Primary Immune Deficiencies Unit, Department of Internal Medicine of the University and Polytechnic Hospital La Fe, Valencia, Spain.
Department of Pneumology, University and Polytechnic Hospital La Fe, Valencia, Spain.
Front Immunol. 2022 Feb 23;13:813491. doi: 10.3389/fimmu.2022.813491. eCollection 2022.
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.
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.
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.
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.
肉芽肿性淋巴细胞性间质性肺病（GLILD）是一种独特的临床-放射-病理间质性肺病（ILD），在 9%至 30%的普通可变免疫缺陷（CVID）患者中发展。它常与肺外免疫失调疾病相关，与长期肺损伤和更差的临床结局相关。本研究旨在探讨整合临床参数、实验室变量和开发的 CT 扫描评分系统，以提高非侵入性工具诊断准确性的潜力。
对一家原发性免疫缺陷转诊单位的 50 例 CVID 患者进行回顾性横断面研究。收集临床变量（包括人口统计学和合并症）；分析参数（包括免疫球蛋白水平、脂质代谢和淋巴细胞亚群）；以及影像学和肺功能测试参数。通过两位观察者对高分辨率 CT（HRCT）扫描中的鲍曼 GLILD 评分系统进行外部验证。我们通过弹性网络和贝叶斯回归开发了一个探索性预测模型，评估其判别能力，并通过自举重采样对内进行验证。
淋巴结病（调整后的优势比 9.42）、脾肿大（调整后的优势比 6.25）、鲍曼 GLILD 评分（调整后的优势比 1.56）和 CD8+细胞计数（调整后的优势比 0.9）被纳入模型。验证后的鲍曼 GLILD HRCT 评分系统的更大取值范围使其具有更高的可预测性。两位观察者之间的 Cohen's κ 统计量为 0.832（95%CI 0.70-0.90），显示出高度一致性。联合模型具有很好的判别能力，内部验证的曲线下面积（AUC）为 0.969。
整合临床、实验室和 CT 扫描评分方法的模型可能会提高 GLILD 非侵入性诊断的准确性，甚至可以避免在某些患者中使用有创诊断工具，如肺活检。
