结核病治疗结果的危险因素:基于统计学习的探索,使用SINAN数据库进行不完整的观察
Nguyen Ky Phat1, Yoonah Lee2, Dinh Hoa Vu3
1Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea.
BMC medical informatics and decision making
|August 12, 2025
概括
多重归算分析有效预测了结核病治疗结果,并确定了致死风险因素,包括结核病的临床形式和治疗方案中缺少异化或利芬素.
科学领域:
- 医学研究 医学研究
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 了解治疗结果的早期预测因素对于有效的结核病 (TB) 管理和资源分配至关重要.
- 预测结核病治疗的成功需要解决诸如不完整的健康记录和数据不平衡等挑战.
研究的目的:
- 使用现实世界健康数据预测结核病治疗结果.
- 确定结核病治疗期间与死亡率相关的风险因素.
- 评估多重归算分析 (MIA) 在处理缺失数据以预测结果的有效性.
主要方法:
- 使用上加权和多重归算分析 (MIA) 来管理缺失的数据和响应不平衡.
- 利用后勤回归 (LOGIT),随机森林和随机梯度提升来预测结果.
- 进行LOGIT模型的解释,将MIA与完整案例分析 (CCA) 进行比较.
主要成果:
- 多重归算分析 (MIA) 证明有效处理结核病患者记录中缺少的数据.
- 来自MIA的LOGIT模型发现了与结核病治疗结果相关的统计学上更显著的共变量,而不是CCA.
- 增加死亡几率的因素包括结核病的临床形式 (肺和肺外结核病),放弃后的再治疗,以及治疗方案中缺少异化或利芬素.
结论:
- MIA是解决结核病治疗结果预测中缺少数据的合适方法.
- 确定了结核病治疗期间死亡的关键风险因素,为临床实践提供了信息.
- 建议使用可解释的LOGIT模型来预测结核病治疗结果.
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