Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada.
Division of Respiratory Medicine, Department of Medicine, The UBC Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, BC, Canada; Institute for Heart and Lung Health, Division of Respiratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Chest. 2020 Mar;157(3):547-557. doi: 10.1016/j.chest.2019.09.003. Epub 2019 Sep 19.
Prediction of future lung function will enable the identification of individuals at high risk of developing COPD, but the trajectory of lung function decline varies greatly among individuals. This study involved the development and validation of an individualized prediction model of lung function trajectory and risk of airflow limitation in the general population.
Data were obtained from the Framingham Offspring Cohort, which included 4,167 participants ≥ 20 years of age and who had ≥ 2 valid spirometry assessments. The primary outcome was prebronchodilator FEV; the secondary outcome was the risk of airflow limitation (defined as FEV/FVC less than the lower limit of normal). Mixed effects regression models were developed for individualized prediction, and a machine learning algorithm was used to determine essential predictors. The model was validated in two large, independent multicenter cohorts (N = 2,075 and 12,913, respectively).
With 20 common predictors, the model explained 79% of the variation in FEV decline in the derivation cohort. In two validation datasets, the model had low error in predicting FEV decline (root mean square error range, 0.18-0.22 L) and high discriminative power in predicting risk of airflow limitation (C-statistic range, 0.86-0.87). This model was implemented in a freely accessible website-based application, which allows prediction based on flexible sets of predictors (http://resp.core.ubc.ca/ipress/FraminghamFEV1).
The individualized predictor is an accurate tool to predict long-term lung function trajectories and risk of airflow limitation in the general population. This model enables identifying individuals at higher risk of COPD, who can then be targeted for preventive therapies.
预测未来的肺功能将能够识别出患有 COPD 风险较高的个体，但个体之间的肺功能下降轨迹差异很大。本研究旨在开发和验证一种用于预测一般人群肺功能轨迹和气流受限风险的个体化预测模型。
数据来自弗雷明汉后代队列，该队列包括 4167 名年龄≥20 岁且至少有 2 次有效肺活量测定的参与者。主要结局指标为支气管扩张剂前 FEV1；次要结局指标为气流受限风险（定义为 FEV1/FVC 低于正常值下限）。采用混合效应回归模型进行个体化预测，并使用机器学习算法确定基本预测因子。该模型在两个大型独立多中心队列（分别为 2075 名和 12913 名）中进行了验证。
在包含 20 个常见预测因子的模型中，该模型可以解释推导队列中 FEV1 下降变异的 79%。在两个验证数据集中，该模型在预测 FEV1 下降方面误差较小（均方根误差范围为 0.18-0.22 L），在预测气流受限风险方面具有较高的判别能力（C 统计量范围为 0.86-0.87）。该模型已实现于一个可免费访问的基于网站的应用程序中，该程序允许根据灵活的预测因子集进行预测（http://resp.core.ubc.ca/ipress/FraminghamFEV1）。
个体化预测因子是一种准确的工具，可用于预测一般人群的长期肺功能轨迹和气流受限风险。该模型能够识别出患有 COPD 风险较高的个体，从而针对这些个体进行预防性治疗。
