在腹腔透析中访问到访问的血压变化和临床结果 - 基于机器学习算法
Yan Lin1,2, Chunyan Yi1, Peiyi Cao1
1Department of Nephrology, The First Affiliated Hospital of Sun Yat-sen University and Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China.
概括
在早期的连续门诊腹膜透析 (CAPD) 期间,高的透析血压变化显著增加了主要不良心血管事件 (MACE) 和死亡率的风险. 剩余的功能可能会减轻这种关联.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
背景情况:
- 持续的门诊腹膜透析 (CAPD) 是一种关键的置换疗法.
- 了解CAPD患者长期结果的早期预测因素至关重要.
- 访问到访问的血压变化 (VVV) 是心血管健康的新兴风险因素.
研究的目的:
- 研究CAPD患者早期VVV与长期临床结果之间的关联.
- 为了确定特定的VVV参数,预测主要不良心血管事件 (MACE) 和死亡率.
- 探索机器学习在预测这些结果中的作用.
主要方法:
- 这是一项666名在2006年至2009年期间开始CAPD的患者的队列研究.
- 在治疗的前六个月评估VVV参数.
- 机器学习算法和竞争性风险回归被用于在15.8年的随访期间进行预测建模.
主要成果:
- 舒张血压的标准偏差 (SDDBP) 是一个显著的预测因素.
- 较高的SDDBP与99%的MACE风险增加有关.
- 较高的SDDBP与46%的死亡风险增加相关,更好的残留功能减轻了MACE风险.
结论:
- 在CAPD早期升高的SDDBP是长期MACE和死亡率增加的重要指标.
- 其余功能在SDDBP和心血管风险之间的关系中起到缓解作用.
- 机器学习有效地确定了SDDBP作为CAPD患者的关键预后标志物.
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