Kreuter Michael, Lee Joyce S, Tzouvelekis Argyrios, Oldham Justin M, Molyneaux Philip L, Weycker Derek, Atwood Mark, Samara Katerina, Kirchgässler Klaus-Uwe, Maher Toby M
Center for Pulmonary Medicine, Departments of Pneumology, Mainz University Medical Center, and of Pulmonary, Critical Care and Sleep Medicine, Marienhaus Clinic Mainz, Mainz, Germany.
Department of Medicine, University of Colorado, Denver, CO, USA.
ERJ Open Res. 2024 Jul 29;10(4). doi: 10.1183/23120541.00666-2023. eCollection 2024 Jul.
The Gender, Age and Physiology (GAP) model is a simple mortality prediction tool in patients with idiopathic pulmonary fibrosis that uses demographic and physiological variables available at initial evaluation. White blood cell variables may have associations with idiopathic pulmonary fibrosis outcomes. We evaluated whether incorporating blood cell counts in modified GAP (cGAP) models would improve outcome prediction in patients with idiopathic pulmonary fibrosis.
This retrospective analysis included pooled data from phase 3 randomised trials of pirfenidone in idiopathic pulmonary fibrosis (ASCEND, CAPACITY 004, CAPACITY 006). Study outcomes (disease progression, all-cause mortality, all-cause hospitalisation, respiratory-related hospitalisation) were evaluated during the initial 1-year period. Shared frailty models were used to evaluate associations between continuous and categorical baseline white and red blood cell parameters and study outcomes in a bivariate context, and to evaluate the impact of adding continuous monocyte count (cGAP1) or white and red blood cell parameters (cGAP2) to traditional GAP variables in a multivariable context based on C-statistics changes.
Data were pooled from 1247 patients (pirfenidone, n=623; placebo, n=624). Significant associations (bivariate analyses) were idiopathic pulmonary fibrosis progression with neutrophil and eosinophil counts; all-cause mortality with monocyte and neutrophil counts; all-cause hospitalisation with monocyte count, neutrophil count and haemoglobin level; and respiratory-related hospitalisation with monocyte count, neutrophil count and haemoglobin level. In multivariate analyses, C-statistics were highest for the cGAP2 model for each of the outcomes.
Modified GAP models incorporating monocyte counts alone or plus other white and red blood cell variables may be useful to improve prediction of outcomes in patients with idiopathic pulmonary fibrosis.
性别、年龄与生理学（GAP）模型是一种用于特发性肺纤维化患者的简单死亡率预测工具，它使用初始评估时可得的人口统计学和生理学变量。白细胞变量可能与特发性肺纤维化的预后相关。我们评估了在改良GAP（cGAP）模型中纳入血细胞计数是否能改善特发性肺纤维化患者的预后预测。
这项回顾性分析纳入了吡非尼酮治疗特发性肺纤维化的3期随机试验（ASCEND、CAPACITY 004、CAPACITY 006）的汇总数据。在最初1年期间评估研究结局（疾病进展、全因死亡率、全因住院、呼吸相关住院）。使用共享脆弱模型在双变量背景下评估连续和分类的基线白细胞和红细胞参数与研究结局之间的关联，并基于C统计量变化在多变量背景下评估将连续单核细胞计数（cGAP1）或白细胞和红细胞参数（cGAP2）添加到传统GAP变量中的影响。
数据来自1247例患者（吡非尼酮组，n = 623；安慰剂组，n = 624）。显著关联（双变量分析）为特发性肺纤维化进展与中性粒细胞和嗜酸性粒细胞计数；全因死亡率与单核细胞和中性粒细胞计数；全因住院与单核细胞计数、中性粒细胞计数和血红蛋白水平；以及呼吸相关住院与单核细胞计数、中性粒细胞计数和血红蛋白水平。在多变量分析中，每个结局的cGAP2模型的C统计量最高。
单独纳入单核细胞计数或加上其他白细胞和红细胞变量的改良GAP模型可能有助于改善特发性肺纤维化患者结局的预测。
