Division of Biostatistics and Bioinformatics, Environmental & Public Health Sciences, University of Cincinnati College of Medicine, CARE/Crawley Building, Suite E-870 3230, Eden Ave, Cincinnati, OH 45267, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA.
Division of Biostatistics and Bioinformatics, Environmental & Public Health Sciences, University of Cincinnati College of Medicine, CARE/Crawley Building, Suite E-870 3230, Eden Ave, Cincinnati, OH 45267, USA.
Sci Total Environ. 2024 Nov 10;950:175348. doi: 10.1016/j.scitotenv.2024.175348. Epub 2024 Aug 6.
Environmental exposures and community characteristics have been linked to accelerated lung function decline in people with cystic fibrosis (CF), but geomarkers, the measurements of these exposures, have not been comprehensively evaluated in a single study. To determine which geomarkers have the greatest predictive potential for lung function decline and pulmonary exacerbation (PEx), a retrospective longitudinal cohort study was performed using novel Bayesian joint covariate selection methods, which were compared with respect to PEx predictive accuracy. Non-stationary Gaussian linear mixed effects models were fitted to data from 151 CF patients aged 6-20 receiving care at a CF Center in the midwestern US (2007-2017). The outcome was forced expiratory volume in 1 s of percent predicted (FEV1pp). Target functions were used to predict PEx from established criteria. Covariates included 11 routinely collected clinical/demographic characteristics and 45 geomarkers comprising 8 categories. Unique covariate selections via four Bayesian penalized regression models (elastic-net, adaptive lasso, ridge, and lasso) were evaluated at both 95 % and 90 % credible intervals (CIs). Resultant models included one to 6 geomarkers (air temperature, percentage of tertiary roads outside urban areas, percentage of impervious nonroad outside urban areas, fine atmospheric particulate matter, fraction achieving high school graduation, and motor vehicle theft) representing weather, impervious descriptor, air pollution, socioeconomic status, and crime categories. Adaptive lasso had the lowest information criteria. For PEx predictive accuracy, covariate selection from the 95 % CI elastic-net had the highest area under the receiver-operating characteristic curve (mean ± standard deviation; 0.780 ± 0.026) along with the 95 % CI ridge and lasso methods (0.780 ± 0.027). The 95 % CI elastic-net had the highest sensitivity (0.773 ± 0.083) while the 95 % CI adaptive lasso had the highest specificity (0.691 ± 0.087), suggesting the need for different geomarker sets depending on monitoring goals. Surveillance of certain geomarkers embedded in prediction algorithms can be used in real-time warning systems for PEx onset.
环境暴露和社区特征与囊性纤维化 (CF) 患者的肺功能下降加速有关，但在单一研究中尚未全面评估地理标记物，即这些暴露的测量值。为了确定哪些地理标记物对肺功能下降和肺部恶化 (PEx) 具有最大的预测潜力，使用新的贝叶斯联合协变量选择方法进行了回顾性纵向队列研究，这些方法在预测 PEx 的准确性方面进行了比较。对在美国中西部 CF 中心接受治疗的 151 名 6-20 岁 CF 患者（2007-2017 年）的数据进行了非平稳高斯线性混合效应模型拟合。结果为预计用力呼气量的 1 秒百分比 (FEV1pp)。目标功能用于根据既定标准预测 PEx。协变量包括 11 个常规收集的临床/人口统计学特征和 45 个地理标记物，共 8 个类别。通过四个贝叶斯惩罚回归模型（弹性网、自适应lasso、岭和lasso）在 95%和 90%置信区间 (CI) 进行了独特的协变量选择。最终模型包括一个到 6 个地理标记物（城市外三级道路的百分比、城市外不透水非道路的百分比、细大气颗粒物、高中毕业率和机动车盗窃），代表天气、不透水描述符、空气污染、社会经济地位和犯罪类别。自适应lasso 的信息准则最低。对于 PEx 的预测准确性，95%CI 弹性网的协变量选择具有最高的接收器操作特征曲线下面积（均值 ± 标准差；0.780 ± 0.026），以及 95%CI 岭和lasso 方法（0.780 ± 0.027）。95%CI 弹性网的敏感性最高（0.773 ± 0.083），而 95%CI 自适应lasso 的特异性最高（0.691 ± 0.087），这表明根据监测目标需要不同的地理标记物集。预测算法中嵌入的某些地理标记物的监测可用于 PEx 发作的实时预警系统。
