评估单核细胞分布宽度在儿科急诊护理

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-05-13 DOI:10.1016/j.cca.2025.120357

Luisa Agnello , Caterina Maria Gambino , Fabio Del Ben , Concetta Scazzone , Carmelinda Cavallaro , Claudia Colomba , Marcello Ciaccio

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引用次数: 0

摘要

儿童脓毒症仍然是一个主要的全球健康挑战，与年龄相关的表现变异性和诊断不确定性使其复杂化。单核细胞分布宽度（MDW）是一种自动报告全血细胞计数的单核细胞异数的测量方法，有望作为成人脓毒症的早期生物标志物。然而，其在儿科患者中的临床应用仍不清楚。在这项研究中，我们探讨了MDW在儿科急诊科（ED）的有效性。方法：我们对因任何原因到急诊科就诊的18岁儿童患者进行了回顾性观察研究。患者分为四组：对照组（无感染）、感染组、败血症组和休克组。MDW值跨组比较，并按年龄分层(≤6岁和>；6年)。采用受试者工作特征（ROC）曲线分析来评估MDW的诊断效果。结果共纳入393例患者：对照组117例，感染组183例，败血症组88例，休克组5例。总体而言，MDW值随着疾病严重程度的增加而增加，在休克患者中达到峰值。然而，在感染组和败血症组之间观察到明显的重叠，特别是在≤6岁的儿童中，即使在对照组中，MDW也升高。在儿童中6年来，MDW在不同疾病类别中呈现出更清晰的逐步增加。ROC分析显示，在临界值为24时，区分感染和非感染患者的AUC为0.73。结论smdw是一种易于获取的生物标志物，可能有助于在急诊环境中识别儿科感染患者。

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Evaluating monocyte distribution width in pediatric emergency care

Background

Pediatric sepsis remains a major global health challenge, complicated by age-related variability in presentation and diagnostic uncertainty. Monocyte Distribution Width (MDW), a measure of monocyte anisocytosis automatically reported with complete blood count, has shown promise as an early biomarker for sepsis in adults. However, its clinical utility in pediatric patients remains unclear. In this study, we explored the usefulness of MDW in pediatric patients presenting to the emergency department (ED).

Methods

We conducted a retrospective observational study of pediatric patients (<18 years) who presented to the ED for any cause. Patients were categorized into four groups: controls (no infection), infection, sepsis, and shock. MDW values were compared across groups and stratified by age (≤6 years and > 6 years). A receiver operating characteristic (ROC) curve analysis was performed to assess MDW diagnostic performance.

Results

A total of 393 patients were enrolled: 117 controls, 183 with infection, 88 with sepsis, and 5 with shock. Overall, MDW values increased with disease severity, peaking in patients with shock. However, significant overlap was observed between infection and sepsis groups, particularly in children ≤ 6 years, where MDW was elevated even in controls. In children > 6 years, MDW showed a clearer stepwise increase across disease categories. ROC analysis revealed an AUC of 0.73 for distinguishing infected from non-infected patients at a cutoff of 24.

Conclusions

MDW is a readily accessible biomarker that may aid in identifying pediatric patients with infection in emergency settings.

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.