Clinically Significant Distress and Physical Problems Detected on a Distress Thermometer are Associated With Survival Among Lung Cancer Patients.

BACKGROUND

The distress thermometer is a well-established screening tool to detect clinically significant distress in cancer patients. It is often administered in combination with the problem list, differentiating further between various (e.g., physical and emotional) sources of distress.

OBJECTIVE

The present study aimed to extend previous research on the association between distress and overall survival. A further exploratory analysis aimed to evaluate the predictive value of the problem list for overall survival.

METHODS

Patients (n = 323) with newly diagnosed lung cancer were recruited from a large cancer center. Patients were split into 2 groups, those with (distress thermometer score ≥5) and those without significant distress. The 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. Multiple linear regression was used to investigate the association of the items from the problem list with survival time.

RESULTS

Patients with significant distress had a shorter survival time than 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 associated with the overall survival time.

CONCLUSIONS

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 among 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 of poor prognosis (on the basis of Kaplan-Meier estimator).