Significance of Nonphysical Predictors of Distress in Cancer Survivors

Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN)

Background

Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN).

Methods

Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN.

Results

Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%.

Conclusion

The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress.

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).

Psychosocial impact of the COVID-19 outbreak and lockdown on Spanish oncological patients: a quantitative approach

BACKGROUND

This study analyses the levels of distress and related psychosocial factors among cancer patients during the Spanish lockdown due to COVID-19.

METHODS

A total of 2,779 cancer patients took part in an observational and lateral study carried out between April 16, 2020 and April 25, 2020. An online questionnaire was distributed including distress-related variables, demographic variables, clinical variables about their oncological condition, socioeconomic variables and variables related to information management and social communication. Distress was measured according to the Kessler (K-6) scale, and its relationship with the remaining variables was analyzed by logistic regression.

RESULTS

33.5% of the patients yielded levels of clinical distress during lockdown. Younger patients and women yielded significantly higher levels of distress. High distress levels were generally associated with the following factors: trust in medical institutions; deterioration of the household's financial conditions; and media management of the information about the pandemic.

CONCLUSIONS

The lockdown triggered by COVID-19 increased distress among cancer patients, and this can be significantly related to a number of variables. Identifying distress, and said factors, at an early stage can help to develop mitigation strategies. Similarly, early detection can help to improve the way information is shared with patients, offer them support and resources and direct them to psychosocial services, increasing the patient's ability to return to normal after COVID-19.