与越南中风患者90天死亡率相关的因素:与可解释的机器学习相比,前性发现,多中心研究
Ton Duy Mai1,2,3, Dung Tien Nguyen1,2, Cuong Chi Tran4
1Faculty of Stroke and Cerebrovascular Disease, University of Medicine and Pharmacy, Vietnam National University, Ho Chi Minh City, Vietnam.
PloS one
|September 20, 2024
概括
在越南,中风后的死亡率很高,脑内出血患者面临更高的风险. 机器学习准确地预测了90天死亡率,将年龄和中风严重程度作为关键因素.
科学领域:
- 神经学 神经学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 关于越南中风死亡率预测因素的数据有限.
- 了解这些因素对于改善患者治疗结果至关重要.
研究的目的:
- 确定患有缺血性中风和脑内出血的越南患者90天死亡率的患病率和预测因素.
- 评估机器学习在预测中风死亡率方面的有效性.
主要方法:
- 对10家越南中风中心的2209名患者进行前性研究.
- 分析包括临床特征和机器学习模型 (极端梯度增强,夏普利添加式扩展).
- 评估了90天死亡率和相关的风险因素.
主要成果:
- 整体90天死亡率为缺血性中风的8.2%,脑内出血的20.5%.
- 心房动增加了缺血性中风的死亡风险 (OR 3.09).
- 美国国立卫生研究院中风量表 (NIHSS) 评分是这两种中风类型的重要预测指标;机器学习模型达到0.91准确度.
结论:
- 脑卒中严重程度 (NIHSS) 和年龄是90天死亡率的关键预测指标.
- 机器学习在临床实践中为预测中风结果提供了一个强大的工具.
- 需要进一步的研究来完善预测模型,并为越南的有针对性的干预提供信息.
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