范科米辛治疗期间血清肌素异常的临床风险评估:使用机器学习模型进行的一项回顾性研究
Yilei Yang1, Haiying Yan1, Xiangyue Wang2
1Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China.
International journal of clinical pharmacy
|August 19, 2025
概括
这项研究确定了风险因子,诱导菌素损伤,开发一个机器学习模型,预测血清肌氨酸异常在住院患者. 梯度增强机模型为早期风险评估和改善患者安全提供了一个实用的工具.
科学领域:
- 药理学和病学 药理学和病学
- 临床信息学和机器学习
背景情况:
- 范科米辛对于格拉姆阳性感染至关重要,但具有毒性风险,血清肌素异常和潜在的急性损伤 (AKI) 表明了这一点.
- 早期识别患有万科米辛诱导毒性风险的患者对于及时干预和更好的患者结果至关重要.
研究的目的:
- 为了确定与万科米辛相关的血清肌氨酸异常的临床风险因素.
- 开发和验证预测模型,用于识别在万科米辛治疗期间住院的高风险患者.
主要方法:
- 进行了一项回顾性队列研究,对1008名接受香草素治疗的患者进行了研究.
- 血清肌素异常被定义为≥26.5μmol/L或≥50%的比基线的增加.
- 训练和比较了五个机器学习算法,使用AUC,精度,灵敏度和特异性来评估性能.
主要成果:
- 血清肌氨酸异常的发生率为9.22%.
- 重要的危险因素包括慢性病,呼吸衰竭,胰腺炎,肺炎和机械通风.
- 梯度提升机 (GBM) 算法显示出最高的预测性能 (AUC 0.783).
结论:
- 旺科米辛治疗期间血清肌氨酸异常是一个重要的临床问题,特别是在重症患者或患有并发症的患者中.
- 基于机器学习的预测模型为个性化风险评估和主动管理提供了一个实用的工具.
- 将这种模型集成到临床工作流程中可以提高患者的安全性,并优化万科米辛的使用.
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