干血斑质量评估的数学算法和来自新生儿查计划的质量结果
Oana R Oprea1, Albert Z Barabas2, Ion B Manescu1
1Department of Clinical Biochemistry and Immunology, "George Emil Palade" University of Medicine, Pharmacy, Science, and Technology, Targu Mures, Romania.
The journal of applied laboratory medicine
|February 22, 2024
概括
用于新生儿查的干血斑点 (DBS) 的自动扫描发现的缺陷比手工检查要多得多,突出了需要强有力的质量评估策略和人员持续培训的需要.
科学领域:
- 临床诊断 临床诊断 临床诊断
- 实验室自动化 实验室自动化
- 公共卫生查检查
背景情况:
- 干血斑 (DBS) 对于新生儿查,临床,流行病学和研究应用至关重要.
- 现有的指导方针涵盖了DBS的收集,储存和运输,但建议采用实验室特定的接受标准.
研究的目的:
- 开发和验证用于评估新生儿查中DBS质量的自动化光学扫描设备.
- 将DBS标本分类为正确或不正确,并确定缺陷类型.
主要方法:
- 使用一个具有验证算法的光学扫描设备,分析了来自11个产科病房的27,301个DBS标本 (2013-2018).
- 算法的性能与经验丰富的实验室人员的共识进行了基准测试.
- 错误的DBS标本根据四个缺陷类别进行分类.
主要成果:
- 自动扫描拒绝了26.96%的标本,与典型的~1%的拒绝率相比大幅增加.
- 多斑点 (状DBS) 是最常见的缺陷,发生在19.13%的标本中.
- 不恰当的样本率在产科病房之间有很大的差异,从5.70%到49.92%不等.
结论:
- 样本缺陷率和类型在很大程度上取决于机构,从特定的产科病房观察到一致的模式.
- 虽然自动扫描更敏感,但持续的员工培训,质量监测和对产科的反至关重要.
- 实施全面的质量评估策略,可能包括自动光学扫描,对于新生儿查实验室至关重要.
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