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

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.