高荧光细胞联合肿瘤标志物在恶性胸腔积液诊断中的应用。

IF 2.5 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinical biochemistry Pub Date : 2025-01-21 DOI:10.1016/j.clinbiochem.2025.110888

Elisa Nuez-Zaragoza , Indira Bhambi-Blanco , Mònica Vidal-Pla , Isabel Aparicio-Calvente , M. Rosa Escoda-Giralt , Joana Gallardo-Campos , Joan C. Ferreres , Luis Frisancho , Laia Mas-Maresma , Patricia Aguilera-Fernández , Sonia Marco-Continente , Marina Sierra-Boada , Pablo Andreu-Cobo , Miquel Gallego , Jaume Trapé , Vicente Aguadero

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引用次数: 0

摘要

背景：新的诊断工具已经出现，以辅助恶性胸腔积液（MPE）的传统诊断，如高荧光细胞（HFc）和肿瘤标志物（TMs），由临床实验室自动胸腔积液检查确定。本研究旨在评价HFc联合TMs对MPE的诊断能力。方法：我们招募了Parc Taulí大学医院住院的胸腔积液患者。我们在临床实验室收集并分析了胸膜液和血清样本，并将胸膜液样本送到病理科做细胞学检查。我们使用Sysmex XN-10检测胸水细胞计数，并使用ECLIA Cobas e801 Roche检测胸水和血清样本中的TMs （CEA, CA19.9和CA15.3）。我们根据细胞学阳性和/或胸膜活检阳性来确定最终的MPE诊断。我们根据这些最终诊断对患者进行分类，并进行变量之间的比较，以及多变量逻辑回归。结果：该研究纳入了221例患者的316例胸腔积液。多因素logistic回归分析显示，血清CA15.3、CEA比值和HFc是MPE最显著的预测变量。我们计算了两种不同的模型：一种不包括HFc，另一种包括HFc，后者显示出更好的诊断能力（曲线下面积0.91）。该模型可以识别100% %的MPE病例，在低截止值下特异性为30%，更高的值可以帮助识别60% %的MPE病例，特异性为100% %。结论：根据我们的研究结果，该模型具有较高的诊断性能，可以作为一种快速、自动化、可靠、无创的MPE检测工具。

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Utility of the combination of high fluorescence cells and tumor markers for the diagnosis of malignant pleural effusions

Background

New diagnostic tools have emerged to assist the traditional diagnosis of malignant pleural effusion (MPE), such as high fluorescence cells (HFc) and tumor markers (TMs), determined by clinical laboratory automated pleural fluid workup. This study aimed to evaluate the diagnostic ability of the combination of HFc and TMs for diagnosing MPE.

Methods

We recruited hospitalized patients with pleural effusion at Parc Taulí University Hospital. We collected and analyzed pleural fluid and serum samples in the clinical laboratory, and we sent a sample of pleural fluid to the Pathology Department for cytology workup. We determined the pleural fluid cell count by Sysmex XN-10 and assessed TMs (CEA, CA19.9, and CA15.3) using the ECLIA Cobas e801 Roche in both pleural fluid and serum samples. We established the final MPE diagnosis based on positive cytology and/or positive pleural biopsy. We classified patients based on these final diagnoses and conducted a comparison between variables, along with multivariate logistic regression.

Results

The study included 316 pleural effusions from 221 patients recruited. Multivariate logistic regression indicated the most significant predictor variables for MPE were CA15.3 in serum, CEA ratio, and HFc. We calculated two different models: one excluding HFc and one including it, with the latter displaying superior diagnostic ability (area under the curve 0.91). This model could identify 100 % of MPE cases with 30 % specificity at low cut-offs, and higher values could help identify 60 % of MPE cases with 100 % specificity.

Conclusions

Per our findings, this model has high diagnostic performance and could serve as a swift, automated, dependable, non-invasive tool for MPE detection.

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来源期刊

Clinical biochemistry 医学-医学实验技术

CiteScore

5.10

自引率

0.00%

发文量

151

审稿时长

25 days

期刊介绍： Clinical Biochemistry publishes articles relating to clinical chemistry, molecular biology and genetics, therapeutic drug monitoring and toxicology, laboratory immunology and laboratory medicine in general, with the focus on analytical and clinical investigation of laboratory tests in humans used for diagnosis, prognosis, treatment and therapy, and monitoring of disease.