Objective

Patients with gynecologic cancers experience a very high symptom burden that has a negative impact on their quality of life. This systematic review aims to identify the common co-occurring symptoms, the prevalence of common symptoms, common instruments used to measure symptoms, associated risk factors, and the symptom burden in patients with gynecologic cancers.

Data sources

A search of four databases (ie, PubMed, Embase, Web of Science, and CINAHL) was done from January 1, 2012, through September 5, 2022. A qualitative synthesis of the extant literature was performed using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (PRISMA 2020).

Conclusion

A total of 118 studies met the prespecified inclusion criteria. Ninety-six symptoms were assessed across these studies. The top six symptoms and their grand mean prevalence rates were lack of energy (64.4%), fatigue (62.1%), abdominal pain (53.3%), depression (52.6%), concentration dysfunction (52.0%), and drowsiness (51.9%). Numerous methodologic challenges were evident across studies. Future research needs to develop a disease-specific symptom assessment measure, evaluate for risk factors associated with a higher symptom burden, and determine the impact of multiple symptoms on patient outcomes.

Implication for nursing practice

The results are relevant for oncology clinicians to assess patients with gynecologic cancers for the presence of common symptoms and risk factors for higher symptom burden in the patients and to offer effective management interventions.

Objective

To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data.

Data sources

Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines.

Conclusion

Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns.

Implications for nursing practice

With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

Background

Patients with gynecologic cancer undergoing chemotherapy experience multiple co-occurring symptoms. Understanding how symptom clusters change over time is essential to the development of interventions that target multiple co-occurring symptoms.

Objective

The aim of this study was to assess the relative stability of symptom clusters across a chemotherapy cycle in patients with gynecologic cancer.

Methods

This is a longitudinal, descriptive study. Eligible patients (n = 232) were English-speaking adults (≥18 years old) with gynecologic cancer. Data were collected in the week before patients' second or third cycle of chemotherapy (T1) and at 1 (T2) and 2 (T3) weeks after chemotherapy. Three dimensions of the symptom experience (occurrence, severity, and distress) were assessed using a modified version of the Memorial Symptom Assessment Scale. Symptom clusters for each dimension and time point were identified through exploratory factor analysis.

Results

A 5-factor solution was selected for each exploratory factor analysis. Hormonal, respiratory, and weight change clusters were identified across all dimensions and time points. A psychological symptom cluster was identified at T1 for occurrence and severity and at T2 and T3 for all 3 dimensions. A gastrointestinal symptom cluster was identified at T1 for occurrence and at T2 and T3 for all 3 dimensions. The hormonal, respiratory, psychological, and weight change symptom clusters exhibited common symptoms across dimensions and time points.

Conclusions

Hormonal, respiratory, weight change, and psychological symptom clusters are relatively stable across a cycle of chemotherapy in patients with gynecologic cancer.

Implications for practice

Clinicians need to assess patients for multiple co-occurring symptoms and initiate multimodal interventions.

Purpose

No information is available on cancer patients' knowledge of and experiences with COVID-19. We undertook an evaluation of differences in COVID-19 symptom occurrence rates, COVID-19 testing rates, clinical care activities, knowledge of COVID-19, and use of mitigation procedures between patients who were and were not receiving active cancer treatment.

Methods

Patients enrolled were > 18 years of age; had a diagnosis of cancer; and were able to complete the emailed study survey online.

Results

Of the 174 patients who participated, 27.6% (n = 48) were receiving active treatment, 13.6% were unemployed because of COVID-19, 12.2% had been tested for COVID-19, and 0.6% had been hospitalized for COVID-19. Patients who were not on active treatment reported a higher mean number of COVID-19 symptoms (3.1 (± 4.2) versus 1.9 (± 2.6)), and patients who reported a higher number of COVID-19 symptoms were more likely to be tested. Over 55% of the patients were confident that their primary care provider could diagnose COVID-19, and the majority of the patients had high levels of adherence with the use of precautionary measures (e.g., social distancing, use of face coverings).

Conclusion

The high level of COVID-19 symptoms and the significant overlap of COVID-19 and cancer-related symptoms pose challenges for clinicians who are assessing and triaging oncology patients for COVID-19 testing. For patients on active treatment, clinicians face challenges with how to assess and manage symptoms that, prior to COVID-19, would be ascribed to acute toxicities associated with cancer treatments or persistent symptoms in cancer survivors.

Context

No information is available on oncology patients' level of stress and symptom burden during the coronavirus disease 2019 (COVID-19) pandemic.

Objectives

To evaluate for differences in demographic and clinical characteristics, levels of social isolation and loneliness, and the occurrence and severity of common symptoms between oncology patients with low vs. high levels of COVID-19 and cancer-related stress. In addition, to determine which of these characteristics were associated with membership in the high-stressed group.

Methods

Patients were 18 years and older; had a diagnosis of cancer; and were able to complete an online survey.

Results

Of the 187 patients in this study, 31.6% were categorized in the stressed group (Impact of Event Scale-Revised [score of ≥24]). Stressed group's Impact of Event Scale-Revised score exceeds previous benchmarks in oncology patients and equates with probable post-traumatic stress disorder. In this stressed group, patients reported occurrence rates for depression (71.2%), anxiety (78.0%), sleep disturbance (78.0%), evening fatigue (55.9%), cognitive impairment (91.5%), and pain (75.9%). Symptom severity scores equate with clinically meaningful levels for each symptom.

Conclusion

We identified alarmingly high rates of stress and an extraordinarily high symptom burden among patients with cancer, exceeding those previously benchmarked in this population and on par with noncancer patients with post-traumatic stress disorder. Given that the COVID-19 pandemic will likely impact cancer care for an indefinite period, clinicians must exhibit increased vigilance in their assessments of patients' level of stress and symptom burden. Moreover, an increase in referrals to appropriate supportive care resources must be prioritized for high-risk patients.

Background

Loneliness and social isolation are significant public health problems that are being exacerbated during the coronavirus disease 2019 pandemic. Little is known about the associations between loneliness and symptom burden in oncology patients before and during the pandemic. Study purposes include determining the prevalence of loneliness in a sample of oncology patients; evaluating for differences in demographic, clinical, and symptom characteristics between lonely and nonlonely patients; and determining which demographic, clinical, and symptom characteristics were associated with membership in the lonely group.

Methods

A convenience sample (n = 606) completed online surveys that evaluated the severity of loneliness, social isolation, and common symptoms (ie, anxiety, depression, fatigue, sleep disturbance, cognitive dysfunction, and pain) in oncology patients. Parametric and nonparametric tests were used to evaluate for differences in scores between the lonely and nonlonely groups. Logistic regression analysis was used to determine risk factors for membership in the loneliness group.

Results

Of the 606 patients, 53.0% were categorized in the lonely group. The lonely group reported higher levels of social isolation, as well as higher symptom severity scores for all of the symptoms evaluated. In the multivariate model, being unmarried, having higher levels of social isolation, as well as higher levels of anxiety and depressive symptoms were associated with membership in the lonely group.

Conclusions

Study findings suggest that a significant number of oncology patients are experiencing loneliness, most likely as a result of mandate social distancing and isolation procedures. The symptom burden of these patients is extremely high and warrants clinical evaluation and interventions.