Prevalence of financial toxicity and construction of a risk prediction model for lung cancer patients at 6 months after discharge

Vollständiger Abstract

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Objective To investigate the prevalence of 6-month post-discharge financial toxicity in lung cancer patients, identify its independent predictors, and establish a valid risk prediction model integrating socioeconomic, clinical, and patient-reported outcome variables as baseline predictors, so as to support early financial toxicity screening, targeted financial counseling and standardized nurse-led stratified transitional nursing interventions. Methods A single-center prospective cohort study enrolled 682 hospitalized lung cancer patients. Baseline demographic, socioeconomic, clinical, and patient-reported data (resilience, social support, fear of progression, symptom burden, self-efficacy) were collected. financial toxicity was assessed via the COST-PROM at 6 months post-discharge (score ≤ 25 defined as financial toxicity). Multivariable logistic regression was used to build the model, with ROC curve, calibration test, calibration slope, calibration intercept, optimism, shrinkage factor, Decision Curve Analysis, and 500-time Bootstrap resampling internal validation. Results The 6-month post-discharge financial toxicity incidence was 76.2%. Annual household income, long-term residence, immunotherapy, self-efficacy, resilience, and fear of progression were independent predictors ( P < 0.05). The model AUC was 0.901 (Bootstrap-corrected: 0.894), with an optimal cut-off of 0.757 (sensitivity: 0.800, specificity: 0.852). The Hosmer-Lemeshow test confirmed good calibration, and risk subgroups showed increasing financial toxicity incidence, demonstrating favorable stratification ability. Conclusion Lung cancer patients/survivors suffer from substantial financial toxicity within 6 months after hospital discharge, with household economic status, residential location, treatment modality, and diverse psychosocial coping factors serving as critical joint predictors of financial toxicity risk. The newly constructed prediction model underwent rigorous 500-time Bootstrap internal validation and demonstrated favorable discrimination, calibration, stability and overall predictive efficacy. This model enables clinicians and nurses to identify patients at high risk of financial toxicity before discharge, conduct early financial toxicity assessment, provide preliminary guidance on healthcare costs and insurance information, and coordinate tiered transitional care interventions through multidisciplinary collaboration to address cancer-related financial distress. Nevertheless, external validation remains essential before formal clinical implementation of the model.