Nancy Fugate Woods1, Chancellor Hohensee, Janet S Carpenter, Lee Cohen, Kristine Ensrud, Ellen W Freeman, Katherine A Guthrie, Hadine Joffe, Andrea Z LaCroix, Julie L Otte. 1. 1Biobehavioral Nursing, University of Washington 2Fred Hutchinson Cancer Research Center, Seattle, WA 3Science of Nursing Care, School of Nursing, Indiana University, Indianapolis, IN 4Center for Women's Mental Health; Perinatal and Reproductive Psychiatry Clinical Research Program, Massachusetts General Hospital, Boston, MA 5Department of Medicine and Division of Epidemiology and Community Health, University of Minnesota 6Center for Chronic Disease Outcomes Research, Minneapolis VA Health Care System, Minneapolis, MN 7Departments of Obstetrics/Gynecology and Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 8Harvard Medical School, Department of Psychiatry, Brigham and Women's Hospital and Dana Farber Cancer Institute, Boston, MA 9Department of Family Medicine and Public Health, University of California San Diego, San Diego, CA.
Abstract
OBJECTIVE: Our objective was to identify symptom clusters using standardized measures completed by participants in the Menopausal Strategies: Finding Lasting Answers to Symptoms and Health clinical trial at baseline, including hot flash interference, and sleep, depressive, anxiety, and pain symptoms. METHODS: Data from all women randomized to interventions and controls from Menopausal Strategies: Finding Lasting Answers to Symptoms and Health studies 1, 2, and 3 (N = 899) were included; 797 with complete data were used in the analyses. Scores from standardized measures obtained at baseline included the following: Hot Flash-Related Daily Interference Scale, Insomnia Severity Index, Pittsburgh Sleep Quality Index, Patient Health Questionnaire-9 measure of depressed mood, Generalized Anxiety Disorder, and Brief Pain Inventory PEG scores (pain intensity [P], interference with enjoyment of life [E], and interference with daily activity [G]). Latent class analysis was used to identify symptom clusters using standardized scale scores and their established cut points. RESULTS: We identified five classes using the Bayesian Information Criterion and the Akaike Information Criterion. Women in classes 1 and 2 had high hot flash interference levels relative to the others, and class 1 (10.5% of total) included severe hot flash interference, severe sleep symptoms, and moderately severe pain symptoms (hot flash, sleep, pain). In class 2 (14.1%), severe hot flash interference was paired with the severe sleep symptoms, and moderate to severe depressed and anxious mood symptoms and pain (hot flash, sleep, mood, pain). In class 3 (39.6%), women reported moderately severe sleep symptoms with moderate hot flash interference, and low severity mood and pain symptoms (hot flash, sleep). Those in class 4 (7.0%) reported moderate hot flash interference with severe levels of anxiety and depressed mood symptoms, but low levels of other symptoms (hot flash, mood). Women in class 5 (28.7%) reported the lowest levels of all the five symptoms (low severity symptoms). CONCLUSIONS: Women meeting hot flash frequency criteria for inclusion in clinical trials exhibited multiple co-occurring symptoms that clustered into identifiable groups according to symptom interference and severity. Variability of symptom profiles between the classes was evident, indicating that the classes were composed of differing symptom types and not simply differing severity levels. These symptom clusters may be useful phenotypes for differentiating treatment effects or evaluating associations with biomarkers or genes.
OBJECTIVE: Our objective was to identify symptom clusters using standardized measures completed by participants in the Menopausal Strategies: Finding Lasting Answers to Symptoms and Health clinical trial at baseline, including hot flash interference, and sleep, depressive, anxiety, and pain symptoms. METHODS: Data from all women randomized to interventions and controls from Menopausal Strategies: Finding Lasting Answers to Symptoms and Health studies 1, 2, and 3 (N = 899) were included; 797 with complete data were used in the analyses. Scores from standardized measures obtained at baseline included the following: Hot Flash-Related Daily Interference Scale, Insomnia Severity Index, Pittsburgh Sleep Quality Index, Patient Health Questionnaire-9 measure of depressed mood, Generalized Anxiety Disorder, and Brief Pain Inventory PEG scores (pain intensity [P], interference with enjoyment of life [E], and interference with daily activity [G]). Latent class analysis was used to identify symptom clusters using standardized scale scores and their established cut points. RESULTS: We identified five classes using the Bayesian Information Criterion and the Akaike Information Criterion. Women in classes 1 and 2 had high hot flash interference levels relative to the others, and class 1 (10.5% of total) included severe hot flash interference, severe sleep symptoms, and moderately severe pain symptoms (hot flash, sleep, pain). In class 2 (14.1%), severe hot flash interference was paired with the severe sleep symptoms, and moderate to severe depressed and anxious mood symptoms and pain (hot flash, sleep, mood, pain). In class 3 (39.6%), women reported moderately severe sleep symptoms with moderate hot flash interference, and low severity mood and pain symptoms (hot flash, sleep). Those in class 4 (7.0%) reported moderate hot flash interference with severe levels of anxiety and depressed mood symptoms, but low levels of other symptoms (hot flash, mood). Women in class 5 (28.7%) reported the lowest levels of all the five symptoms (low severity symptoms). CONCLUSIONS: Women meeting hot flash frequency criteria for inclusion in clinical trials exhibited multiple co-occurring symptoms that clustered into identifiable groups according to symptom interference and severity. Variability of symptom profiles between the classes was evident, indicating that the classes were composed of differing symptom types and not simply differing severity levels. These symptom clusters may be useful phenotypes for differentiating treatment effects or evaluating associations with biomarkers or genes.
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