使用全面的患者分析算法评估病进展:一种混合集群方法
Mohammad A Al-Mamun1, Ki Jin Jeun1, Todd Brothers2
1Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, West Virginia, United States of America.
PloS one
|July 11, 2025
概括
这项研究开发了一种算法,以识别急性损伤 (AKI) 后患有慢性病 (CKD) 风险的患者. 该算法有助于确定特定患者的个人资料,以制定有针对性的预防策略.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 数据科学数据科学数据科学
- 临床信息学 临床信息学
背景情况:
- 急性损伤 (AKI) 显著增加患慢性病 (CKD) 的风险,但许多幸存者缺乏足够的后续护理.
- 识别患有AKI后CKD高风险的患者对于及时干预和预防至关重要.
研究的目的:
- 开发和验证一个患者分析算法,以识别与AKI进展到CKD相关的临床表型.
- 分析患有医院获得的AKI (HA-AKI) 和社区获得的AKI (CA-AKI) 的患者的风险因素和并发症模式.
主要方法:
- 2010-2022年电子健康记录的回顾性分析.
- 将AKI分为HA-AKI,CA-AKI和No-AKI组的分类.
- 开发一种定制算法,结合基于网络的社区和可变集群方法来识别患者个人资料和风险因素.
主要成果:
- 在58,876名CKD患者中,10.2%患有HA-AKI,11.5%患有CA-AKI.
- 在AKI组中,常见的危险因素包括长期使用阿片类药物,阿特莱克塔斯,缺血性心脏病和乳酸性酸性.
- HA-AKI队列表现出更复杂的并发症网络,高胆固醇和慢性疼痛等疾病显示出更高的网络中心性.
结论:
- 开发的患者分析算法有效地识别了与CKD进展相关的AKI表型.
- 这种方法有助于早期识别CKD风险因素,从而实现有针对性的预防,并可能降低医疗保健成本.
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