Integrating Clinics, Laboratory, and Imaging for the Diagnosis of Common Variable Immunodeficiency-Related Granulomatous-Lymphocytic Interstitial Lung Disease

Abstract

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.

Keywords: CVID; GLILD; common variable immunodeficiency; diagnosis; interstitial lung disease; predictive model; scoring system; splenomegaly.

Figure 1

(A) Nomogram predicting the probability of GLILD in CVID patients. (B) Receiver operating characteristic curve of the Bayesian regression model including Baumann’s composite GLILD score, splenomegaly, CD8 cell count, and generalized lymphadenopathies for the prediction of GLILD. To obtain the nomogram predicted probability of GLILD, locate the patient values for each of the four variables at their own variable axis. Draw a vertical line to the upper “Point” axis to determine how many points are attributed for each variable value. Sum the points for all variables. Locate the sum of the total points in the “Total points” line. Draw a vertical line towards the “Probability of GLILD” line to determine the estimated probability of GLILD (an example of the use of this nomogram is provided in Appendix S1 ). GLILD, granulomatous–lymphocytic interstitial lung disease; CVID, common variable immunodeficiency.

Figure 2

Chest CT from 4 different patients of our study with some of the evaluated parameters. (A) GLILD patient with multiple bilateral ground-glass nodules <5 mm (arrowheads), solid nodules (stars), and mixed (inflammatory and fibrotic) lines (arrow). (B) Non-GLILD patient with diffuse mild bronchial wall thickening and mild bronchiectasis (arrows) with air trapping predominantly in the upper lobes and partial atelectasis of left lower lobe. (C) Non-GLILD patient with linear parenchymal band in the left upper lobe (arrowhead). (D) Non-GLILD patient with mucus plugging in large airways of the left lower lobe with >50% atelectasis of the same lobe (arrows), complete atelectasis of the middle lobe (star), tree in bud pattern in the right lower lobe (arrows), and air trapping on the left upper and right lower lobes. GLILD, granulomatous–lymphocytic interstitial lung disease.