Vasomotor symptoms in early breast cancer-a "real world" exploration of the patient experience

Real-world clinical evaluation of natural and induced vasomotor symptoms in the USA and Europe

OBJECTIVE

This study aimed to examine physicians' and patients' perceptions regarding symptom burden and impact in women experiencing natural vasomotor symptoms (nVMS) or vasomotor symptoms induced by endocrine therapy for breast cancer (iVMS).

METHODS

The cross-sectional survey based on real-world clinical consultations was conducted in the USA and five European countries. Obstetrician-gynecologists, primary-care physicians and oncologists provided demographic and symptom data for patients experiencing VMS; patients optionally self-reported their experiences via questionnaires, including their symptom profile and work/activity burden through the Menopause Quality of Life (MENQOL) and Work Productivity and Activity Impairment (WPAI) tools.

RESULTS

Physicians completed survey forms on 2451 consulting patients; patients completed 1029 questionnaires. nVMS and iVMS severity was significantly associated with the severity of mood symptoms and sleep disturbances (p < 0.0001). However, around half of the patients with mild nVMS/iVMS also experienced moderate-severe mood changes (55.4%/43.7%) or sleep disturbances (42.4%/40.4%). Presence of mood/sleep disturbances alongside nVMS increased MENQOL vasomotor scores (p = 0.004/p < 0.001). Presence of sleep disturbances increased WPAI activity impairment (p < 0.001) but mood changes did not. Similar findings were reported for iVMS patients.

CONCLUSION

Significant burden from the triad of natural or induced menopausal symptoms, sleep disturbances and mood changes affected women's daily activities, work and quality of life more than vasomotor symptoms alone.

Vasomotor symptoms and management of women undergoing treatment for breast cancer: literature review with focus on the therapeutic potential of cytoplasmic pollen extract

OBJECTIVE

Effective management of vasomotor symptoms (VMS) in patients undergoing treatment for breast cancer (BC) represents a critical but frequent unmet need. This review summarizes the epidemiology, pathophysiology, and clinical features of VMS in patients with BC and provides a synopsis of the complementary and alternative medicine (CAM) approaches in relieving VMS with a focus on purified cytoplasm of pollen (PCP).

METHODS

The literature on VMS epidemiology, pathophysiology, clinical burden, and CAM treatment in healthy women and patients with BC was reviewed.

RESULTS

VMS are common in patients with BC undergoing hormonal treatment and negatively impact quality of life, leading to treatment discontinuation in up to 25% of patients with detrimental impact on risk of BC recurrence and overall survival. CAM approaches to treat VMS in patients with BC include vitamin E, phytoestrogens, and black cohosh, even if there is a lack of solid evidence to guide clinicians in the choice of treatment. PCP, obtained according to standards of good manufacturing practice, has a definite pharmacological mechanism of action, is devoid of estrogen activity, and has shown clinical efficacy on menopause-associated symptoms with a favorable safety profile and high compliance. As such, it appears to represent a valid management option to improve quality of life in patients with pre- and postmenopausal BC.

CONCLUSIONS

Physicians should actively investigate the presence and impact of VMS in patients receiving therapy for BC. Additional and appropriately sized randomized clinical trials are needed to provide clear evidence on how to best meet the needs of patients with BC suffering from menopause-associated symptoms.

Estrogen deprivation effects of endocrine therapy in breast cancer patients: Incidence, management and outcome

Endocrine therapy is one of the standard adjuvant treatments to reduce the risk of recurrence and mortality in patients with hormone receptor positive early breast cancer. Despite its proven efficacy, ET side effects, which persist over time even if low grade, may deteriorate quality of life. During follow-up visits, emphasis is generally placed on the risk of disease recurrence, while the topic of ET side effects is commonly neglected and discussed only briefly. This could lead to poor adherence to therapy and early treatment discontinuation, resulting in worse survival outcomes. The aim of this review is to provide an overview of the available evidence on the incidence and reporting of ET-related side effects (including vasomotor symptoms, musculoskeletal disorders and genitourinary syndrome of menopause, as well as fatigue, psychological and ocular disorders, dysmetabolic effects and loss of bone density) and of the pharmacological and non-pharmacological strategies available to mitigate symptom burden.

Using machine learning to predict individual patient toxicities from cancer treatments

PURPOSE

Machine learning (ML) is a powerful tool for interrogating datasets and learning relationships between multiple variables. We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS).

METHODS

A gradient boosted decision model utilizing cross-sectional survey data from 360 EBC patients was created. Seventeen patient- and treatment-specific variables were considered in the model. The outcome variable was based on the Hot Flush Night Sweats (HFNS) Problem Rating Score, and individual scores were dichotomized around the median to indicate individuals with high and low problem scores. Model accuracy was assessed using the area under the receiver operating curve, and conditional partial dependence plots were constructed to illustrate relationships between variables and the outcome of interest.

RESULTS

The model area under the ROC curve was 0.731 (SD 0.074). The most important variables in the model were as follows: the number of hot flashes per week, age, the prescription, or use of drug interventions to manage VMS, whether patients were asked about VMS in routine follow-up visits, and the presence or absence of changes to breast cancer treatments due to VMS. A threshold of 17 hot flashes per week was identified as being more predictive of severe VMS. Patients between the ages of 49 and 63 were more likely to report severe symptoms.

CONCLUSION

Machine learning is a unique tool for predicting severe VMS. The use of ML to assess other treatment-related toxicities and their management requires further study.