基于机器学习的凝血风险指数预测出血创伤患者的急性创伤性凝血病
Justin E Richards1, Shiming Yang, Rosemary A Kozar
1From the Department of Anesthesiology (J.E.R., S.Y., P.H.), Department of Surgery (S.Y., R.A.K., T.M.S., P.H.), Shock, Trauma, and Anesthesia Research (R.A.K.), University of Maryland School of Medicine (J.E.R., S.Y., R.A.K., T.M.S., P.H.), Program in Trauma (J.E.R., S.Y., R.A.K., T.M.S., P.H.), R Adams Cowley Shock Trauma Center, Baltimore, Maryland.
The journal of trauma and acute care surgery
|September 27, 2024
概括
一个新的凝血风险指数 (CRI) 使用机器学习来预测出血患者的急性创伤性凝血病 (ATC). 这种工具可以指导早期复苏,并改善严重出血风险的创伤患者的治疗结果.
科学领域:
- 创伤复苏的复苏治疗方法
- 医疗人工智能的人工智能
- 凝血障碍 凝血障碍 凝血障碍
背景情况:
- 急性创伤性凝血病 (ATC) 是受伤后的危急状况,增加了大量输血和死亡的风险.
- 早期识别ATC对于有效的出血性休克管理至关重要.
研究的目的:
- 开发和验证基于机器学习的凝血风险指数 (CRI),用于早期预测出血创伤患者的ATC.
- 评估CRI在识别有危险临界管理门和大量输血风险的患者中的准确性.
主要方法:
- 在入院后的头15分钟内,CRI是使用光聚血学和心电学波形,氧度和血压趋势的连续生命体征 (VS) 来开发的.
- 机器学习模型在2年的数据上进行了训练,并在一个创伤中心的2年的数据上进行了测试,其中包括17567名患者.
- 用国际标准化比率 (INR) >1.2和>1.5来定义ATC,并使用接收机操作员曲线下的面积来评估模型准确性.
主要成果:
- 在临界管理门患者中,CRI显示了ATC的优异预测准确性,INR>1.2和>1.5的真实阳性率分别为95.6%和94.9%,在临界管理门患者中.
- 对于接受大规模输血的患者,CRI显示了高的真实阳性率,分别为92.8%和100%的INR>1.2和>1.5.
- 该研究分析了17567名患者的数据,其中52.8%的患者遭受了伤,平均年龄为44.6岁.
结论:
- 凝血风险指数 (CRI) 能够有效地预测出血患者早期急性创伤性凝血病,使用连续生命体征和机器学习.
- 临床实施CRI可以指导及时的静血复苏策略.
- 将CRI扩展到医院前的环境可能使ATC的早期干预成为可能,这可能会改善患者的生存率和结果.
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