The symptom cluster experience profile framework.
Ontology highlight
ABSTRACT:
SUBMITTER: Finnegan L
PROVIDER: S-EPMC5547820 | biostudies-other | 2010 Nov
REPOSITORIES: biostudies-other
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Project description:An overview of proceedings, findings, and recommendations from the workshop on "Advancing Symptom Science Through Symptom Cluster Research" sponsored by the National Institute of Nursing Research (NINR) and the Office of Rare Diseases Research, National Center for Advancing Translational Sciences, is presented. This workshop engaged an expert panel in an evidenced-based discussion regarding the state of the science of symptom clusters in chronic conditions including cancer and other rare diseases. An interdisciplinary working group from the extramural research community representing nursing, medicine, oncology, psychology, and bioinformatics was convened at the National Institutes of Health. Based on expertise, members were divided into teams to address key areas: defining characteristics of symptom clusters, priority symptom clusters and underlying mechanisms, measurement issues, targeted interventions, and new analytic strategies. For each area, the evidence was synthesized, limitations and gaps identified, and recommendations for future research delineated. The majority of findings in each area were from studies of oncology patients. However, increasing evidence suggests that symptom clusters occur in patients with other chronic conditions (eg, pulmonary, cardiac, and end-stage renal disease). Nonetheless, symptom cluster research is extremely limited and scientists are just beginning to understand how to investigate symptom clusters by developing frameworks and new methods and approaches. With a focus on personalized care, an understanding of individual susceptibility to symptoms and whether a "driving" symptom exists that triggers other symptoms in the cluster is needed. Also, research aimed at identifying the mechanisms that underlie symptom clusters is essential to developing targeted interventions.
| S-EPMC5939621 | biostudies-literature
Project description:AimThe aim of the study was to explore the menopausal symptom experience of White midlife women.BackgroundRecent cross-cultural investigations have indicated important ethnic differences in menopausal symptoms and have challenged the universality of these symptoms. Currently available cultural knowledge on menopausal experience, however, is inadequate to guide appropriate and adequate care even for White women in the menopausal transition.MethodA cross-sectional qualitative online forum study was conducted in 2007 with 23 midlife women who self-identified as non-Hispanic Whites using convenience sampling. Seven topics related to menopausal symptom experience were used to guide the online forum for 6 months. The data were analysed using thematic analysis.ResultsThe experiences of menopause caused women to redefine themselves within their busy daily life schedules. They were optimistic about their symptoms, and tried to laugh at the experience to boost their inner strength and motivate themselves to persevere. Many thought that both generational and life-style differences were much more important than ethnic differences in menopausal symptom experiences. In seeking assistance with the symptoms of menopause, women were not satisfied with the guidance of their physicians.ConclusionNurses need to listen carefully to what women themselves say about their own experiences with menopausal symptoms and avoid imposing predetermined symptom management strategies. Further studies of women's perceptions of life-style influences are needed to guide symptom management. In addition, studies of generational changes in menopausal symptom experience are needed.
| S-EPMC2430027 | biostudies-literature
Project description:BACKGROUND:Symptom clusters are conventionally distilled into a single score using composite scoring, which is based on the mathematical assumption that all symptoms are equivalently related to outcomes of interest; this may lead to a loss of important variation in the data. OBJECTIVES:This article compares two ways of calculating a single score for a symptom cluster: a conventional, hypothesis-driven composite score versus a data-driven, reduced rank regression score that weights the symptoms based on their individual relationships with key outcomes. METHODS:We conducted a secondary analysis of psychoneurological symptoms from a sample of 356 low-income mothers. Four of the psychoneurological symptoms (fatigue, cognitive dysfunction, sleep disturbance, and depressed mood) were measured with the Center for Epidemiological Studies Depression Scale; the fifth (pain) was measured using an item from the Medical Outcomes Study 12-item Short Form Health Survey (SF-12). Mothers' function was measured using the 12-item Short Form Health Survey. The composite score was calculated by summing standardized scores for each individual psychoneurological symptom. In contrast, reduced rank regression weighted the individual symptoms using their respective associations with mothers' function; the weighted individual symptom scores were summed into the reduced rank regression symptom score. RESULTS:The composite score and reduced rank regression score were highly correlated at .93. The cluster of psychoneurological symptoms accounted for 53.7% of the variation in the mothers' function. Depressed mood and pain accounted for almost all the explained variation in mothers' function at 37.2% and 15.0%, respectively. DISCUSSION:The composite score approach was simpler to calculate, and the high correlation with the reduced rank regression score indicates that the composite score reflected most of the variation explained by the reduced rank regression approach in this data set. However, the reduced rank regression analysis provided additional information by identifying pain and depressed mood as having the strongest association with a mother's function, which has implications for understanding which symptoms to target in future interventions. Future studies should also explore composite versus reduced rank regression approaches given that reduced rank regression may yield different insights in other data sets.
| S-EPMC7050366 | biostudies-literature
Project description:BackgroundMillions of Americans experience traumatic orthopaedic injuries (TOIs) annually. Post-injury symptoms of acute stress disorder (ASD), anxiety, depression, pain, and sleep disturbance are common. Symptoms often present in clusters. Symptom cluster profiles phenotypically characterize TOI survivors' experiences with clustered symptoms. Expression of brain-derived neurotrophic factor (BDNF) may contribute to the biological underpinnings of symptom cluster profile membership.MethodsWe recruited hospitalized TOI survivors within 72 hours of injury. We measured symptoms of ASD with the Acute Stress Disorder Scale and symptoms of anxiety, depression, pain, and sleep disturbance with Patient-Reported Outcomes Measurement Information System (PROMIS) short forms. We measured serum BDNF concentrations with enzyme-linked immunosorbent assay (ELISA) and identified rs6265 genotypes with TaqMan real-time PCR. We performed latent profile analysis to identify the symptom cluster profiles. We identified the variables associated with symptom cluster profile membership with unadjusted and adjusted multinomial logistic regression.ResultsWe identified 4 symptom cluster profiles characterized by symptom severity that we labelled Physical Symptoms Only, and Mild, Moderate, and Severe Psychological Distress. Age, self-identified Black race, resilience, and serum BDNF concentrations were associated with lower odds, and female sex with higher odds, of being in the Psychological Distress clusters. Clinical characteristics and rs6265 genotypes were not associated with symptom cluster profile membership.ConclusionTOI survivors experience distinct symptom cluster profiles. Sociodemographic characteristics and serum BDNF concentrations, not clinical characteristics, were associated with symptom cluster profile membership. These findings support comprehensive symptom screening and treatment for all TOI survivors and further evaluating BDNF as a biomarker of post-injury symptom burden.
| S-EPMC9232974 | biostudies-literature
Project description:Competent data mining methods are vital to discover knowledge from databases which are built as a result of enormous growth of data. Various techniques of data mining are applied to obtain knowledge from these databases. Data clustering is one such descriptive data mining technique which guides in partitioning data objects into disjoint segments. K-means algorithm is a versatile algorithm among the various approaches used in data clustering. The algorithm and its diverse adaptation methods suffer certain problems in their performance. To overcome these issues a superlative algorithm has been proposed in this paper to perform data clustering. The specific feature of the proposed algorithm is discretizing the dataset, thereby improving the accuracy of clustering, and also adopting the binary search initialization method to generate cluster centroids. The generated centroids are fed as input to K-means approach which iteratively segments the data objects into respective clusters. The clustered results are measured for accuracy and validity. Experiments conducted by testing the approach on datasets from the UC Irvine Machine Learning Repository evidently show that the accuracy and validity measure is higher than the other two approaches, namely, simple K-means and Binary Search method. Thus, the proposed approach proves that discretization process will improve the efficacy of descriptive data mining tasks.
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Project description:Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients' symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.
| S-EPMC6381090 | biostudies-literature
Project description:Traumatic injuries affect millions of Americans annually, resulting in $671 billion in healthcare costs and lost productivity. Postinjury symptoms, like pain, sleep disturbance, anxiety, depression, and stressor-related disorders are highly prevalent following traumatic orthopedic injuries (TOI) and may contribute to negative long-term outcomes. Symptoms rarely present in isolation, but in clusters of two or more symptoms that co-occur to affect health in aggregate. Identifying symptom cluster profiles following TOI may identify those at highest risk for negative outcomes. Dysregulation of brain-derived neurotrophic factor (BDNF) is a potential biological mechanism responsible for symptom cluster profile membership after TOI and may be targeted in future precision-health applications. The purpose of this paper is to present the protocol of a cross-sectional study designed to identify symptom cluster profiles and measure the extent to which the BDNF val66met mutation and serum concentration of BDNF are associated with membership in symptom cluster profiles. We plan to recruit 150 TOI survivors within the first 72 h of injury. The study aims are to (1) describe TOI survivors' membership in symptom cluster profiles, indicated by pain, sleep disturbance, and symptoms of anxiety, depression, and stressor-related disorders, immediately following a TOI; (2) examine associations between demographic and clinical factors and symptom cluster profile membership among TOI survivors; (3) test the hypothesis that low serum concentrations of BDNF are associated with membership among symptom cluster profiles following TOI; and (4) test the hypothesis that the presence of the val66met mutation on one or both alleles of the BDNF gene is associated with membership among symptom cluster profiles following TOI.
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Project description:Our approach is based on a tri-partite method of integrating psychodynamic hypotheses, cognitive subliminal processes, and psychophysiological alpha power measures. We present ten social phobic subjects with three individually selected groups of words representing unconscious conflict, conscious symptom experience, and Osgood Semantic negative valence words used as a control word group. The unconscious conflict and conscious symptom words, presented subliminally and supraliminally, act as primes preceding the conscious symptom and control words presented as supraliminal targets. With alpha power as a marker of inhibitory brain activity, we show that unconscious conflict primes, only when presented subliminally, have a unique inhibitory effect on conscious symptom targets. This effect is absent when the unconscious conflict primes are presented supraliminally, or when the target is the control words. Unconscious conflict prime effects were found to correlate with a measure of repressiveness in a similar previous study (Shevrin et al., 1992, 1996). Conscious symptom primes have no inhibitory effect when presented subliminally. Inhibitory effects with conscious symptom primes are present, but only when the primes are supraliminal, and they did not correlate with repressiveness in a previous study (Shevrin et al., 1992, 1996). We conclude that while the inhibition following supraliminal conscious symptom primes is due to conscious threat bias, the inhibition following subliminal unconscious conflict primes provides a neurological blueprint for dynamic repression: it is only activated subliminally by an individual's unconscious conflict and has an inhibitory effect specific only to the conscious symptom. These novel findings constitute neuroscientific evidence for the psychoanalytic concepts of unconscious conflict and repression, while extending neuroscience theory and methods into the realm of personal, psychological meaning.
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Project description:Symptoms that are multidimensional and concurrent should be assessed from different dimensions and managed together. Few studies have evaluated concurrent and multidimensional symptoms in patients with stroke. Most studies of stroke focused on dysfunctions and complications. We hypothesize that patients with stroke have a heavy symptom burden within 1 year. This study aimed to describe multidimensional and concurrent symptoms within 1 year after stroke. This study recruited 230 patients with stroke from the Rehabilitation Department of Xuhui District Center Hospital of Shanghai and the Shanghai Sunshine Rehabilitation Center in China from March to September 2017. The patients' multidimensional symptom experience and symptom burden were analyzed using a self-made structured questionnaire and the influential factors for symptom burden were identified. The mean number of symptoms in patients with stroke was 11.7 ± 3.5. More than two thirds of the participants suffered from at least 10 co-occurring symptoms. Unilateral limb weakness had the highest prevalence and frequency. Participation restriction had the highest symptom dimensions of severity and distress. Lack of self-care ability (severity), memory deterioration (frequency), imbalance of body (distress), moodiness (distress), being unable to move limbs at will (distress), shoulder pain (distress), and slower response (frequency) were independent factors of the total symptom burden score. These findings can provide essential information for efficient symptom management of patients with stroke. This trial was registered with the ISRCTN registry (registration number: ISRCTN18421629).
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Project description:Symptom burden has been identified as a predictor of medication adherence, but little is known about which symptoms are most strongly implicated. This study examines self-reported medical adherence in relation to demographic, clinical, and symptom characteristics among 302 adults living with HIV. Only 12% reported missing medication during the 3-day assessment, but 75% gave at least one reason for missing it in the previous month. Poor adherence was associated with higher viral load and greater symptom burden. Trouble sleeping and difficulty concentrating were strongly associated with poor adherence. Given that "forgetting" was the most common reason for missing medication and nearly one third reported sleeping through dose time, future research should examine the influence of sleep disturbance on adherence. Effective management of common symptoms, such as sleep disturbance, fatigue, and gastrointestinal side-effects of medications may result in better adherence, as well as improved clinical outcomes and quality of life.
| S-EPMC3117946 | biostudies-literature