Differences in symptom clusters before and twelve months after breast cancer surgery

Priorities for alleviating menopausal symptoms after cancer

OBJECTIVE

To determine treatment priorities in women cancer patients attending a dedicated Menopausal Symptoms After Cancer service.

METHODS

Cancer type and stage were abstracted from medical records. Women ranked up to three symptoms as treatment priorities from the list "hot flushes/night sweats," "mood changes," "vaginal dryness or soreness," "sleep disturbances," "feeling tired or worn out (fatigue)," "sexual problems and/or pain with intercourse," "joint pain," and "something else" with free-text response. For each prioritized symptom, patients completed standardized patient-reported outcome measures to determine symptom severity and impact.

RESULTS

Of 189 patients, most had breast cancer (48.7%, n = 92), followed by hematological (25.8%, n = 49), gynecological (18.0%, n = 34), or colorectal (2.6%, n = 5). The highest (first-ranked) treatment priority was vasomotor symptoms (33.9%, n = 64), followed by fatigue (18.0%, n = 34), vaginal dryness/soreness (9.5%, n = 18), and sexual problems/pain with intercourse (9.5%, n = 18). Symptoms most often selected in the top three ("prioritized") were fatigue (57.7%, n = 109), vasomotor symptoms (57.1%, n = 108), and sleep disturbance (49.2%, n = 93). In patients who prioritized vasomotor symptoms, medians on the "problem," "distress," and "interference" dimensions of the Hot Flash Related Daily Interference Scale were, respectively, 6.0 (interquartile range [IQR], 5.0-8.0), 5.5 (IQR, 3.0-8.0), and 5.0 (IQR, 3.-7.0), indicating moderate severity. In patients who prioritized fatigue, the median Fatigue Scale score was 28 (IQR, 19-36), 37% worse than general population.

CONCLUSIONS

Vasomotor symptoms, fatigue, sexual problems, and vaginal dryness/soreness were the leading priorities for treatment. Understanding symptom severity and patient priorities will inform better care for this growing population.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

OBJECTIVE

A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes-related symptoms using a large nationwide electronic health record (EHR) database.

METHODS

We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes-related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes-related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data.

RESULTS

Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21-60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.

CONCLUSION

To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes-related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

The prevalence and risk of symptom and function clusters in colorectal cancer survivors

PURPOSE

Our purpose was to describe the prevalence and predictors of symptom and function clusters in a diverse cohort of colorectal cancer survivors.

METHODS

We used data from a cohort of 909 adult colorectal cancer survivors. Participants were surveyed at a median of 9 months after diagnosis to ascertain the co-occurrence of eight distinct symptom and functional domains. We used factor analysis to identify co-occurring domains and latent profile analysis (LPA) to identify subgroups of survivors with different symptom and function clusters. Multinomial logistic regression models were used to identify risk/protective factors.

RESULTS

Factor analysis demonstrated a single underlying factor structure that included all eight health domains with depression and anxiety highly correlated (r = 0.87). The LPA identified three symptom and function clusters, with 30% of survivors in the low health-related quality of life (HRQOL) profile having the highest symptom burden and lowest functioning. In multivariable models, survivors more likely to be in the low HRQOL profile included being non-White, female, those with a history of cardiac or mental health conditions, and chemotherapy recipients. Survivors less likely to be in the low HRQOL profile included those with older age, greater financial well-being, and more spirituality.

CONCLUSION

Nearly one-third of colorectal cancer survivors experienced a cluster of physical and psychosocial symptoms that co-occur with clinically relevant deficits in function.

IMPLICATIONS FOR CANCER SURVIVORS

Improving the identification of risk factors for having the highest symptom and lowest function profile can inform the development of clinical interventions to mitigate their adverse impact on cancer survivors' HRQOL.

A Computational Framework to Analyze the Associations Between Symptoms and Cancer Patient Attributes Post Chemotherapy Using EHR Data

Patients with cancer, such as breast and colorectal cancer, often experience different symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea, vomiting, lack of appetite), psychoneurological symptoms (depressive symptoms, anxiety), or other types. Previous research focused on understanding the symptoms using survey data. In this research, we propose to utilize the data within the Electronic Health Record (EHR). A computational framework is developed to use a natural language processing (NLP) pipeline to extract the clinician-documented symptoms from clinical notes. Then, a patient clustering method is based on the symptom severity levels to group the patient in clusters. The association rule mining is used to analyze the associations between symptoms and patient attributes (smoking history, number of comorbidities, diabetes status, age at diagnosis) in the patient clusters. The results show that the various symptom types and severity levels have different associations between breast and colorectal cancers and different timeframes post-chemotherapy. The results also show that patients with breast or colorectal cancers, who smoke and have severe fatigue, likely have severe gastrointestinal symptoms six months after the chemotherapy. Our framework can be generalized to analyze symptoms or symptom clusters of other chronic diseases where symptom management is critical.

Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data

This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.

Symptom Clusters Predictive of Quality of Life Among Jordanian Women with Breast Cancer

OBJECTIVES

This study was conducted to explore symptom clusters among women with breast cancer in Jordan.

DATA SOURCES

A cross-sectional survey of 516 women with breast cancer who were recruited from three hospitals.

CONCLUSION

This study demonstrated that women with breast cancer experienced several symptoms at the same time. These symptoms tend to cluster in five main groups, and patients experiencing the psychological, nausea and vomiting, and pain clusters are expected to have a lower mean score of quality of life.

IMPLICATIONS FOR NURSING PRACTICE

Nurses need to assess and manage symptoms as clusters to improve the quality of life of women with breast cancer. Symptoms clusters should guide symptoms management practice and be given a priority equal to the active treatment of cancer. Symptoms management and cancer treatment should be started simultaneously.

Symptom clusters experienced by breast cancer patients at various treatment stages: A systematic review

Breast cancer patients often experience symptoms that adversely affect their quality of life. It is understood that many of these symptoms tend to cluster together: while they might have different manifestations and occur during different phases of the disease trajectory, the symptoms often have a common aetiology that is a potential target for intervention. Understanding the symptom clusters associated with breast cancer might usefully inform the development of effective care plans for affected patients. The aim of this paper is to provide an updated systematic review of the known symptom clusters among breast cancer patients during and/or after cancer treatment. A search was conducted using five databases for studies reporting symptom clusters among breast cancer patients. The search yielded 32 studies for inclusion. The findings suggest that fatigue-sleep disturbance and psychological symptom cluster (including anxiety, depression, nervousness, irritability, sadness, worry) are the most commonly-reported symptom clusters among breast cancer patients. Further, the composition of symptom clusters tends to change across various stages of cancer treatment. While this review identified some commonalities, the different methodologies used to identify symptom clusters resulted in inconsistencies in symptom cluster identification. It would be useful if future studies could separately examine the symptom clusters that occur in breast cancer patients undergoing a particular treatment type, and use standardised instruments across studies to assess symptoms. The review concludes that further studies could usefully determine the biological pathways associated with various symptom clusters, which would inform the development of effective and efficient symptom management strategies.

Symptom Clusters in Breast Cancer Survivors: A Latent Class Profile Analysis

OBJECTIVES

To identify symptom clusters in breast cancer survivors and to determine sociodemographic and clinical characteristics influencing symptom cluster membership.

SAMPLE AND SETTING

The authors performed a cross-sectional secondary analysis of data obtained from a community-based cancer registry-linked survey with 1,500 breast cancer survivors 6-13 months following a breast cancer diagnosis.

METHODS AND VARIABLES

Symptom clusters were identified using latent class profile analysis of four patient-reported symptoms (pain, fatigue, sleep disturbance, and depression) with custom PROMIS® short forms.

RESULTS

Four distinct classes were identified.

IMPLICATIONS FOR NURSING

Common symptom clusters may lead to better prevention and treatment strategies that target a group of symptoms. Results also suggest that certain factors place patients at high risk for symptom burden, which can guide tailored interventions.

Higher Risk of Depression After Total Mastectomy Versus Breast Reconstruction Among Adult Women With Breast Cancer: A Systematic Review and Metaregression

This systematic review with a meta-regression was conducted to determine the risk of depression after mastectomy compared to breast reconstruction among women with breast cancer 1 year after surgery. A literature search was conducted according to PRISMA guidelines using 4 databases: Medline (Ovid), Embase, Cinahl, and the Cochrane Library for the period January 2000 to March 2019. Studies that measured the status of depression within 1 year and immediately after surgery were included. Outcomes related to depression were analyzed by using a pool of event rates and a risk ratio of 95% confidence interval (CI), P value, and a fitting model based on the results of a heterogeneity test of mastectomy and BR. The statistical analysis was conducted using Comprehensive Meta-analysis 3.0 software. Nine studies met the inclusion criteria. There were 865 cases of mastectomy only, with a 22.2% risk of depression (95% CI, 12.4-36.2). In 869 women who underwent BR, the risk of depression was 15.7% (95% CI, 8.8-26.2). The depression risk ratio for mastectomy compared to BR was 1.36 (95% CI, 1.11-1.65). Patients with delayed reconstruction exhibited lower levels of depression (risk ratio 0.96, 95% CI 0.57-1.01). The Beck Depression Inventory (BDI) scale showed high sensitivity, and the Hospital Anxiety Depression Scale (HADS) with a cutoff of > 7 could measure even low to moderate depressive symptoms. One in 4 women with breast cancer had symptoms of depression after mastectomy; both surgeries were associated with depression in women 1 year after surgery. Our results will permit the development of proactive treatment plans before and after surgery to mitigate risk and prevent depression through the use of sensitive depression scales like BDI.

The Correlation of Symptom Clusters and Functional Performance in Adult Acute Leukemia Patients Under Chemotherapy

BACKGROUND

Adult acute leukemia (AL) patients who receive chemotherapy usually experience multiple symptoms during the treatment course. The symptom clusters (SCs) as well as subsets of concurrent symptoms in AL patients have not yet been demonstrated.

OBJECTIVE

To investigate the SCs of adult AL patients who were receiving chemotherapy and to determine their correlations with functional performance.

METHODS

A total of 132 hospitalized adult AL patients were included in this study. A cross-sectional survey aimed to examine symptoms and functional performance was conducted. The patients' symptoms were assessed using the Chinese version of the Condensed Memorial Symptom Assessment Scale, and functional performance was evaluated through activities of daily living and quality of life.

RESULTS

We identified 4 SCs in adult AL patients: psychological SC, pain-fatigue-sleep SC, dry mouth-constipation SC, and nutrition-impaired SC. The psychological SC was the most common and most distressing SC. The different SCs were each differentially correlated with patient characteristics. The distress of the psychological SC, pain-fatigue-sleep SC, and nutrition-impaired SC was adversely correlated with functional performance.

CONCLUSIONS

Adult AL patients undergoing chemotherapy experience multiple symptoms that can be further categorized into 4 SCs. The distress from some SCs is negatively associated with patients' functional performance.

IMPLICATIONS FOR PRACTICE

Symptom burden remains a major problem for adult AL patients undergoing chemotherapy. Identifying SCs of AL patients should be the basis for accurate and cost-effective interventions. Personalized SC management may improve the functional performance and healthcare quality of adult AL patients.

Symptom Clusters and Quality of Life over 1 Year in Breast Cancer Patients Receiving Adjuvant Chemotherapy

Objective:

Evidence is scant regarding symptom clusters and quality of life (QOL) over 1 year in women who receive adjuvant breast cancer chemotherapy (CTX). Our purpose was to identify the prevalence and severity of individual symptoms, symptom clusters, and QOL in women receiving adjuvant breast cancer CTX from baseline over 1 year.

Methods:

Symptoms were identified in a sample (n = 219) at three times: baseline (prior to the first adjuvant CTX treatment), 1 month after the last CTX (approximately 6 months after baseline), and 1 year after baseline. The Hospital Anxiety and Depression Scale and Symptom Experience Scale measured symptoms. The Medical Outcomes Study, Short-Form Survey, measured QOL. Exploratory factor analysis identified symptom clusters at each time and core symptoms in clusters over time.

Results:

The prevalence and severity of 10 symptoms decreased over time (P < 0.05). Fatigue, sleep disturbance, and pain were most prevalent; all were of mild severity. Two symptom clusters were identified at baseline and one met internal consistency reliability criteria at the later times. Core symptoms were identified. Both the physical and mental component scores of QOL improved over time (P < 0.01), but physical was below the general population norms 1 year after baseline.

Conclusions:

The symptom experience was dynamic, and symptom clusters changed over 1 year. Despite mild severity, core symptoms and clusters persisted over 1 year, and physical health was below the general population norms. Breast cancer survivors with persistent single and co-occurring symptoms need to be taught to manage the patterns of symptoms over time because they may not resolve by 1 year.

Stability and Impact of Symptom Clusters in Patients With End-Stage Renal Disease Undergoing Dialysis

CONTEXT

Patients with end-stage renal disease undergoing dialysis experience multiple concurrent symptoms. These symptoms cluster together and have negative impacts on patient outcomes. However, information on changes in symptom clusters over time is limited.

OBJECTIVES

This longitudinal study examined the stability of symptom clusters and their impacts on health-related quality of life and functional status over a period of one year.

METHODS

Eligibility criteria were patients diagnosed with end-stage renal disease; had received dialysis consecutively for at least three months; and had given written informed consent. Dialysis Symptom Index, Kidney Disease Quality of Life 36, and Karnofsky Performance Status Scale were used to evaluate the impacts of symptom clusters and outcomes. Exploratory factor analyses and multiple regression analyses were used to determine symptom clusters and their associations with patient outcomes.

RESULTS

Among the 354 recruited patients, 271 completed the 12-month assessment. Four symptom clusters were identified across the three assessments, namely, uremic, gastrointestinal, skin, and emotional. Within each cluster, the specific symptoms were varied. The uremic symptom cluster accounted for the largest amount of variability. Across the three assessments, a higher uremic cluster factor score was associated with poorer physical well-being, whereas a higher emotional cluster factor score was consistently associated with poorer mental well-being.

CONCLUSION

Symptoms in patients on dialysis clustered in relatively stable patterns. The four symptom clusters identified had consistent negative effects on various aspects of patients' well-being. Our findings suggest the need for ongoing symptom assessment and early recognition of symptoms that may contribute to adverse patient outcomes.

Effect of progressive resistance training on health-related quality of life in the first year after breast cancer surgery - results from a randomized controlled trial

Aims: To examine the effect of progressive resistance training (PRT) on health related quality of life and a predefined symptom cluster of pain-sleep-fatigue. Methods: This study was a planned secondary analysis of a randomized controlled trial examining the effect of PRT on prevention of arm lymphedema in a population of women between 18 and 75 years undergoing breast cancer surgery with axillary lymph node dissection. Participants were allocated by computer randomization to usual care control or a PRT intervention in a 1:1 ratio. The intervention, initiated in the third post-operative week, consisted of three times PRT per week, supervised in groups in the first 20 weeks, and self-administered in the following 30 weeks. Questionnaire assessments were made at baseline, 20 weeks and 12 months, with the European Organization for Research and Treatment in Cancer Core questionnaire (EORTC QLQ C30) and the Functional Assessment of Chronic Illness Therapy-(FACIT) fatigue questionnaire. The symptom cluster of pain-sleep-fatigue was measured with a constructed score adding EORTC C30 subscales of insomnia, pain, and fatigue. Data were treated as repeated measurements and analyzed with mixed models. Results: Among 158 recruited participants, we found a clinically relevant increased emotional functioning with nine points at both follow-ups (p = .02), and 16 and 11 points at 20 weeks and 12 months respectively (p = .04) in social functioning. Furthermore, in the subgroup of women with the symptom cluster pain-sleep-fatigue present at baseline, a significant effect was found for global health status (p = .01) and social functioning (p = .02). Conclusion: To our knowledge, this is the first study to report clinically relevant effects of PRT on social and emotional functioning in the first postoperative year after breast cancer surgery. Furthermore, a subgroup of women with the pain-sleep-fatigue symptom cluster had particular benefit from PRT on global health status and social functioning.

Network Analysis of the Multidimensional Symptom Experience of Oncology

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.