A Nomogram for Predicting Pulmonary Embolism in Silicosis Patients

Background: As one of the most severe occupational diseases that prevention efforts have supported for several decades, silicosis is still a public health issue that lacks a prediction model for pulmonary embolism. Methods: A total of 162 patients confirmed to have silicosis were all involved in a training cohort to construct a nomogram with the outcome diagnosed by the CTPA using logistic regression. Univariate and LASSO analyses were used to select variables for the nomogram. Result: mMRC, pectoralgia, history of VTE, active tumor, unilateral lower limb pain or edema, hormonotherapy, reduced mobility, and heart failure/respiratory failure were selected for the establishment of the nomogram for silicosis with pulmonary embolism. Conclusion: A novel nomogram was developed to predict pulmonary embolism in silicosis patients. The internal validation indicated that clinicians could utilize this predictive model to help decision-making and patient management.