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特发性肺纤维化的统一基线和纵向死亡率预测。

Unified baseline and longitudinal mortality prediction in idiopathic pulmonary fibrosis.

机构信息

Dept of Medicine, University of California San Francisco, San Francisco, CA, USA

InterMune Inc., Brisbane, CA, USA.

出版信息

Eur Respir J. 2015 May;45(5):1374-81. doi: 10.1183/09031936.00146314. Epub 2015 Jan 22.

DOI:10.1183/09031936.00146314
PMID:25614172
Abstract

The Gender-Age-Physiology (GAP) model is a validated, baseline-risk prediction model for mortality in idiopathic pulmonary fibrosis. Longitudinal variables have been shown to contribute to risk prediction in idiopathic pulmonary fibrosis and may improve the predictive performance of the baseline GAP model. Our aims were to further validate the GAP model and evaluate whether the addition of longitudinal variables improves its predictive performance. The study population was derived from a large clinical trials cohort of patients with idiopathic pulmonary fibrosis (n=1109). Model performance was determined by improvement in the C-statistic, net reclassification improvement, clinical net reclassification improvement, and a goodness-of-fit test. The GAP model had good discriminative performance with a C-statistic of 0.757 (95% CI 0.750-0.764). However, the original GAP model tended to overestimate risk in this cohort. A novel, easy to use model, consisting of the original GAP predictors plus history of respiratory hospitalisation and 24-week change in forced vital capacity (the longitudinal GAP model) improved model performance with a C-statistic of 0.785 (95% CI 0.780-0.790), net reclassification improvement of 8.5%, clinical net reclassification improvement of 25%, and a goodness-of-fit test of 0.929. The Longitudinal GAP model, along with the original GAP model, may unify baseline and longitudinal mortality risk prediction in idiopathic pulmonary fibrosis.

摘要

性别-年龄-生理(GAP)模型是一种经过验证的、用于预测特发性肺纤维化患者死亡率的基线风险预测模型。已有研究表明,纵向变量可用于预测特发性肺纤维化患者的风险,并且可能提高基线 GAP 模型的预测性能。我们的目的是进一步验证 GAP 模型,并评估是否增加纵向变量可以改善其预测性能。该研究人群来自一项大型特发性肺纤维化临床试验队列(n=1109)。通过 C 统计量、净重新分类改善、临床净重新分类改善和拟合优度检验来确定模型性能。GAP 模型具有良好的区分性能,C 统计量为 0.757(95%CI 0.750-0.764)。然而,原始 GAP 模型在该队列中存在高估风险的趋势。一个新的、易于使用的模型,由原始 GAP 预测因素加上呼吸住院史和 24 周用力肺活量变化(纵向 GAP 模型)组成,改善了模型性能,C 统计量为 0.785(95%CI 0.780-0.790),净重新分类改善 8.5%,临床净重新分类改善 25%,拟合优度检验为 0.929。纵向 GAP 模型与原始 GAP 模型一起,可能会统一特发性肺纤维化的基线和纵向死亡率风险预测。

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