基于第一原则的自动化疟疾寄生虫诊断机的验证:在资源有限的环境中可接受的结果和治疗监测的先决条件

Iseimokumo Christopher Peletiri
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摘要

背景:在最近引进自动疟疾寄生虫诊断机之后;需要根据确定疟原虫密度的第一原则方案验证这些高技术机器,以获得可接受的结果和监测治疗,这一点怎么强调都不为过。这篇综述的目的是更新医学实验室科学家、医学实验室技术人员和研究人员关于使用吉姆萨染色的厚血膜和薄血膜诊断疟疾的第一原则,并建立他们如何验证任何自动化疟疾寄生虫诊断机的能力。方法:采用第一原则方案测定疟原虫密度。在直径为18mm的圆(厚膜)内散布8µL的血液,得到一个厚膜场的血容量(0.002µL);乘以一个系数(500)得到1µL。每100个厚膜场所见的寄生虫数量或每个厚膜场的平均数量乘以500即为每μ L血液中寄生虫的数量。结果:5 ~ 50只(1+)、50 ~ 500只(2+)、500 ~ 5000只(3+)和(4+)> 5000只/µL血液的疟原虫计数与自动检测机的结果相符,并将结果输入2 × 2表格,显示自动检测机的性能评价。结论:在获得若干结果后,任何自动化疟疾诊断机都可以验证其检测疾病的能力(敏感性、特异性、阳性和阴性预测值)。在寄生虫学实验室开始使用自动疟疾寄生虫诊断机不应导致在疟疾诊断中使用厚血膜和薄血膜的中断,因为它在资源有限的环境中仍然是金标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation of automated malaria parasite diagnostic machines based on first principle: A pre-requisite for acceptable results and treatment monitoring in resource limited settings
Background: Following the very recent introduction of automated malaria parasite diagnostic machines; the need to validate these high technology machines based on the first principle protocol in malaria parasite density determination for acceptable results and treatment monitoring cannot be over-emphasized. The aim of this review is to update Medical Laboratory Scientists, Medical Laboratory Technicians, and researchers alike on the first principle in the diagnosis of malaria using Giemsa stained thick and thin blood films and to build their capacity on how to validate any automated malaria parasite diagnostic machine. Methods: The first principle protocol in malaria parasite density determination was used. With 8 µL of blood spread within 18 mm diameter of circle (thick film), the volume of blood in one thick film field (0.002 µL) is obtained; which when multiplied by a factor (500) gives 1 µL. The number of parasites seen per 100 thick film fields or average number per each thick film field multiplied by 500 gives the number of parasites / µL of blood. Results: Malaria parasites counts of 5 – 50 parasites (1+), 50 – 500 parasites (2+), 500 – 5000 parasites (3+), and (4+) > 5000 parasites / µL of blood, and with the results obtained from the automated machine which when entered into a 2 x 2 table reveal the performance evaluation of automated machine. Conclusion: With several results obtained, any automated malaria diagnostic machine can be validated for its ability to detect disease (sensitivity, specificity, positive and negative predictive values). Commencement of the use of automated malaria parasites diagnostic machines in parasitology laboratory should not lead to discontinuity in the use of thick and thin blood films in malaria diagnosis as it remains the gold standard in resource limited settings.
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