一种基于机器学习的预测模型,用于使用淋巴细胞计数预测败血症的不良预后:一个全国性的,多中心的潜在队列
Siang Huang1, Luyao Liu1, Chaoyang Wang1
1Department of Critical Care Medicine, The First Hospital of China Medical University, China Medical University, 155 Nanjing North Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
使用动态淋巴细胞计数识别败血症患者的持续性淋巴缺血症可以预测不良结果. 这种方法有助于识别有针对性的免疫治疗的高风险个体,改善败血症管理.
科学领域:
- 免疫学 免疫学 免疫学
- 关键护理医学 关键护理医学
- 数据科学数据科学数据科学
背景情况:
- 败血症诱导的免疫抑制与患者不良结果有关.
- 循环淋巴细胞计数 (LC) 是感染性败血症免疫状况的随时可用的标志物.
研究的目的:
- 用动态淋巴细胞计数 (LC) 轨迹来表征败血症患者的免疫表型.
- 为了使高风险败血症个体的早期识别.
主要方法:
- 隐性类轨迹建模 (LCTM) 分析了从毒症诊断后7天内重复测量的动态LC模式.
- 使用了卡普兰-梅尔曲线,考克斯回归,博鲁塔算法和机器学习模型 (包括可解释性的SHAP).
- 外部验证是在一个单独的败血症患者队列上进行的.
主要成果:
- 确定了四种不同的LC动态轨迹.
- 持久性淋巴缺血 (PL) 亚组显示疾病严重程度最高,预后最差.
- 开发的机器学习模型准确预测了PL亚表型,改善了ICU死亡率预测.
结论:
- 动态LC轨迹揭示了败血症中的免疫异质性.
- 早期发现持续性淋巴缺血症 (PL) 对于免疫治疗患者的分层至关重要.
- 这种方法有助于更好地针对败血症患者的干预措施.
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