Prevalence of pulmonary nodules in patients of coal worker's pneumoconiosis and analysis of risk factors of high-risk nodules
Objective To investigate the prevalence and risk factors of pulmonary nodules in coal worker's pneumoconiosis patients and to clarify the CT parameters and diagnostic efficacy of high-risk nodules.Methods The demographic data of coal worker's pneumoconiosis patients hospitalized at Xuzhou Mining Group General Hospital from January 1,2019,to June 30,2023,were retrospectively included,and the data of quantitative CT parameters were analyzed to understand the risk factors for the occurrence of pulmonary nodules.The binary logistics regression analysis and ROC curve were used to clarify the CT parameters and diagnostic efficacy of high-risk nodule screening.Results Among 156 patients,124(79.5％)had stage I pneumoconiosis,28(18.0％)had stage II pneumoconiosis,and 4(2.6％)had stage Ⅲ pneumoconiosis.Low-risk silica dust nodules were detected in 86 patients(55.1％)and high-risk nodules in 9 patients(5.8％)had.The mean diameter and maximum cross-sectional area of high-risk nodules were significantly higher than those of non-high-risk nodules(Z=-2.690,-2.836,P＜0.05),and the minimum CT value was significantly lower than that of non-high-risk nodules(Z=2.994,P＜0.05).The binary logistic regression results showed that the occurrence of high-risk nodules was closely related to the minimum CT value(Wald x2=5.897,P＜0.05).The ROC curve for predicting high-risk nodules showed that the mean diameter(AUC 0.775,sensitivity 77.8％,specificity 79.2％),minimum CT value(AUC 0.807,sensitivity 77.8％,specificity 76.6％),and maximum cross-sectional area(AUC 0.790,sensitivity 77.8％,specificity 83.1％)had predictive value for high-risk nodules.Conclusion More than half of coal worker's pneumoconiosis patients have been detected with pulmonary nodules.Quantitative analysis CT with mean diameter,minimum CT value,and maximum cross-sectional area as indicators can assess high-risk nodules of coal worker's pneumoconiosis patients,it should be given attention in clinical practice.
NETL
NSTL
万方数据
维普
