Improving the Likelihood of Identifying Alpha-1 Antitrypsin Deficiency Among Patients With COPD: A Novel Predictive Model Using Real-World Data

Predictive modeling using real-world data is a sound approach for assessing AATD risk and useful for identifying COPD patients who should be confirmed by genetic testing. External validation is warranted to further assess the generalizability of these results.

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Publication Details

Journal
Chronic Obstructive Pulmonary Diseases Journal of the COPD Foundation
Published
2024-12-04
DOI
https://doi.org/10.15326/jcopdf.2023.0491
Citations
2
Primary Topic
Protease and Inhibitor Mechanisms
Type
article
Field-Weighted Citation Impact
0.57
Controls
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article

Improving the Likelihood of Identifying Alpha-1 Antitrypsin Deficiency Among Patients With COPD: A Novel Predictive Model Using Real-World Data

Pulkit Sehgal, Charlie Strange, Michael Calloway, Thomas Ardiles et al.
2 citations
Chronic Obstructive Pulmonary Diseases Journal of the COPD Foundation
Protease and Inhibitor Mechanisms
0.57
article

Improving the Likelihood of Identifying Alpha-1 Antitrypsin Deficiency Among Patients With COPD: A Novel Predictive Model Using Real-World Data

Pulkit Sehgal, Charlie Strange, Michael Calloway, Thomas Ardiles, Rahul Dhakne, Daniel N. Pfeffer, Omnya E Massad, M. Chris Runken
article en
2 citations

Abstract

Predictive modeling using real-world data is a sound approach for assessing AATD risk and useful for identifying COPD patients who should be confirmed by genetic testing. External validation is warranted to further assess the generalizability of these results.

Chronic Obstructive Pulmonary Diseases Journal of the COPD FoundationVol. 12(1)
Medical University of South Carolina (US), STEM Forward (US), AlphaNet (US), Health Economics and Outcomes Research (United Kingdom) (GB), Durham VA Medical Center (US), EVERSANA (Canada) (CA)
Zero hunger
Openalex Percentile: Top 30%
Protease and Inhibitor Mechanisms
0.57
DOIOpen Access PDFOpenAlex
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