De Angelis Rossella, Cipolletta Edoardo, Francioso Francesca, Carotti Marina, Farah Sonia, Giovagnoni Andrea, Salaffi Fausto
Rheumatology Unit, Department of Clinical and Molecular Sciences, Polytechnic University of Marche, "Carlo Urbani" Hospital, Jesi, 60035 Ancona, Italy.
IRCCS INRCA, 60121 Ancona, Italy.
J Pers Med. 2024 Jun 14;14(6):635. doi: 10.3390/jpm14060635.
The aim of this paper is to identify factors associated with interstitial lung disease (ILD) in patients with systemic sclerosis (SSc) and build an algorithm to better define this association for a personalised application in clinical practice.
A total of 78 SSc patients underwent HRCT to assess ILD. Demographic, clinical and laboratory variables were collected, focusing on those associated either directly or indirectly with lung involvement. The discriminant value of each variable was determined using the operating characteristic curves (ROC) and included in a model to estimate the strength of ILD association in SSc.
Thirty-three (42.31%) patients showed ILD on HRCT. DLco, M-Borg, GERD-Q and capillary density were significantly associated with the presence of ILD-SSc. A model including these variables had a coefficient of determination (R) of 0.697. DLco had an AUC of 0.861 ( < 0.001) with a cut-off of ≤72.3% (sensitivity 78.8%, specificity 91.1%, +LR 8.86). The m-Borg Scale showed an AUC of 0.883 ( < 0.001) with a cut-off >2 (sensitivity 84.8%, specificity 82.2%, +LR 4.77), GERD-Q had an AUC of 0.815 ( < 0.001) with a cut-off >7 (sensitivity 72.7%, specificity 86.7%, +LR 5.45). The capillary density showed an AUC of 0.815 ( < 0.001) with a cut-off of ≤4.78 (sensitivity 87.9%, specificity 68.9%, +LR 2.82). Based on the pre-test probability values, these four variables were applied to Fagan's nomogram to calculate the post-test probability of this association.
Our study identified four associated clinical factors of ILD in SSc patients. Moreover, their inclusion in an algorithm for the post-test probability, tailored to the specific patients' characteristics, significantly increases the ability to find out the presence of SSc-ILD.
本文旨在确定系统性硬化症（SSc）患者间质性肺疾病（ILD）的相关因素，并构建一种算法，以便在临床实践中更好地界定这种关联，实现个性化应用。
共78例SSc患者接受了HRCT以评估ILD。收集了人口统计学、临床和实验室变量，重点关注那些直接或间接与肺部受累相关的变量。使用操作特征曲线（ROC）确定每个变量的判别值，并将其纳入一个模型，以估计SSc中ILD关联的强度。
33例（42.31%）患者在HRCT上显示有ILD。DLco、M-博格量表、GERD-Q和毛细血管密度与ILD-SSc的存在显著相关。包含这些变量的模型的决定系数（R）为0.697。DLco的曲线下面积（AUC）为0.861（<0.001），截断值≤72.3%（敏感性78.8%，特异性91.1%，阳性似然比8.86）。M-博格量表的AUC为0.883（<0.001），截断值>2（敏感性84.8%，特异性82.2%，阳性似然比4.77），GERD-Q的AUC为0.815（<0.001），截断值>7（敏感性72.7%，特异性86.7%，阳性似然比5.45）。毛细血管密度的AUC为0.815（<0.001），截断值≤4.78（敏感性87.9%，特异性68.9%，阳性似然比2.82）。根据检验前概率值，将这四个变量应用于费根列线图，以计算这种关联的检验后概率。
我们的研究确定了SSc患者中ILD的四个相关临床因素。此外，将它们纳入针对特定患者特征量身定制的检验后概率算法中，显著提高了发现SSc-ILD存在的能力。
