基于机器学习的预测,在重症COVID-19患者的住院死亡率使用血液学标记器
Rongrong Dong1, Han Yao1, Taoran Chen1
1Department of Laboratory Medicine, First Hospital of Jilin University, Changchun 130021, Jilin, China.
概括
使用常规血液检测的新模型可以预测严重的COVID-19患者的死亡风险. 这种工具有助于早期风险评估和患者管理,以获得更好的结果.
科学领域:
- 血液学 血液学 血液学
- 关键护理医学 关键护理医学
- 机器学习在医学中的应用
背景情况:
- 严重的COVID-19与高死亡率有关,需要早期风险分层.
- 在入院时确定预后因素对于及时干预和管理至关重要.
- 血液学参数提供了一个易于获得的资源,用于预测患者的结果.
研究的目的:
- 开发和验证严重COVID-19患者死亡风险的预测模型.
- 用在入院时获得的血液学参数进行风险评估.
- 为了确定重症COVID-19中死亡率的关键血液学预测因素.
主要方法:
- 从396名 (队列1) 和112名 (队列2) 严重的COVID-19患者的临床和实验室数据的回顾性收集.
- 使用LASSO方法进行特征选择和使用9个机器学习算法进行模型开发,选择性能最好的物流回归 (LR) 模型.
- 在第2组中对开发的模型进行外部验证,包括名录图可视化和子组分析.
主要成果:
- 该LR模型显示出强大的预测性能,在第1队列中AUC为0.852.
- 确定的关键预测因素包括D-二聚体,血小板,中性粒细胞计数,淋巴细胞计数和激活的部分血栓形成时间.
- 该模型在第2组中显示出良好的验证 (AUC = 0.841) 并在不同年龄组中表现良好.
结论:
- 通过仅使用血液学参数,成功开发了一种有效的COVID-19严重病例死亡风险预测模型.
- 该模型是用LR算法构建的,可以及时准确地分层风险,并对重症COVID-19患者进行管理.
- 这种基于血液学的模型为改善严重COVID-19病例的临床决策提供了实用工具.
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