Symptom clusters and quality of life

The Prevalence and Patterns of Menopausal Symptoms in Women Living with HIV

Increasing numbers of women with HIV are experiencing menopause. We use data from a large, representative sample of women with HIV to describe the prevalence and clustering of menopausal symptoms amongst pre-, peri- and post-menopausal women using hierarchical agglomerative cluster analysis. Of the 709 women included, 21.6%, 44.9% and 33.6% were pre-, peri- and post-menopausal, respectively. Joint pain (66.4%) was the most commonly reported symptom, followed by hot flashes (63.0%), exhaustion (61.6%) and sleep problems (61.4%). All symptoms were reported more commonly by peri- and post-menopausal women compared to pre-menopausal women. Psychological symptoms and sleep problems clustered together at all menopausal stages. Somatic and urogenital symptom clusters emerged more distinctly at peri- and post-menopause. We recommend regular and proactive assessment of menopausal symptoms in midlife women with HIV, with an awareness of how particular patterns of symptoms may evolve over the menopausal transition.

Adolescents and Young Adults with Cancer Using a Symptom Heuristics App: Provider Perceptions and Actions

Purpose: This study examined health care providers' perceptions of the usefulness and ease of use of a symptom heuristics app delivered via a tablet computer as a resource for understanding symptom experiences of adolescents and young adults (AYAs) with cancer. AYAs' app-generated symptom reports were compared with providers' documentation of AYAs' symptoms. Methods: This multisite study included responses from 86 AYAs 15-29 years of age who completed the Computerized Symptom Capture Tool (C-SCAT) before two scheduled visits for chemotherapy. After each visit, their providers completed a survey addressing their perspective of: (1) the usefulness of data provided by the C-SCAT to understand the AYAs' symptom experience, and (2) the nature of the discussion of symptoms with the AYA. An electronic health record review compared symptoms that AYAs identified by using the C-SCAT with providers' documentation of symptoms. Results: One hundred forty-four complete surveys were returned after 162 visits. Fifty percent (n = 72) of responses reported that the C-SCAT helped identify the patient's symptoms, and 53% (n = 76) reported that it helped identify the patient's priority symptoms. Providers also reported higher patient engagement and more focused discussions regarding symptoms. They reported that use of the C-SCAT facilitated the development of symptom management plans. Priority symptoms were documented more frequently than nonpriority symptoms (54% vs. 32.7%; p < 0.01) as was a plan for managing priority symptoms (33.7% vs. 17.9%; p < 0.01). Conclusion: Use of the C-SCAT enhanced providers' understanding of AYAs' symptom experiences. Further research is needed to demonstrate the effectiveness of the C-SCAT as a resource to improve symptom management among AYAs with cancer.

It is not just menopause: symptom clustering in the Study of Women's Health Across the Nation

Background

Patterns of symptom clustering in midlife women may suggest common underlying mechanisms or may identify women at risk of adverse health outcomes or, conversely, likely to experience healthy aging. This paper assesses symptom clustering in the Study of Women’s Health Across the Nation (SWAN) longitudinally by stage of reproductive aging and estimates the probability of women experiencing specific symptom clusters. We also evaluate factors that influence the likelihood of specific symptom clusters and assess whether symptom clustering is associated with women’s self-reported health status.

Methods

This analysis includes 3289 participants in the multiethnic SWAN cohort who provided information on 58 symptoms reflecting a broad range of physical, psychological and menopausal symptoms at baseline and 7 follow-up visits over 16 years. We conducted latent transition analyses to assess symptom clustering and to model symptomatology across the menopausal transition (pre, early peri-, late peri- and post-menopausal). Joint multinomial logistic regression models were used to identify demographic characteristics associated with premenopausal latent class membership. A partial proportional odds regression model was used to assess the association between latent class membership and self-reported health status.

Results

We identified six latent classes that ranged from highly symptomatic (LC1) across most measured symptoms, to moderately symptomatic across most measured symptoms (LC2), to moderately symptomatic for a subset of symptoms (vasomotor symptoms, pain, fatigue, sleep disturbances and physical health symptoms) (LC3 and LC5) with one class (LC3) including interference in life activities because of physical health symptoms, to numerous milder symptoms, dominated by fatigue and psychological symptoms (LC4), to relatively asymptomatic (LC6). In pre-menopause, 10% of women were classified in LC1, 16% in LC2, 14% in LC3 and LC4, 26% in LC5, and 20% in LC6. Intensity of vasomotor and urogenital symptoms as well as sexual desire) differed minimally by latent class. Classification into the two most symptomatic classes was strongly associated with financial strain, White race/ethnicity, obesity and smoking status. Over time, women were most likely to remain within the same latent class as they transitioned through menopause stages (range 39–76%), although some women worsened or improved. The probability of moving between classes did not differ substantially by menopausal stage. Women in the highly symptomatic classes more frequently rated their health as fair to poor compared to women in the least symptomatic class.

Conclusion

Clear patterns of symptom clustering were present early in midlife, tended to be stable over time, and were strongly associated with self-perceived health. Notably, vasomotor symptoms tended to cluster with sleep disturbances and fatigue, were present in each of the moderate to highly symptomatic classes, but were not a defining characteristic of the symptom clusters. Clustering of midlife women by symptoms may suggest common underlying mechanisms amenable to interventions. Given that one-quarter of midlife women were highly or moderately symptomatic across all domains in the pre-menopause, addressing symptom burden in early midlife is likely critical to ameliorating risk in the most vulnerable populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s40695-017-0021-y) contains supplementary material, which is available to authorized users.

Symptom Clusters in Ovarian Cancer Patients With Chemotherapy After Surgery: A Longitudinal Survey

BACKGROUND

Ovarian cancer is 1 of the most common malignancies in the female reproductive system. Identification of symptom clusters in ovarian cancer patients may improve management of symptoms.

OBJECTIVE

The objective of this article is to explore the changes in symptom clusters in ovarian cancer patients with adjuvant chemotherapy after surgery at different time points.

METHODS

Basic details of the patients were documented and a longitudinal investigation was carried out. We used the Memorial Symptom Assessment Scale to examine 115 Chinese ovarian cancer patients' symptom experience at 4 time points: days before chemotherapy (T1), chemotherapy cycle 1 (T2), chemotherapy cycle 3 (T3), and chemotherapy cycle 6 (T4). The exploratory factor analysis was performed to determine the numbers and components of symptom clusters.

RESULTS

Symptom clusters varied at different time points, which were classified as pain-related symptom cluster, psychological symptom cluster, menopausal symptom cluster, gastrointestinal symptom cluster, body image symptom cluster, and peripheral neurologic symptom cluster. The gastrointestinal symptom cluster and body image symptom cluster appeared at T1 and remained consistent at T3 and T4, whereas the peripheral neurologic symptom cluster was noted at T3 and T4.

CONCLUSIONS

Clinicians should prioritize symptom management interventions with ovarian cancer patients to focus on the most severe symptom cluster: psychological symptom cluster at T1, gastrointestinal symptom cluster at T2, and body image symptom cluster at T3 and T4.

IMPLICATIONS FOR PRACTICE

The ability to predict symptom clusters in ovarian cancer patients receiving chemotherapy may help to make optimized clinical decision in advance to alleviate patients' symptoms and improve their life quality.

Symptoms and Symptom Clusters Identified by Adolescents and Young Adults With Cancer Using a Symptom Heuristics App

Adolescents and young adults (AYAs) with cancer experience multiple distressing symptoms during treatment. Because the typical approach to symptom assessment does not easily reflect the symptom experience of individuals, alternative approaches to enhancing communication between the patient and provider are needed. We developed an iPad-based application that uses a heuristic approach to explore AYAs' cancer symptom experiences. In this mixed-methods descriptive study, 72 AYAs (13-29 years old) with cancer receiving myelosuppressive chemotherapy used the Computerized Symptom Capture Tool (C-SCAT) to create images of the symptoms and symptom clusters they experienced from a list of 30 symptoms. They answered open-ended questions within the C-SCAT about the causes of their symptoms and symptom clusters. The images generated through the C-SCAT and accompanying free-text data were analyzed using descriptive, content, and visual analyses. Most participants (n = 70) reported multiple symptoms (M = 8.14). The most frequently reported symptoms were nausea (65.3%), feeling drowsy (55.6%), lack of appetite (55.6%), and lack of energy (55.6%). Forty-six grouped their symptoms into one or more clusters. The most common symptom cluster was nausea/eating problems/appetite problems. Nausea was most frequently named as the priority symptom in a cluster and as a cause of other symptoms. Although common threads were present in the symptoms experienced by AYAs, the graphic images revealed unique perspectives and a range of complexity of symptom relationships, clusters, and causes. Results highlight the need for a tailored approach to symptom management based on how the AYA with cancer perceives his or her symptom experience.