使用人工智能在全血细胞计数和随后不必要的红细胞输血中识别IV液体污染
Carly Maucione1, Nathan McLamb1, Mark A Zaydman1
1Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA.
Transfusion
|January 8, 2026
概括
机器学习模型现在可以在全血细胞计数 (CBCs) 中识别静脉液体污染,提高实验室的准确性. 这有助于防止不必要的输血,并提高患者的安全.
科学领域:
- 临床病理学 临床病理学
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 血液样本中的静脉注射 (IV) 液体污染会导致显著的全血细胞计 (CBC) 测量误差.
- 目前的方法缺乏用于追溯识别IV污染的黄金标准,阻碍了质量改进.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于大规模,追溯识别CBC结果中的IV液体污染.
- 评估这些ML模型在多个机构的现实应用性.
主要方法:
- ML模型被训练在模拟的IV液体污染使用血红蛋白,血小板和白细胞计数从CBCs.
- 模型与专家审查的数据集进行了验证,并对两所机构一年的真实世界CBC数据进行了测试.
主要成果:
- ML模型在区分受污染和非受污染的CBC结果方面表现出高准确性 (AUC 0.972和0.957).
- 预计约2%的住院CBC被污染,6-9%的输血可能是不必要的.
- 这些模型被证明有效地识别了大规模的IV液体污染.
结论:
- 机器学习提供了一种高效有效的方法来识别CBC中的IV液体污染.
- 需要进一步的研究,包括前性研究和实时检测,才能充分实现对患者安全和实验室操作的好处.
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