关键词: 肺癌, 诊疗延迟, 精细化分段, 疾病管理, 影响因素

Abstract: Objective To investigate the current status of diagnostic and treatment delays among lung cancer patients utilizing a fine-grained segmentation approach and to analyze the multidimensional factors influencing these delays. Methods A retrospective study was conducted on the clinical data of lung cancer patients admitted to the First Hospital of Handan City from January 2024 to December 2024. Data on patient-related delay, diagnostic delay, treatment initiation delay, and total delay were collected. Linear regression analysis was employed to identify the determinants of each delay interval. Results Among the 366 lung cancer patients included, the median patient-related delay was 60.00 (44.00, 74.00) days;the median diagnostic delay was 34.00 (24.00, 46.25) days;the median treatment initiation delay was 20.00 (14.00, 26.00) days;and the median total delay was 114.00 (93.75, 133.25) days. Linear regression analysis revealed that age (≥60 years, β′=0.145), health insurance type (urban employee-based, β′=-0.236), and clinical stage at diagnosis (Stage I-II, β′=0.136) were significant factors influencing patient-related delay (P<0.05). Determinants of diagnostic delay included health insurance type (urban employee-based, β′=-0.348;self-funded, β′=-0.292), clinical stage at diagnosis (Stage I-II, β′=0.136), level of the initial consultation hospital (tertiary hospital, β′=-2.267), and experiencing inter-hospital transfers to obtain a definitive diagnosis (β′=0.157) (P<0.05). Factors associated with treatment initiation delay were clinical stage at diagnosis (Stage I-II, β′=1.137), experiencing inter-hospital transfers for diagnosis (β′=0.151), and awaiting genetic testing results (β′=0.158). The significant predictors for total delay were age (≥60 years, β′=0.143), health insurance type (urban employee-based, β′=-0.229;self-funded, β′=0.123), clinical stage at diagnosis (Stage I-II, β′=0.136), level of the initial consultation hospital (secondary hospital, β′=-0.234;tertiary hospital, β′=-0.329), experiencing inter-hospital transfers for diagnosis (β′=0.146), and awaiting genetic testing results (β′=0.136). Conclusions The delays in the diagnosis and treatment of lung cancer, in descending order of duration, were patient-related delay, diagnostic delay, and treatment initiation delay. Targeted interventions based on the influencing factors identified at each stage may be implemented to shorten the overall diagnostic and treatment timeline for lung cancer.

Key words: Lung cancer, Diagnostic and treatment delays, Refined segmentation, Disease management, Influencing factors