在六次大流行浪潮中,严重COVID-19患者的死亡率的预测模型
Nazaret Casillas1,2, Antonio Ramón3, Ana María Torres2,4
1Department of Internal Medicine, Hospital Virgen De La Luz, 16002 Cuenca, Spain.
Viruses
|November 25, 2023
概括
机器学习准确预测严重的COVID-19患者的死亡率. 关键预测因素包括费里丁,纤维素,D-二次体和C-反应蛋白,指导关键护理决策.
科学领域:
- 关键护理医学 关键护理医学
- 传染性疾病 传染性疾病
- 生物统计学和机器学习
背景情况:
- 严重的COVID-19继续构成全球健康挑战,导致重症监护室 (ICU) 的入院.
- 尽管取得了进展,但在重症COVID-19患者中确定死亡率预测因素仍然至关重要.
研究的目的:
- 通过机器学习识别使用严重的COVID-19患者死亡率的最有影响力的预测因素.
- 评估各种机器学习模型在预测重症COVID-19患者预后方面的有效性.
主要方法:
- 对684名严重的COVID-19患者 (2020年6月 - 2023年3月) 的回顾性多中心研究.
- 利用电子健康记录数据,包括社会人口统计学,临床和实验室参数.
- 对比了六种监督机器学习方法,重点是极端梯度增强 (XGB).
主要成果:
- 极端梯度提升 (XGB) 模型在预测死亡率方面实现了最高的平衡精度 (96.61%).
- 确定的关键死亡预测因子包括费里丁,纤维素,D-二次体,血小板计数,C反应蛋白 (CRP),前列血时间 (PT),侵入性机械通风 (IMV),PaO2 / FiO2比率 (PaFi),乳酸脱酶 (LDH),淋巴细胞计数,激活部分血栓形成时间 (aPTT),体重指数 (BMI),肌素和年龄.
- 在分类患者结局方面,XGB表现强.
结论:
- 机器学习,特别是XGB,是预测严重COVID-19患者死亡率的强大工具.
- 生物标志物 (费里丁,纤维素,D-二次体,CRP) 和临床因素 (IMV,PaFi,年龄,BMI) 是预后的关键指标.
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