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A Nomogram for Predicting Pulmonary Embolism in Silicosis Patients
Jiaqing Zhou1, Wen Du1, Jin Liu2
1Department of Respiratory Medicine, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
The Clinical Respiratory Journal
|March 4, 2025
Summary
Researchers developed a new prediction model to identify pulmonary embolism (PE) in patients with silicosis. This tool aids clinical decision-making for this severe occupational lung disease.
Area of Science:
- Occupational Medicine
- Pulmonary Medicine
- Medical Informatics
Background:
- Silicosis is a severe occupational disease with significant public health implications.
- Existing prevention efforts have not led to a predictive model for pulmonary embolism (PE) in silicosis patients.
Purpose of the Study:
- To develop and validate a novel nomogram for predicting pulmonary embolism in individuals diagnosed with silicosis.
Main Methods:
- A training cohort of 162 silicosis patients was utilized.
- Logistic regression, univariate, and LASSO analyses were employed to select variables.
- Computed tomography pulmonary angiography (CTPA) was used for outcome diagnosis.
Main Results:
- Key predictors identified for the nomogram included mMRC, chest pain, VTE history, active tumors, unilateral leg symptoms, hormone therapy, reduced mobility, and heart/respiratory failure.
- A nomogram was successfully established incorporating these variables.
Conclusions:
- A new, validated nomogram can predict pulmonary embolism in silicosis patients.
- This predictive model can assist clinicians in decision-making and patient management.
