Uncovering Barriers to Screening for Distress in Patients With Cancer via Machine Learning.

Abstract

Psychologic distress and manifest mental disorders are overlooked in 30-50% of patients with cancer. Accordingly, international cancer treatment guidelines recommend routine screening for distress in order to provide psychologic support to those in need. Yet, institutional and patient-related factors continue to hinder implementation. This study aims to investigate factors, which are associated with no screening for distress in patients with cancer. Using machine learning, factors associated with lack of distress screening were explored in 6491 patients with cancer between 2011 and 2019 at a large cancer treatment center. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting and to comply with assumptions for machine learning approaches. Patients unlikely to be screened were not discussed at a tumor board, had inpatient treatment of less than 28 days, did not consult with a psychiatrist or clinical psychologist, had no (primary) nervous system cancer, no head and neck cancer, and did have breast or skin cancer. The final validated model was optimized to maximize sensitivity at 83.9%, and achieved a balanced accuracy of 68.9, area under the curve of 0.80, and specificity of 53.9%. Findings of this study may be relevant to stakeholders at both a clinical and institutional level in order to optimize distress screening rates.