respecified model A, 44.0% of the indirect effect of tofacitinib treatment on fatigue was mediated via back pain/morning stiffness, 40.0% via morning stiffness alone and 16.0% via back pain alone (all P < 0.05). For respecified model B, 80.8% of the indirect effect of tofacitinib treatment on fatigue was mediated via pain/morning stiffness and 19.2% via pain alone (both P < 0.05). Conclusions: In tofacitinib-treated patients with AS, improvements in fatigue were collectively mediated through combined treatment effects on morning stiffness and pain

Patterns of Anti-Osteoporosis Medication Use among Women at High Risk of Fracture : Findings from the Global Longitudinal Study of Osteoporosis in Women (GLOW)

To assess patterns of anti-osteoporosis medication (AOM) use over 3 years among women at high risk of major fracture. The GLOW registry follows a cohort of more than 40,000 women aged ≥55 from 615 primary care practices in 10 countries. Self-administered surveys (baseline, 12, 24, and 36 months) collected data on patient characteristics, perception of fracture risk, and AOM use. FRAX scores were calculated from the baseline surveys and women classified as high risk if their FRAX 10-year probability of major fracture was ≥20%. A total of 5774 women were classified as at high risk and had complete data over 3 years. At baseline, 2271 (39%) reported receiving AOM, 739 (13%) reported prior but not current use, and 2764 (48%) said they had never used AOM. Over 3 years, 85% of baseline non-users continued as non-users and 15% initiated AOM; among baseline users, 49% continued the same medication class, 29% stopped AOM, and 12% switched. Women who stopped AOM were less likely to self-report osteoporosis (HR 0.56, 95% CI 0.42-0.75) than women who continued AOM. Compared with non-users who did not begin treatment, women initiating AOM were more likely to report a diagnosis of osteoporosis (HR 11.3, 95% CI 8.2-15.5) or osteopenia (HR 4.1, 95% CI 2.9-5.7) and be very concerned about osteoporosis (HR 1.9, 95% CI 1.3-2.8). Less than 40% of women at high risk of fracture reported taking AOM. Women who stopped AOM were less likely to believe they have osteoporosis. Women who initiated treatment appeared motivated primarily by a diagnosis of osteoporosis or osteopenia and concern about the condition

Patterns of anti-osteoporosis medication use among women at high risk of fracture: findings from the Global Longitudinal Study of Osteoporosis in Women (GLOW)

OBJECTIVE: To assess patterns of anti-osteoporosis medication (AOM) use over 3 years among women at high risk of major fracture. METHODS: The GLOW registry follows a cohort of more than 40,000 women aged \u3e /= 55 from 615 primary care practices in 10 countries. Self-administered surveys (baseline, 12, 24, and 36 months) collected data on patient characteristics, perception of fracture risk, and AOM use. FRAX scores were calculated from the baseline surveys and women classified as high risk if their FRAX 10-year probability of major fracture was \u3e /= 20%. RESULTS: A total of 5774 women were classified as at high risk and had complete data over 3 years. At baseline, 2271 (39%) reported receiving AOM, 739 (13%) reported prior but not current use, and 2764 (48%) said they had never used AOM. Over 3 years, 85% of baseline non-users continued as non-users and 15% initiated AOM; among baseline users, 49% continued the same medication class, 29% stopped AOM, and 12% switched. Women who stopped AOM were less likely to self-report osteoporosis (HR 0.56, 95% CI 0.42-0.75) than women who continued AOM. Compared with non-users who did not begin treatment, women initiating AOM were more likely to report a diagnosis of osteoporosis (HR 11.3, 95% CI 8.2-15.5) or osteopenia (HR 4.1, 95% CI 2.9-5.7) and be very concerned about osteoporosis (HR 1.9, 95% CI 1.3-2.8). CONCLUSIONS: Less than 40% of women at high risk of fracture reported taking AOM. Women who stopped AOM were less likely to believe they have osteoporosis. Women who initiated treatment appeared motivated primarily by a diagnosis of osteoporosis or osteopenia and concern about the condition

Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters.</p> <p>Methods</p> <p>Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain ≥40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships.</p> <p>Results</p> <p>Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions).</p> <p>Conclusion</p> <p>Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.</p