Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers.

BACKGROUND: To introduce the Hemorrhage Intensive Severity and Survivability (HISS) score, based on the fusion of multi-biomarker data; glucose, lactate, pH, potassium, and oxygen tension, to serve as a patient-specific attribute in hemorrhagic trauma. MATERIALS AND METHODS: One hundred instances of Sensible Fictitious Rationalized Patient (SFRP) data were synthetically generated and the HISS score assigned by five clinically active physician experts (100 [5]). The HISS score stratifies the criticality of the trauma patient as; low(0), guarded(1), elevated(2), high(3) and severe(4). Standard classifier algorithms; linear support vector machine (SVM-L), multi-class ensemble bagged decision tree (EBDT), artificial neural network with bayesian regularization (ANN:BR) and possibility rule-based using function approximation (PRBF) were evaluated for their potential to similarly classify and predict a HISS score. RESULTS: SVM-L, EBDT, ANN:BR and PRBF generated score predictions with testing accuracies (majority vote) corresponding to 0.91 ± 0.06, 0.93 ± 0.04, 0.92 ± 0.07, and 0.92 ± 0.03, respectively, with no statistically significant difference (p > 0.05). Targeted accuracies of 0.99 and 0.999 could be achieved with SFRP data size and clinical expert scores of 147[7](0.99) and 154[9](0.999), respectively. CONCLUSIONS: The predictions of the data-driven model in conjunction with an adjunct multi-analyte biosensor intended for point-of-care continual monitoring of trauma patients, can aid in patient stratification and triage decision-making.

Toward Impedimetric Measurement of Acidosis with a pH-Responsive Hydrogel Sensor.

A pH-responsive, poly(2-hydroxyethyl methacrylate) [poly(HEMA)]-based hydrogel has been fashioned into an impedimetric pH sensor for the continual measurement and monitoring of tissue acidosis that can arise due to hemorrhaging trauma. Four hydrogel systems molecularly engineered to influence water distribution and ionic abundance were studied: a cationogenic primary amine, N-(2-aminoethyl) methacrylate (AEMA), a tertiary amine moiety, N,N-(2-dimethylamino)ethyl methacrylate (DMAEMA), and a combined AEMA-DMAEMA formulation. Electrochemical impedance spectroscopy (EIS) of hydrogel discs held between platinized Type 304 stainless steel mesh electrodes in pH-adjusted 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid sodium salt (HEPES) buffer and equivalent circuit modeling indicated that the AEMA hydrogel had the highest sensitivity containing the relevant pathophysiological range (pH 7.0-8.0). Thus, the AEMA formulation was studied at 0, 1, 3, 4.4, and 30 mol % AEMA. The 1 mol % AEMA was found to significantly (p < 0.05) discern nominal pH (7.35, 7.40, 7.45). The Taguchi Design of Experiments approach was employed and confirmed composition as a factor and 1 mol % AEMA to be the most robust. DMAEMA (0, 4.4, 14, 30 mol %) and AEMA-DMAEMA (0, 4.4, 14, 30 mol %) allowed the use of the one-factor Response Surface Methodology optimizer to confirm the AEMA 1 mol % system to be most robust, sensitive, and possessing optimal sensitivity in the pathophysiological pH sensing range (7.35-7.45) for hemorrhagic trauma. This composition was fashioned as a responsive membrane on a microlithographically fabricated interdigitated microsensor electrode and the sensitivity was determined using R(QR)(QR) analysis. Water distribution within the AEMA (0, 1, 4.4, 30 mol %), determined by gravimetric analysis and differential scanning calorimetry, revealed a strong anticorrelation between nonfreezable bound water and pH sensitivity (-0.82) and was in good agreement with the total hydration (-0.70). Nonfreezable bound water was found to be the most strongly correlated factor that governs the pH response of hydrogels.

Molecular engineering of poly(HEMA-co-PEGMA)-based hydrogels: Role of minor AEMA and DMAEMA inclusion.

Hydrogel membranes of in-dwelling pH-responsive devices are of interest for the development of biomedical sensors that must measure small changes in pH associated with tissue acidosis. Poly(2-hydroxyethyl methacrylate)-based hydrogels possessing minor amounts of the cationogenic N-(2-aminoethyl) methacrylamide (AEMA) (4 mol%) or a tertiary amine moiety, N,N-(2-dimethylamino)ethyl methacrylamide (DMAEMA) (4 mol%) or AEMA-DMAEMA (2 mol% each) were UV cross-linked with 1 mol% tetra(ethylene glycol) diacrylate (TEGDA) and the degree of hydration, free and bound water distribution, glass transition temperature, elastic modulus, membrane resistance and protein adsorption were studied. Correlation analysis reveals that each of these biotechnical properties is strongly anti-correlated with total hydration (-0.92) and that the bound water content dominates this anti-correlation (~-0.83). However, free water shows a direct, though only weak correlation with these properties (~+0.5). Thus, minor changes in the hydrogel composition (~4 mol%) can significantly influence biomaterials properties and may be useful in tailoring hydrogel properties for application in biosensors and engineered tissue scaffolds.

Dataset on hydrophobicity indices and differential scanning calorimetry thermograms for poly(HEMA)-based hydrogels.

Hydrophobicity indices for poly(HEMA)-based hydrogels: HEMA, AEMA, and DMAEMA calculated from two different methods: 1) Partition coefficients, and 2) Kyte-Doolittle scale are depicted. Thermograms from differential scanning calorimetry of poly(HEMA)-based hydrogels containing AEMA, DMAEMA, and a mixture of AEMA and DMAEMA are included to represent the glass transition temperature (Tg) values of the hydrogels. More information on the methodology to calculate the hydrophobicity indices using the aforementioned methods and the procedure for using a differential scanning calorimeter and analysis of a thermogram is described. Details of how the changes in the feed composition of poly(HEMA)-based hydrogels was made is provided in the research article 'MOLECULAR ENGINEERING OF POLY(HEMA-co-PEGMA)-BASED HYDROGELS: ROLE OF MINOR AEMA AND DMAEMA INCLUSION' (Bhat et al., 2019).[1].