Distress and Psycho-Oncological Support for Patients With Advanced Breast Cancer.

OBJECTIVES: Patients with advanced breast cancer (ABC) face an incurable disease that brings along many challenges. Health care professionals, and nurses in particular, have a main role in supporting these patients to adapt and adjust to their condition. In this study, we discuss how good communication skills can be the first level of emotional support to patients and families; and how the high prevalence of distress in this population makes it of great importance to screen for distress regularly and treat it when needed. DATA SOURCES: We present our research study on the impact of negative effects on biobehavioral processes that contribute to disease progression, and comment on the psychological interventions that may reduce it, with a particular focus on the CALM therapy model we validated for the Portuguese ABC patients. We also report on the added human value of a retreat for couples and professionals that our team has tested with ABC patients and their partners. CONCLUSION: It is critical to screen for distress in ABC patients who have a higher prevalence of distress. There are available evidence-based interventions to assist clinicians in reducing their suffering. CALM therapy and a retreat format may be options to consider with ABC patients. IMPLICATIONS FOR NURSING PRACTICE: As front-line clinicians, nurses have an important role in providing provide emotional support to patients using good communication skills, but also in identifying patients at risk for distress, screening for it regularly, and referring patients for specialized psychosocial care when needed.

Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN).

Background: Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN). Methods: Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN. Results: Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%. Conclusion: The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress.