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.

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

BACKGROUND: Despite the frequency of vasomotor symptoms (VMS) in patients with early breast cancer (EBC), their optimal management remains unknown. A patient survey was performed to determine perspectives on this important clinical challenge. METHODS: Patients with EBC experiencing VMS participated in an anonymous survey. Patients reported on the frequency and severity of VMS using the validated Hot Flush Rating Scale (HFRS) and ranked their most bothersome symptoms. Respondents were also asked to determine endpoints that defined effective treatment of VMS and report on the effectiveness of previously tried interventions. RESULTS: Responses were received from 373 patients, median age 56 years (range 23-83), who experienced an average of 5.0 hot flashes per day (SD 6.57). Patients reported the most bothersome symptoms to be feeling hot/sweating (155/316, 49%) and sleeping difficulties (86/316, 27%). Fifty-five percent (201/365) of patients would consider a treatment to be effective if it reduced night-time awakenings. While 68% of respondents were interested in trying interventions from their healthcare team to manage VMS, only 18% actually did so. Of the 137 patients who had tried an intervention for VMS, pharmacological treatments, exercise, and relaxation strategies were more likely to be effective, while therapies such as melatonin and black cohosh were deemed less effective. CONCLUSION: VMS are a common and bothersome problem for EBC patients, with a minority receiving interventions to manage these symptoms. Further research is needed to identify patient-centered strategies for managing these distressing symptoms.

Developing patient-centred strategies to optimize the management of vasomotor symptoms in breast cancer patients: a survey of health care providers.

PURPOSE: Vasomotor symptoms (VMS) such as hot flashes and night sweats are common in breast cancer patients and can affect both quality of life and treatment adherence. However, there is limited practical data to guide clinicians in the optimal selection of therapeutic strategies. A survey of health care providers was performed to better understand perspectives and prescribing practices for managing this problem. METHODS: Canadian health care providers who treat patients with early stage breast cancer (EBC) participated in an anonymous electronic survey. Participants provided their perspectives on the prevalence and severity of VMS among patients with EBC, outlined their management strategies, and provided feedback on the perceived efficacy of interventions for VMS. RESULTS: Responses were received from 65 providers including breast oncologists (36/65, 55%) and nurses with oncology expertise (29/65, 45%). Seventy-seven percent of participants reported regularly asking patients about VMS, and most indicated that bothersome VMS occurred in the majority of patients. Health care providers cited hot flash severity and sleep disruption as the most important issues for patients. The most common first- and second-line interventions recommended were lifestyle modifications (n = 32/65, 49.2%) and pharmacologic strategies (n = 27/65, 41.5%), respectively. Most respondents felt that interventions, including pharmacologic, over-the-counter, and complementary therapies, were only "somewhat effective". Overall, half of respondents (n = 35/65, 54%) reported being "confident" in managing VMS. CONCLUSION: Given the variability of treatment recommendations, and health care provider uncertainty around the benefits of therapies for VMS, more 'real-world' trials are needed to optimize patient care.