Plasma and Cellular Forms of Fibronectin as Prognostic Markers in Sepsis

Background. There is a pressing need for specific prognostic markers that could be used to monitor the severity of sepsis. The aims of our study were to investigate changes in the expression of different molecular forms of fibronectin in sepsis and to assess their relationship to the clinical severity and mortality of patients. Material and Methods. Forms of fibronectin: plasma (pFN), cellular (EDA-FN), FN-fibrin complexes, and fibronectin fragments were analyzed in 71 sepsis patients (survivors and nonsurvivors) and in the control by ELISA and immunoblotting. Results. The baseline pFN concentration of patients with sepsis was significantly lower than in the control (133.0 mg/L vs. 231.2 mg/L) (P<0.001), and in nonsurvivors, it was lower than in survivors (106.0 mg/L vs. 152.8 mg/L) (P=0.004). The baseline EDA-FN was significantly elevated in both sepsis groups (survivors: 6.7 mg/L; nonsurvivors: 9.4 mg/L) compared to the control (1.4 mg/L) (P<0.001). It should be noted that among patients with more severe sepsis, the EDA-FN level was higher in nonsurvivors than in survivors. Furthermore, molecular FN-fibrin complexes as well as FN fragments occurred much more frequently in nonsurvivors than in survivors. Conclusion. The study showed that in sepsis, changes in plasmatic and cellular form of fibronectin were associated with the severity of sepsis and may be useful predictors of outcome

Fibronectin as a Marker of Disease Severity in Critically Ill COVID-19 Patients

The SARS-CoV-2 virus alters the expression of genes for extracellular matrix proteins, including fibronectin. The aim of the study was to establish the relationship between different forms of fibronectin, such as plasma (pFN), cellular (EDA-FN), and proteolytic FN-fragments, and disease severity and mortality of critically ill patients treated in the intensive care unit. The levels of pFN, EDA-FN, and FN-fragments were measured in patients with a viral (N = 43, COVID-19) or bacterial (N = 41, sepsis) infection, using immunoblotting and ELISA. The level of EDA-FN, but not pFN, was related to the treatment outcome and was significantly higher in COVID-19 Non-survivors than in Survivors. Furthermore, EDA-FN levels correlated with APACHE II and SOFA scores. FN-fragments were detected in 95% of COVID-19 samples and the amount was significantly higher in Non-survivors than in Survivors. Interestingly, FN-fragments were present in only 56% of samples from patients with bacterial sepsis, with no significant differences between Non-survivors and Survivors. The new knowledge gained from our research will help to understand the differences in immune response depending on the etiology of the infection. Fibronectin is a potential biomarker that can be used in clinical settings to monitor the condition of COVID-19 patients and predict treatment outcomes

Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis

Fibronectin (FN) plays an essential role in the host&rsquo;s response to infection. In previous studies, a significant decrease in the FN level was observed in sepsis; however, it has not been clearly elucidated how this parameter affects the patient&rsquo;s survival. To better understand the relationship between FN and survival, we utilized innovative approaches from the field of explainable machine learning, including local explanations (Break Down, Shapley Additive Values, Ceteris Paribus), to understand the contribution of FN to predicting individual patient survival. The methodology provides new opportunities to personalize informative predictions for patients. The results showed that the most important indicators for predicting survival in sepsis were INR, FN, age, and the APACHE II score. ROC curve analysis showed that the model&rsquo;s successful classification rate was 0.92, its sensitivity was 0.92, its positive predictive value was 0.76, and its accuracy was 0.79. To illustrate these possibilities, we have developed and shared a web-based risk calculator for exploring individual patient risk. The web application can be continuously updated with new data in order to further improve the model