Machine Learning and Prediction of All-Cause Mortality in COPD
Machine Learning and Prediction of All-Cause Mortality in COPD
复制标题
DOI：
10.1016/j.chest.2020.02.079
复制
发表时间：
2020-09-01
期刊：
影响因子：
9.6
通讯作者：
Cho, Michael H.
中科院分区：
文献类型：
--
作者：
Moll, Matthew;Qiao, Dandi;Cho, Michael H.
背景技术背景：COPD是死亡率的主要原因。研究问题：我们假设将机器学习应用于临床和定量CT成像特征将改善COPD的死亡率预测。研究设计和方法：我们选择了30个临床、肺功能和成像特征作为随机生存森林的输入。我们在考克斯回归中使用了顶级特征来创建COPD模型中的机器学习死亡率预测（MLMP），并评估了其他统计和机器学习模型的性能。我们在COPD遗传流行病学（COPDGene）的一部分受试者中训练了中度至重度COPD受试者的模型，并在COPDGene和COPD纵向评估以确定预测替代终点（ECLIPSE）中测试了其余中度至重度COPD受试者的预测性能。我们比较了我们的模型与BMI，气流阻塞，呼吸困难，运动能力（BODE）指数; BODE修改;和年龄，呼吸困难，气流阻塞指数。结果：我们包括2,632名参与者从COPDGene和1,268名参与者从ECLIPSE。死亡率的最高预测因子是6分钟步行距离、FEV1%预测值和年龄。最重要的影像学预测因子是肺动脉-主动脉比值。MLMP-COPD模型在COPDGene和ECLIPSE中的C指数均为0.7（中位随访时间分别为6.4年和7.2年），显著优于所有测试的死亡率指数（P <0.05）。MLMP-COPD模型的预测因子较少，但与其他模型的预测因子相似。具有最高BODE评分（7-10）的组具有64%的死亡率，而由MLMP-COPD模型定义的最高死亡率组具有77%的死亡率（P 1/4.012）。解释：MLMP-COPD模型在预测两个COPD群组的全因死亡率方面优于四个现有模型。机器学习的性能与传统统计方法相似。该模型可在网上获得：https：//cdnm。shinyapps.io/cgmortalityapp/.
BACKGROUND: COPD is a leading cause of mortality.RESEARCH QUESTION: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD.STUDY DESIGN AND METHODS: We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index.RESULTS: We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index $ 0.7 in both COPDGene and ECLIPSE (6.4and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P 1/4 .012).INTERPRETATION: An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm. shinyapps.io/cgmortalityapp/.