关键词: 非小细胞肺癌, 影响因素, 因果图模型, 快速因果推断算法

Abstract: Objective The aim of this study was to use the fast causal inference(FCI)algorithm to construct a causal graph model,analyze the direct and indirect factors that affect the quality of treatment for non-small cell lung cancer(NSCLC),and provide a basis for improving the quality of patient treatment. Methods Case information of NSCLC patients from 10 tertiary hospitals was collected;the influencing factors were determined as the research variable,and the incidence of adverse events was the evaluation indicator of patient treatment quality,i.e.the outcome variable;the FCI algorithm to mine case data were used to construct a causal diagram model of research variables and outcomes,and analyze causal relationships between research variables and outcome variables,as well as between different research variables. Results A total of 2,846 patients with an average age of 56.00±7.70 years were included in this study,and the incidence of adverse events was 9.63%.The causal diagram model consisted 24 nodes and 71 edges,including 54 directed edges and 7 bidirectional edges.The direct factors affecting the occurrence of adverse events included hospital type,histological grade,lymph node dissection,and length of hospitalization;indirect factors included occupation,medical insurance type,current medical history,pathological stage,comprehensive treatment,surgical nature,and type of lung resection;The analysis of the interaction between factors showed that the current medical history,histological classification,comprehensive treatment,surgical nature,and type of lung resection determined whether the patient received lymph node dissection;The nature of surgery,method of lung resection,and comprehensive treatment affected the length of hospitalization;Medical history affected the histological classification of lung cancer;The type of occupation and medical insurance affected the type of hospital where patients sought medical treatment. Conclusion In the analysis of factors affecting the quality of NSCLC treatment,the causal diagram model can obtain direct and indirect factors that affect the occurrence of adverse events,identify target variables that can be intervened,and provide a basis for improving the quality of NSCLC treatment;Hospitals can reduce the incidence of adverse events by increasing the acceptance rate of lymph node dissection and comprehensive treatment.

Key words: Non-small cell lung cancer, Influencing factors, Causal diagram model, Fast causal inference algorithm