ArticleERJ open research2023
Development of a risk score to increase detection of severe alpha-1 antitrypsin deficiency.
Article in ERJ open research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Alpha-1 Antitrypsin Deficiency in Patients With Cirrhosis in a US National Cohort.Clinical and translational gastroenterology · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Alpha-1 antitrypsin deficiency (AATD) is an under-recognised genetic cause of chronic obstructive lung disease, and many fewer cases than estimated have been identified. Can a reported respiratory and hepatic disease history from a large AATD testing database be used to stratify a person's risk of severe AATD? Methods: We analysed data extracted from the AATD National Detection Program. Demographics and medical history were evaluated to predict AATD PI*ZZ genotype. Logistic regression and integer programming models identified predictors and obtained risk scores. These were internally validated on a subset of the data. Results: Out of 301 343 subjects, 1529 (0.5%) had PI*ZZ genotype. Predictors of severe AATD were asthma, bronchitis, emphysema, allergies, bronchiectasis, family history of AATD, cirrhosis, hepatitis and history of abnormal liver function tests. The derived model establishes a subject's risk of severe AATD, and scores ≥0 had an estimated risk of 0.41%, sensitivity 84.62% and specificity 24.32%. A model simulating guideline recommendations had an estimated risk of 0.51% with a sensitivity of 37.98% and specificity 46.60%. By recommending screening for scores ≥0, we estimate that more subjects would be screened (75.7% Conclusion: This medical history risk model is a useful predictive tool to detect subjects at greater risk of having severe AATD and improves sensitivity of detection. Scores <0 are at lower risk and may need not be screened; testing is recommended for scores ≥0 and consistent with current guidelines.
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Registered trials
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