Padoan Andrea, Talli Ilaria, Pelloso Michela, Galla Luisa, Tosato Francesca, Diamanti Daniela, Cosma Chiara, Pangrazzi Elisa, Brogi Alessandra, Zaninotto Martina, Plebani Mario
Department of Medicine (DIMED), University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital, Padova, Italy.
Department of Medicine (DIMED), University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital, Padova, Italy; QI.LAB.MED, Spin-off of the University of Padova, Italy.
Clin Chim Acta. 2025 Jun 15;574:120308. doi: 10.1016/j.cca.2025.120308. Epub 2025 Apr 22.
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.
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.
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 %.
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.
红细胞沉降率（ESR）是一种传统的炎症标志物，因其操作简单、成本低廉而受到重视，但特异性和敏感性不尽人意。本研究评估了三种自动分析仪测得的ESR值与魏氏法结果的等效性。此外，还采用了各种机器学习（ML）技术来评估早期沉降动力学在炎症性疾病分类中的作用。
共分析了来自对照组、风湿性疾病组、肿瘤组和脓毒症/急性炎症状态组的346份血液样本。使用TEST 1（意大利帕多瓦的Alifax Spa公司）、VESMATIC 5（意大利锡耶纳的Diesse Diagnostica Senese Spa公司）、CUBE 30 TOUCH（意大利锡耶纳的Diesse Diagnostica Senese Spa公司）分析仪以及魏氏法测量ESR。评估了用CUBE 30 TOUCH获得的早期沉降率动力学（20分钟内）。使用ESR、沉降斜率和临床数据训练并验证了用于区分各组的ML模型[梯度提升机（GBM）、支持向量机（SVM）、朴素贝叶斯（NB）、神经网络（NN）和逻辑回归（LR）]。使用对照组和脓毒症样本的第二个验证队列来验证LR模型。
自动化方法与魏氏法的结果显示出良好的一致性。多变量分析确定了ESR值（由CUBE 30 TOUCH测量）与年龄（p = 0.025）、性别（p < 0.001）以及总体上与样本组（p < 0.001）之间存在显著关联。各组的沉降率斜率差异显著，特别是在12至20分钟之间，脓毒症病例呈现出独特的模式。ML模型达到了中等准确性，其中GBM表现最佳（AUC为0.800）。验证队列中用于脓毒症分类的LR的AUC为0.884，敏感性高（96.9%），特异性高（74.2%）。在第二个验证队列中，LR的表现优于先前的结果，AUC达到0.991（95%CI：0.973 - 1.000）；敏感性为95.2%，特异性为100%。
目前用于ESR测量的自动化技术与参考方法高度一致，并为评估全身感染提供了可靠的结果。本研究的新颖之处在于将ESR沉降动力学与疾病状态联系起来，特别是用于识别脓毒症/急性炎症状态。需要更大数据集的未来研究来验证这些方法并指导临床应用。
