ABSTRACT
Background: The severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is a highly contagious illness associated with a hyperactivated and dysregulated host immune humoral response. In detail, there is a cytokine storm which may take to the release of interleukin IL-6 as a critical mediator for respiratory failure, shock and multiorgan dysfunction. Such dysregulation may act as a target for therapeutics and, in this view, a blockade of IL-6 function by an anti-IL-6 receptor antibody (tocilizumab) has been described to be effective for the treatment of the inflammatory process COVID-19-related. Timing of administration of therapy was reported in literature to have a critical role in benefit for patients;thus, the aim of the present study is to compare two different methods for the IL-6 assessment: the Human IL-6 ELISA Kit (Invitrogen) and the Elecsys IL-6 (Roche). Method(s): IL-6 levels of 128 COVID-19 patients, who were consequently admitted to the Emergency and Medicine Department of AOU Careggi (Hospital in Florence -Italy ) between April and May 2020, were assessed by using the two above mentioned methods and were analysed through Passing-Bablok regression fit and Bland-Altman plot. Result(s): The analyses showed that the two methods correlate, but do not agree in terms of numeric results. In particular, further investigation were performed on the Bland-Altman results, showing that the maximum number of samples for which the differences between the two methods is close to "0" (p > 0.05) (which means a good overlap between the two methods) is 49 (p=0.07), and among them, 40 samples showed a complete agreement of results (p=0.95). These results can be attributed to the different methods' linearities: 3.1-200 pg/mL for ELISA and 1.5#5000 pg/mL for ECLIA, which could be extended to 50 000 pg/mL. Conclusion(s): Although a small percentage of data overlapping in a certain range, still a high correlation among the two methods can be found;given the overall analytical performance of the ECLIA, it can be considered more adequate for different reasons: i) it is available on a fully automated platform h24, ii) it uses of a small sample volume, iii) it is low cost and no-time consuming and iiii) the different timing for measuring IL-6 is much attractive.
ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic.According to the CDC, RT-PCR in respiratory samples is the gold standard for confirming the disease, although it has practical limitations as time-consuming procedures and a high rate of false-negative results. Based on data collected at Careggi Hospital from April 7th-30th 2020,we aim to assess the accuracy of a COVID-19 diagnosis through classification methods based on blood tests and information collected at the ED. 971 pts with pre-specified features of suspected COVID-19 were enrolled;physicians prospectively dichotomized patients in COVID-19 likely/unlikely based on clinical features plus results of bedside imaging.Considering the limits of each method to classify a case COVID-19 positive, further evaluation was performed to form the COVID-19 final diagnosis, established after independent clinical review of 30-day follow-up data. Several classifiers were implemented, both parametric (Logistic Regression, LR;Quadratic Discriminant Analysis, QDA) and non-parametric (Random Forest, RF;Support Vector Machine;Neural Networks;K-nearest neighbour;Naive Bayes). Log transform was applied to some of the covariates and results compared with non transformed data.The dataset was divided in training and validation sets.Results based on validation sample show an AUC>0.8 for all classifiers. Best results are obtained applying RF, LR and QDA to a rebalanced sample using the SMOTE techniques on the log transformed data, showing an AUC of 0.890 (LR),0.896 (QDA) and 0.864 (RF). In parallel, best Sens and Spec are obtained via the above methods, the highest chieved by the LR (Sens 0.696;Spec 0.877). The rather high rate of false negative seems to be a feature inherently characterizing this classification problem.Good discriminatory power was shown for: WBC, Neut, AST, LDH, PCR, Na, IL-6 plus symptoms' information. Parametric models have the additional advantage of allowing a scientific interpretation.The performance of the classifiers with respect to the physician's gestalt and data validation are ongoing. The proposed classifiers show a good level of Sens.To improve Spec, a 3-level classification can be implemented;this tool can help in taking decisions when time and resources are scarce.