Multivariate computational analysis of biosensor's data for improved CD64 quantification for sepsis diagnosis

Abstract

Sepsis, as a leading cause of death worldwide, relies on systemic inflammatory response syndrome (SIRS) criteria for its diagnosis. SIRS is highly non-specific as it relies on monitoring of patients' vitals for sepsis diagnosis, which are known to change with many confounding factors. Changes in leukocyte counts and CD64 expression levels are known specific biomarkers of pro-inflammatory host response at the onset of sepsis. Recently, we have developed a biosensor chip that can enumerate the leukocyte counts and quantify the neutrophil CD64 expression levels from a drop of blood. We were able to show improved sepsis diagnosis and prognosis in clinical studies by measuring these parameters during different times of the patients' stay in hospital. In this paper, we investigated the rate of cell capture with CD64 expression levels and used this in a multivariate computational model using artificial neural networks (ANNs) and showed improved accuracy of quantifying CD64 expression levels from the biosensor (n = 106 whole blood experiments). We found a high coefficient of determination and low error between biosensor- and flow cytometry-based neutrophil CD64 expression levels using multiple ANN training methods in comparison to those of univariate regression commonly employed. This approach can find many applications in biosensor data analytics by utilizing multiple features of the biosensor's data for output determination.