期刊
CURRENT ONCOLOGY
卷 30, 期 11, 页码 9746-9759出版社
MDPI
DOI: 10.3390/curroncol30110707
关键词
distress in oncological patients; rejecting psycho-oncological support; distress screening; machine learning
类别
This study aims to explore factors associated with declining psycho-oncological support in order to increase nurses' efficiency in screening patients. The developed model has a high sensitivity in detecting patients who decline psycho-oncological support, with older patients, patients with lower distress levels, fewer comorbidities, fewer physical problems, and those who do not feel sad, afraid, or worried being more likely to refuse psycho-oncological support.
(1) Background: International cancer treatment guidelines recommend low-threshold psycho-oncological support based on nurses' routine distress screening (e.g., via the distress thermometer and problem list). This study aims to explore factors which are associated with declining psycho-oncological support in order to increase nurses' efficiency in screening patients for psycho-oncological support needs. (2) Methods: Using machine learning, routinely recorded clinical data from 4064 patients was analyzed for predictors of patients declining psycho-oncological support. Cross validation and nested resampling were used to guard against model overfitting. (3) Results: The developed model detects patients who decline psycho-oncological support with a sensitivity of 89% (area under the cure of 79%, accuracy of 68.5%). Overall, older patients, patients with a lower score on the distress thermometer, fewer comorbidities, few physical problems, and those who do not feel sad, afraid, or worried refused psycho-oncological support. (4) Conclusions: Thus, current screening procedures seem worthy to be part of daily nursing routines in oncology, but nurses may need more time and training to rule out misconceptions of patients on psycho-oncological support.
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