{"title":"A Nomogram for Predicting Pulmonary Embolism in Silicosis Patients","authors":"Jiaqing Zhou, Wen Du, Jin Liu, Lijun Peng","doi":"10.1111/crj.70059","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Result</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":55247,"journal":{"name":"Clinical Respiratory Journal","volume":"19 3","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/crj.70059","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Respiratory Journal","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/crj.70059","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Overview
Effective with the 2016 volume, this journal will be published in an online-only format.
Aims and Scope
The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic.
We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including:
Asthma
Allergy
COPD
Non-invasive ventilation
Sleep related breathing disorders
Interstitial lung diseases
Lung cancer
Clinical genetics
Rhinitis
Airway and lung infection
Epidemiology
Pediatrics
CRJ provides a fast-track service for selected Phase II and Phase III trial studies.
Keywords
Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease,
Abstracting and Indexing Information
Academic Search (EBSCO Publishing)
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Embase (Elsevier)
Health & Medical Collection (ProQuest)
Health Research Premium Collection (ProQuest)
HEED: Health Economic Evaluations Database (Wiley-Blackwell)
Hospital Premium Collection (ProQuest)
Journal Citation Reports/Science Edition (Clarivate Analytics)
MEDLINE/PubMed (NLM)
ProQuest Central (ProQuest)
Science Citation Index Expanded (Clarivate Analytics)
SCOPUS (Elsevier)