一个机器学习模型探索慢性药物治疗和COVID-19临床结果之间的关系
Berta Miró1, Natalia Díaz González1, Juan-Francisco Martínez-Cerdá2
1Statistics and Bioinformatics Unit, Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain.
慢性药物使用影响COVID-19的结果. 某些药物,如ACE抑制剂和甲胺,可能会提供保护,而其他药物,如DPP-4抑制剂,则与较高的死亡风险有关.
科学领域:
- 临床医学 临床医学
- 药理学 药理学是指药理学的学科.
- 数据科学数据科学数据科学
背景情况:
- 慢性药物对COVID-19预后的影响仍在争论中.
- 了解这些关联对于患者护理和治疗优化至关重要.
研究的目的:
- 调查慢性药物使用与COVID-19结果之间的关系.
- 使用机器学习识别影响COVID-19严重性的关键药物相关因素.
主要方法:
- 在加泰罗尼亚对137,835名COVID-19患者的分析 (2020年2月至9月).
- 利用极端梯度提升来预测住院,ICU入院和死亡率.
- 补充了对特定疾病 (糖尿病,高血压,脂质疾病) 的后勤回归和灵敏度分析.
主要成果:
- 机器学习模型准确预测了COVID-19死亡风险 (AUCROC为18-65岁年龄组的0.89).
- 关键预测因素包括药物数量,全身皮质类药物,HMG-CoA减少酶抑制剂和高血压药物.
- ангиотензин转化酶 (ACE) 抑制剂或 ангиотензинII受体阻断剂 (ARBs) 与65岁以上高血压患者的死亡率降低有关.
结论:
- 机器学习有效地根据患者数据识别了COVID-19的结果.
- 接受ACE抑制剂,ARB或比瓜尼德的患者应继续治疗,因为可能会产生保护作用.
- 特定的药物,如甲福明,显示出保护性益处,而DPP-4抑制剂与某些年龄组的死亡率增加有关.
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