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DataSheet_1_Integrating Clinics, Laboratory, and Imaging for the Diagnosis of Common Variable Immunodeficiency-Related Granulomatous–Lymphocytic Inter.docx (263.48 kB)

DataSheet_1_Integrating Clinics, Laboratory, and Imaging for the Diagnosis of Common Variable Immunodeficiency-Related Granulomatous–Lymphocytic Interstitial Lung Disease.docx

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posted on 2022-02-23, 04:07 authored by Marta Dafne Cabanero-Navalon, Victor Garcia-Bustos, Leonardo Fabio Forero-Naranjo, Eduardo José Baettig-Arriagada, María Núñez-Beltrán, Antonio José Cañada-Martínez, Maria José Forner Giner, Nelly Catalán-Cáceres, Manuela Martínez Francés, Pedro Moral Moral
Background

Granulomatous–lymphocytic interstitial lung disease (GLILD) is a distinct clinic-radio-pathological interstitial lung disease (ILD) that develops in 9% to 30% of patients with common variable immunodeficiency (CVID). Often related to extrapulmonary dysimmune disorders, it is associated with long-term lung damage and poorer clinical outcomes. The aim of this study was to explore the potential use of the integration between clinical parameters, laboratory variables, and developed CT scan scoring systems to improve the diagnostic accuracy of non-invasive tools.

Methods

A retrospective cross-sectional study of 50 CVID patients was conducted in a referral unit of primary immune deficiencies. Clinical variables including demographics and comorbidities; analytical parameters including immunoglobulin levels, lipid metabolism, and lymphocyte subpopulations; and radiological and lung function test parameters were collected. Baumann’s GLILD score system was externally validated by two observers in high-resolution CT (HRCT) scans. We developed an exploratory predictive model by elastic net and Bayesian regression, assessed its discriminative capacity, and internally validated it using bootstrap resampling.

Results

Lymphadenopathies (adjusted OR 9.42), splenomegaly (adjusted OR 6.25), Baumann’s GLILD score (adjusted OR 1.56), and CD8+ cell count (adjusted OR 0.9) were included in the model. The larger range of values of the validated Baumann’s GLILD HRCT scoring system gives it greater predictability. Cohen’s κ statistic was 0.832 (95% CI 0.70–0.90), showing high concordance between both observers. The combined model showed a very good discrimination capacity with an internally validated area under the curve (AUC) of 0.969.

Conclusion

Models integrating clinics, laboratory, and CT scan scoring methods may improve the accuracy of non-invasive diagnosis of GLILD and might even preclude aggressive diagnostic tools such as lung biopsy in selected patients.

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