Developing a visual model for predicting depression in patients with lung cancer

Abstract Aims and objectives To investigate and analyse the prevalence of depression among patients with lung cancer, identify risk factors of depression, and develop a visual, non‐invasive, and straightforward clinical prediction model that can be used to predict the risk probability of depression in patients with lung cancer quantitatively. Background Depression is one of the common concomitant symptoms of patients with lung cancer, which can increase the risk of suicide. However, the current assessment tools cannot combine multiple risk factors to predict the risk probability of depression in patients. Design A cross‐sectional study. Methods The clinical data from 297 patients with lung cancer in China were collected and analysed in this cross‐sectional study. The clinical prediction model was constructed according to the results of the Chi‐square test and the logistic regression analysis, evaluated by discrimination, calibration, and decision curve analysis, and visualised by a nomogram. This study was reported using the TRIPOD checklist. Results 130 patients with lung cancer had depressive symptoms with a prevalence of 43.77%. A visual prediction model was constructed based on age, disease duration, exercise, stigma, and resilience. This model showed good discrimination at an AUC of 0.842. Calibration curve analysis indicated a good agreement between experimental and predicted values, and the decision curve analysis showed a high clinical utility. Conclusions The visual prediction model developed in this study has excellent performance, which can accurately predict the occurrence of depression in patients with lung cancer at an early stage and assist the medical staff in taking targeted preventative measures. Relevance to clinical practice The visual, non‐invasive, and simple nomogram can help clinical medical staff to calculate the risk probability of depression among patients with lung cancer, formulate personalised preventive care measures for high‐risk groups as soon as possible, and improve the quality of life of patients.