Towards identifying cancer patients at risk to miss out on psycho‐oncological treatment via machine learning. (9th February 2022)

• Record Type: Journal Article
• Authors: Günther, Moritz Philipp; Kirchebner, Johannes; Schulze, Jan Ben; von Känel, Roland; Euler, Sebastian
• Is Part Of: European journal of cancer care. Volume 31:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-09
• Subjects: cancer -- machine learning -- mental disorders -- psychological support -- psycho‐oncology; Cancer -- Nursing -- Periodicals; 616.994
• DOI: 10.1111/ecc.13555
• Languages: English
• ISSNs: 0961-5423

Abstract

Abstract: Objective: In routine oncological treatment settings, psychological distress, including mental disorders, is overlooked in 30% to 50% of patients. High workload and a constant need to optimise time and costs require a quick and easy method to identify patients likely to miss out on psychological support. Methods: Using machine learning, factors associated with no consultation with a clinical psychologist or psychiatrist were identified between 2011 and 2019 in 7, 318 oncological patients in a large cancer treatment centre. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting. Results: Patients were least likely to receive psycho‐oncological (i.e., psychiatric/psychotherapeutic) treatment when they were not formally screened for distress, had inpatient treatment for less than 28 days, had no psychiatric diagnosis, were aged 65 or older, had skin cancer or were not being discussed in a tumour board. The final validated model was optimised to maximise sensitivity at 85.9% and achieved an area under the curve (AUC) of 0.75, a balanced accuracy of 68.5% and specificity of 51.2%. Conclusion: Beyond conventional screening tools, results might contribute to identify patients at risk to be neglected in terms of referral to psycho‐oncology within routine oncological care.