Development and internal validation of a nomogram for predicting financial toxicity in patients with Lung cancer (Preprint)

Abstract

Financial toxicity refers to the economic hardships faced by cancer patients and their families due to treatment costs and income loss.

This study aimed to develop a predictive nomogram for financial toxicity among lung cancer patients.

We surveyed lung cancer patients at a tertiary oncology hospital in Shandong from October to December 2023 using random sampling. Factors influencing financial toxicity were identified through univariate logistic and multivariate stepwise regression analyses, and a nomogram prediction model was constructed and validated.

A total of 826 lung cancer patients were recruited and randomly divided into training (n=584) and validation (n=250) cohorts. Independent risk factors for financial toxicity included annual household income, enrollment family doctor services, debt due to medical treatment, number of exercise sessions per week, history of targeted therapy, working status after diagnosis, and time since diagnosis (all P < 0.05). These factors were used to construct the nomograms, which demonstrated good discriminative ability in both the training and validation cohorts, with AUC values of 0.814 and 0.889, respectively. The Hosmer-Lemeshow test confirmed a good fit of the model (P > 0.05). Both the calibration curves and decision curve analysis validated the model's accuracy and clinical utility.

Financial toxicity is relatively common among lung cancer patients, with over half facing economic challenges associated with their condition. The developed nomogram accurately predicts financial toxicity risk, aiding in early identification and intervention for at-risk patients, providing a basis for devising personalized intervention strategies.