A machine learning approach for assessing acute infection by erythrocyte sedimentation rate (ESR) kinetics

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-04-22 DOI:10.1016/j.cca.2025.120308

Andrea Padoan , Ilaria Talli , Michela Pelloso , Luisa Galla , Francesca Tosato , Daniela Diamanti , Chiara Cosma , Elisa Pangrazzi , Alessandra Brogi , Martina Zaninotto , Mario Plebani

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引用次数: 0

Abstract

Background

The erythrocyte sedimentation rate (ESR) is a traditional marker of inflammation, valued for its simplicity and low cost but limited by unsatisfactory specificity and sensitivity. This study evaluated the equivalence of ESR measurements obtained from three automated analyzers compared to the Westergren method. Furthermore, various machine learning (ML) techniques were employed to assess the usefulness of early sedimentation kinetics in inflammatory disease classification.

Methods

A total of 346 blood samples from control, rheumatological, oncological, and sepsis/acute inflammatory status groups were analyzed. ESR was measured using TEST 1 (Alifax Spa, Padua, Italy), VESMATIC 5 (Diesse Diagnostica Senese Spa, Siena, Italy), CUBE 30 TOUCH (Diesse Diagnostica Senese Spa, Siena, Italy) analyzers, and the Westergren method. Early sedimentation rate kinetics (within 20 min) obtained with the CUBE 30 TOUCH were assessed. ML models [Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Naïve Bayes (NB), Neural Networks (NN) and logistic regression (LR)] in discriminating groups were trained and validated using ESR, sedimentation slopes, and clinical data. A second validation cohort of control and sepsis samples was used to validate LR models.

Results

Automated methods showed good agreement with Westergren’s results. Multivariate analyses identified significant associations between ESR values (measured by CUBE 30 TOUCH) and age (p = 0.025), gender (p < 0.001), and, overall, with samples’ group (p < 0.001). Sedimentation rate slopes differed significantly across groups, particularly between 12 and 20 min, with sepsis cases showing distinct patterns. ML models achieved moderate accuracy, with GBM performing best (AUC 0.800). LR for sepsis classification in the validation cohort achieved an AUC of 0.884, with high sensitivity (96.9 %) and specificity (74.2 %). In the second validation cohort, LR outperformed prior results, reaching an AUC of 0.991 (95 % CI: 0.973–1.000), with 95.2 % sensitivity and 100 %.

Conclusions

Current automated technologies for ESR measurement well agree with the reference method and provide robust results for evaluating systemic infections. The novelty of this study lies in connecting ESR sedimentation kinetics to disease states, particularly for identifying sepsis/acute inflammatory status. Future studies with larger datasets are needed to validate these approaches and guide clinical application.

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通过红细胞沉降率（ESR）动力学评估急性感染的机器学习方法

背景红细胞沉降率（ESR）是一种传统的炎症标记物，因其简单、成本低而备受推崇，但其特异性和灵敏度却不尽如人意。本研究评估了三种自动分析仪与 Westergren 方法相比 ESR 测量结果的等效性。此外，还采用了各种机器学习（ML）技术来评估早期血沉动力学在炎症疾病分类中的实用性。方法分析了来自对照组、风湿病组、肿瘤组和败血症/急性炎症组的共 346 份血液样本。使用 TEST 1（Alifax Spa，意大利帕多瓦）、VESMATIC 5（Diesse Diagnostica Senese Spa，意大利锡耶纳）、CUBE 30 TOUCH（Diesse Diagnostica Senese Spa，意大利锡耶纳）分析仪和 Westergren 法测量血沉。对使用 CUBE 30 TOUCH 获得的早期沉降率动力学（20 分钟内）进行了评估。使用血沉、血沉斜率和临床数据训练并验证了用于分辨组别的多重模型[梯度提升机（GBM）、支持向量机（SVM）、奈夫贝叶斯（NB）、神经网络（NN）和逻辑回归（LR）]。第二个验证队列包括对照组和败血症样本，用于验证 LR 模型。多变量分析发现，血沉值（通过 CUBE 30 TOUCH 测量）与年龄（p = 0.025）、性别（p < 0.001）以及样本组别（p < 0.001）之间存在显著关联。各组血沉斜率差异显著，尤其是在 12 至 20 分钟之间，脓毒症病例表现出独特的模式。ML 模型达到了中等准确度，其中 GBM 表现最佳（AUC 0.800）。在验证队列中，脓毒症分类 LR 的 AUC 为 0.884，灵敏度（96.9%）和特异度（74.2%）都很高。在第二个验证队列中，LR 的表现优于之前的结果，AUC 达到 0.991（95% CI：0.973-1.000），灵敏度为 95.2%，特异性为 100%。这项研究的新颖之处在于将血沉动力学与疾病状态联系起来，特别是用于识别败血症/急性炎症状态。今后还需要利用更大的数据集进行研究，以验证这些方法并指导临床应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.