Search

The distress thermometer (DT) is a well-established screening tool to detect clinically significant distress in cancer patients. It is often administered in combination with the problem list (PL), differentiating further between various (e.g., physical and emotional) sources of distress. The present study aimed to extend previous research on the association between distress and overall survival. Further exploratory analysis aimed to evaluate the predictive value of the PL for overall survival.Patients (n=323) with newly diagnosed lung cancer were recruited from a large cancer center. Patients were split into two groups, those with (DT score ≥5) and those without significant distress. Overall survival time was illustrated by a Kaplan Meier curve and compared with a log rank test. Univariable Cox proportional hazard models were built to control the association of distress with overall survival for age, gender, disease stage,comorbidity and their interaction terms. A multiple linear regression was used to investigate the association of the items from the problem list with survival time.Patients with significant distress had a shorter survival time compared to patients without significant distress (25 vs. 43 months). Regression analysis revealed more problems with both "bathing and dressing" and "eating", as well as absence of "diarrhea" and increased "nervousness" to negatively impact overall survival time.Our results show that estimation of the survival function using cancer-related distress is possible. However, when using Cox regression, distress shows no significant value for survival as a predictor. Moreover, our study did not reveal an interaction effect between disease stage, comorbidity, and distress. Overall, results suggest that physical and emotional problems that arise from lung cancer may be useful to identify patients at risk for poor prognosis.

Background 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. Objective This study aims to investigate factors, which are associated with no screening for distress in patients with cancer. Methods 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. Results 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%. Conclusion Findings of this study may be relevant to stakeholders at both a clinical and institutional level in order to optimize distress screening rates.