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Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.慢性阻塞性肺疾病基因(COPDGene®)研究中一秒用力呼气容积进展的机器学习预测
Chronic Obstr Pulm Dis. 2022 Jul 29;9(3):349-365. doi: 10.15326/jcopdf.2021.0275.
2
10-Year Follow-Up of Lung Function, Respiratory Symptoms, and Functional Capacity in the COPDGene Study.COPDGene 研究中肺功能、呼吸道症状和功能能力的 10 年随访结果。
Ann Am Thorac Soc. 2022 Mar;19(3):381-388. doi: 10.1513/AnnalsATS.202007-873OC.
3
A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study.COPDGene®研究中吸烟者死亡率的风险预测模型
Chronic Obstr Pulm Dis. 2020 Oct;7(4):346-361. doi: 10.15326/jcopdf.7.4.2020.0146.
4
FEV is a stronger mortality predictor than FVC in patients with moderate COPD and with an increased risk for cardiovascular disease.在患有中度 COPD 且心血管疾病风险增加的患者中,FEV 比 FVC 更能预测死亡率。
Int J Chron Obstruct Pulmon Dis. 2020 May 20;15:1135-1142. doi: 10.2147/COPD.S242809. eCollection 2020.
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Low FVC/TLC in Preserved Ratio Impaired Spirometry (PRISm) is associated with features of and progression to obstructive lung disease.在保留比肺活量/肺总量比值的肺功能障碍(PRISm)中,低 FVC/TLC 与阻塞性肺疾病的特征和进展有关。
Sci Rep. 2020 Mar 20;10(1):5169. doi: 10.1038/s41598-020-61932-0.
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BMI is associated with FEV decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials.BMI 与慢性阻塞性肺疾病的 FEV 下降相关:临床试验的荟萃分析。
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An Individualized Prediction Model for Long-term Lung Function Trajectory and Risk of COPD in the General Population.个体化预测模型用于预测一般人群的长期肺功能轨迹和 COPD 风险。
Chest. 2020 Mar;157(3):547-557. doi: 10.1016/j.chest.2019.09.003. Epub 2019 Sep 19.
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Discriminative Accuracy of FEV1:FVC Thresholds for COPD-Related Hospitalization and Mortality.FEV1:FVC 阈值对 COPD 相关住院和死亡的鉴别准确性。
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Chest wall strapping increases expiratory airflow and detectable airway segments in computer tomographic scans of normal and obstructed lungs.胸壁固定可增加正常和阻塞性肺部计算机断层扫描中的呼气气流和可检测的气道节段。
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Comparison of the 2017 and 2015 Global Initiative for Chronic Obstructive Lung Disease Reports. Impact on Grouping and Outcomes.2017 年和 2015 年全球慢性阻塞性肺疾病倡议报告比较。对分组和结果的影响。
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使用一秒用力呼气容积和一秒用力呼气容积与用力肺活量比值的联合模型预测当前吸烟者和既往吸烟者的慢性阻塞性肺疾病进展情况。

Predicting COPD Progression in Current and Former Smokers Using a Joint Model for Forced Expiratory Volume in 1 Second and Forced Expiratory Volume in 1 Second to Forced Vital Capacity Ratio.

作者信息

Strand Matthew, Khatiwada Aastha, Baraghoshi David, Lynch David, Silverman Edwin K, Bhatt Surya P, Austin Erin, Regan Elizabeth A, Boriek Aladin M, Crapo James D

机构信息

Division of Biostatistics, National Jewish Health, Denver, Colorado, United States.

Department of Radiology, National Jewish Health, Denver, Colorado, United States.

出版信息

Chronic Obstr Pulm Dis. 2022 Jul 29;9(3):439-453. doi: 10.15326/jcopdf.2022.0281.

DOI:10.15326/jcopdf.2022.0281
PMID:35905755
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9448007/
Abstract

Understanding baseline characteristics that can predict the progression of lung disease such as chronic obstructive pulmonary disease (COPD) for current or former smokers may allow for therapeutic intervention, particularly for individuals at high risk of rapid disease progression or transition from non-COPD to COPD. Classic diagnostic criteria for COPD and disease severity such as the Global Initiative for Chronic Obstructive Lung Disease document are based on forced expiratory volume in 1 second (FEV) and FEV to forced vital capacity (FVC) ratio. Modeling changes in these outcomes jointly is beneficial given that they are correlated, and they are both required for specific disease classifications. Here, linear mixed models were used to model changes in FEV and FEV/FVC jointly for 5- and 10-year intervals, using important baseline predictors to better understand the factors that affect disease progression. Participants with predicted loss of FEV and/or FEV/FVC of at least 5% tended to have more emphysema, higher functional residual capacity, higher airway wall thickness as measured by Pi10, lower FVC to total lung capacity ratio and a lower body mass index at baseline, all relative to overall cohort averages. The model developed can be used to predict progression for any potential COPD individual, based on demographic, symptom, computed tomography, and comorbidity variables.

摘要

了解当前或既往吸烟者中能够预测慢性阻塞性肺疾病(COPD)等肺部疾病进展的基线特征,可能有助于进行治疗干预,尤其是对于疾病快速进展或从非COPD转变为COPD风险较高的个体。COPD的经典诊断标准和疾病严重程度,如慢性阻塞性肺疾病全球倡议文件,是基于一秒用力呼气容积(FEV)和FEV与用力肺活量(FVC)的比值。鉴于这些结果是相关的,并且特定疾病分类都需要它们,因此联合对这些结果的变化进行建模是有益的。在这里,使用线性混合模型对5年和10年间隔内的FEV和FEV/FVC变化进行联合建模,使用重要的基线预测因素来更好地了解影响疾病进展的因素。与总体队列平均值相比,预测FEV和/或FEV/FVC至少下降5%的参与者在基线时往往有更多的肺气肿、更高的功能残气量、通过Pi10测量的更高的气道壁厚度、更低的FVC与肺总量比值以及更低的体重指数。所开发的模型可用于根据人口统计学、症状、计算机断层扫描和合并症变量预测任何潜在COPD个体的疾病进展。