环境暴露和社区特征的可靠识别,可以预测肺部疾病的快速进展
Clayton J Peterson1, Marepalli B Rao2, Anushka Palipana3
1Division 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.
The Science of the total environment
|August 8, 2024
概括
通过地质标志物测量环境暴露,可以预测囊性纤维化 (CF) 患者的肺功能下降和肺恶化 (PEx). 特定的地理标志物,如空气温度和污染水平,显示了PEx早期预警系统的预测潜力.
科学领域:
- 环境健康 环境健康
- 肺部病理学 肺部病理学
- 生物统计学 生物统计学
背景情况:
- 环境暴露和社区特征与囊性纤维化 (CF) 中的加速肺功能下降有关.
- 地质标志物,作为环境暴露的测量,尚未全面评估其在CF中的预测潜力.
- 肺部恶化 (PEx) 在患有CF的个体中显著影响肺功能和生活质量.
研究的目的:
- 在CF患者中确定肺功能下降的最大预测潜力的地质标志物.
- 评估CF中肺部恶化 (PEx) 的各种地质标志物的预测准确性.
- 为了比较新的贝叶斯联合共变量选择方法来识别关键预测地质标志物.
主要方法:
- 从2007年至2017年,对151名CF患者 (年龄在6至20岁) 进行了回顾性纵向队列研究.
- 非静止的高斯线性混合效应模型被用来分析预测的1秒百分比 (FEV1pp) 的强迫呼气体积.
- 四个贝叶斯惩罚回归模型 (弹性网,自适应拉索,,拉索) 用于在45个地理标志物和临床因素中进行共变选择.
主要成果:
- 模型确定了重要的地理标志物,包括空气温度,道路特性,颗粒物,教育水平和犯罪率.
- 贝叶斯惩罚回归模型确定了独特的地理标记集,其中适应性拉索显示了最低的信息标准.
- 95%可信区间弹性网模型证明了PEx的最高预测准确性 (AUC = 0.780 ± 0.026),而自适应拉索则提供了最高的特异性.
结论:
- 与天气,环境,空气污染,社会经济地位和犯罪有关的特定地质标志物可预测肺功能下降和CF中的PEx.
- 不同的地质标记集可能是最佳的,这取决于在PEx监测中是否优先考虑灵敏度或特异性.
- 将地理标记监测集成到预测算法中,可以促进对CF患者PEx发作的实时预警系统.
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