Impact of genomic testing and patient-reported outcomes on receipt of adjuvant chemotherapy

Practice guidelines incorporate genomic tumor profiling, using results such as the Oncotype DX Recurrence Score (RS), to refine recurrence risk estimates for the large proportion of breast cancer patients with early-stage, estrogen receptor-positive disease. We sought to understand the impact of receiving genomic recurrence risk estimates on breast cancer patients’ well-being and the impact of these patient-reported outcomes on receipt of adjuvant chemotherapy. Participants were 193 women (mean age 57) newly diagnosed with early-stage breast cancer. Women were interviewed before and 2–3 weeks after receiving the RS result between 2011 and 2015. We assessed subsequent receipt of chemotherapy from chart review. After receiving their RS, perceived pros (t = 4.27, P < .001) and cons (t = 8.54, P <.001) of chemotherapy increased from pre-test to post-test, while perceived risk of breast cancer recurrence decreased (t = 2.90, P = .004). Women with high RS tumors were more likely to receive chemotherapy than women with low RS tumors (88 vs. 5 %, OR 0.01, 0.00–0.02, P < .001). Higher distress (OR 2.19, 95 % CI 1.05–4.57, P < .05) and lower perceived cons of chemotherapy (OR 0.50, 95 % CI 0.26–0.97, P < .05) also predicted receipt of chemotherapy. Distressed patients who saw few downsides of chemotherapy received this treatment. Clinicians should consider these factors when discussing chemotherapy with breast cancer patients

Understanding the Needs of Young Women Regarding Breast Cancer Risk Assessment and Genetic Testing: Convergence and Divergence among Patient-Counselor Perceptions and the Promise of Peer Support

Young women from hereditary breast and ovarian cancer (HBOC) families face a series of medical decisions regarding their cancer risk management and integrating this information into their life planning. This presents unique medical and psychosocial challenges that exist without comprehensive intervention. To help lay the groundwork for intervention, we conducted a qualitative study among young women from HBOC families (N = 12; Mean age = 22) and cancer genetic counselors (N = 12) to explicate domains most critical to caring for this population. Women and counselors were interviewed by telephone. The predominant interview themes included preventative care planning and risk management, decision making around the pros and cons of cancer risk assessment, medical management, and psychosocial stresses experienced. Young women endorsed psychosocial stress significantly more frequently than did counselors. Both groups noted the short- and long-term decision making challenges and the support and conflict engendered among familial relationships. Our results suggest young women value the support they receive from their families and their genetic counselors, but additional, external supports are needed to facilitate adaptation to HBOC risk. In feedback interviews focused on intervention planning with a subset of these young women (N = 9), they endorsed the predominant interview themes discovered as important intervention content, a structure that would balance discussion of medical information and psychosocial skill-building that could be tailored to the young women’s needs, and delivery by trained peers familiar with HBOC risk

Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial

Abstract Hospital readmission prediction models often perform poorly, but most only use information collected until the time of hospital discharge. In this clinical trial, we randomly assigned 500 patients discharged from hospital to home to use either a smartphone or wearable device to collect and transmit remote patient monitoring (RPM) data on activity patterns after hospital discharge. Analyses were conducted at the patient-day level using discrete-time survival analysis. Each arm was split into training and testing folds. The training set used fivefold cross-validation and then final model results are from predictions on the test set. A standard model comprised data collected up to the time of discharge including demographics, comorbidities, hospital length of stay, and vitals prior to discharge. An enhanced model consisted of the standard model plus RPM data. Traditional parametric regression models (logit and lasso) were compared to nonparametric machine learning approaches (random forest, gradient boosting, and ensemble). The main outcome was hospital readmission or death within 30 days of discharge. Prediction of 30-day hospital readmission significantly improved when including remotely-monitored patient data on activity patterns after hospital discharge and using nonparametric machine learning approaches. Wearables slightly outperformed smartphones but both had good prediction of 30-day hospital-readmission

A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor's appointment

Many Americans fail to get life-saving vaccines each year, and the availability of a vaccine for COVID-19 makes the challenge of encouraging vaccination more urgent than ever. We present a large field experiment (N = 47,306) testing 19 nudges delivered to patients via text message and designed to boost adoption of the influenza vaccine. Our findings suggest that text messages sent prior to a primary care visit can boost vaccination rates by an average of 5%. Overall, interventions performed better when they were 1) framed as reminders to get flu shots that were already reserved for the patient and 2) congruent with the sort of communications patients expected to receive from their healthcare provider (i.e., not surprising, casual, or interactive). The best-performing intervention in our study reminded patients twice to get their flu shot at their upcoming doctor's appointment and indicated it was reserved for them. This successful script could be used as a template for campaigns to encourage the adoption of life-saving vaccines, including against COVID-19.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Impact of genomic testing and patient-reported outcomes on receipt of adjuvant chemotherapy

Practice guidelines incorporate genomic tumor profiling, using results such as the Oncotype DX Recurrence Score (RS), to refine recurrence risk estimates for the large proportion of breast cancer patients with early-stage, estrogen receptor-positive disease. We sought to understand the impact of receiving genomic recurrence risk estimates on breast cancer patients’ well-being and the impact of these patient-reported outcomes on receipt of adjuvant chemotherapy. Participants were 193 women (mean age 57) newly diagnosed with early-stage breast cancer. Women were interviewed before and 2–3 weeks after receiving the RS result between 2011 and 2015. We assessed subsequent receipt of chemotherapy from chart review. After receiving their RS, perceived pros (t = 4.27, P < .001) and cons (t = 8.54, P <.001) of chemotherapy increased from pre-test to post-test, while perceived risk of breast cancer recurrence decreased (t = 2.90, P = .004). Women with high RS tumors were more likely to receive chemotherapy than women with low RS tumors (88 vs. 5 %, OR 0.01, 0.00–0.02, P < .001). Higher distress (OR 2.19, 95 % CI 1.05–4.57, P < .05) and lower perceived cons of chemotherapy (OR 0.50, 95 % CI 0.26–0.97, P < .05) also predicted receipt of chemotherapy. Distressed patients who saw few downsides of chemotherapy received this treatment. Clinicians should consider these factors when discussing chemotherapy with breast cancer patients