Respiratory Research Unit, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark.
Department of Pathology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
J Asthma. 2022 Mar;59(3):552-560. doi: 10.1080/02770903.2020.1868498. Epub 2021 Jan 8.
Cell differential count (CDC) of induced sputum is considered the gold standard for inflammatory phenotyping of asthma but is not implemented in routine care due to its heavy time- and staff demands. Digital Cell Morphology is a technique where digital images of cells are captured and presented preclassified as white blood cells (neutrophils, eosinophils, lymphocytes, macrophages, and unidentified) and nonwhite blood cells for review. With this study, we wanted to assess the accuracy of an automated CDC in identifying the key inflammatory cells in induced sputum.
Sputum from 50 patients with asthma was collected and processed using the standard processing protocol with one drop 20% albumin added to hinder cell smudging. Each slide was counted automatically using the CellaVision DM96 and manually by an experienced lab technician. Sputum was classified as eosinophilic or neutrophilic using 3% and 61% cutoffs, respectively.
We found a good agreement using intraclass correlation for all target cells, despite significant differences in the cell count rate. The automated CDC had a sensitivity of 65%, a specificity of 93%, and a kappa-coefficient of 0.61 for identification of sputum eosinophilia. In contrast, the automated CDC had a sensitivity of 29%, a specificity of 100%, and a kappa-coefficient of 0.23 for identification of sputum neutrophilia.
Automated- and manual cell counts of sputum agree with regards to the key inflammatory cells. The automated cell count had a modest sensitivity but a high specificity for the identification of both neutrophil and eosinophil asthma.
诱导痰细胞分类计数（CDC）被认为是哮喘炎症表型的金标准，但由于其时间和人员需求繁重，并未在常规护理中实施。数字细胞形态学是一种技术，其中捕获细胞的数字图像并呈现为预先分类的白细胞（中性粒细胞、嗜酸性粒细胞、淋巴细胞、巨噬细胞和未识别细胞）和非白细胞，以供审查。通过这项研究，我们希望评估自动 CDC 识别诱导痰中关键炎症细胞的准确性。
收集 50 例哮喘患者的痰液，并使用标准处理方案进行处理，添加一滴 20%白蛋白以阻止细胞模糊。使用 CellaVision DM96 自动计数每个载玻片，并由经验丰富的实验室技术员手动计数。使用 3%和 61%的截止值分别将痰液分类为嗜酸性粒细胞或中性粒细胞。
尽管细胞计数率存在显著差异，但使用组内相关系数评估所有目标细胞时，我们发现具有良好的一致性。自动 CDC 对痰液嗜酸性粒细胞的识别具有 65%的敏感性、93%的特异性和 0.61 的kappa 系数。相比之下，自动 CDC 对痰液中性粒细胞的识别具有 29%的敏感性、100%的特异性和 0.23 的kappa 系数。
自动和手动痰液细胞计数在关键炎症细胞方面具有一致性。自动细胞计数对识别中性粒细胞和嗜酸性粒细胞哮喘具有中等敏感性，但特异性较高。
