Oxygen saturation determined from deep muscle, not thenar tissue, is an early indicator of central hypovolemia in humans.

OBJECTIVE: To compare the responses of noninvasively measured tissue oxygen saturation (StO2) and calculated muscle oxygen tension (PmO2) to standard hemodynamic variables for early detection of imminent hemodynamic instability during progressive central hypovolemia in humans. DESIGN: Prospective study. SETTING: Research laboratory. SUBJECTS: Sixteen healthy human volunteers. INTERVENTIONS: Progressive lower body negative pressure (LBNP) to onset of cardiovascular collapse. MEASUREMENTS AND MAIN RESULTS: Noninvasive measurements of blood pressures, heart rate, and stroke volume were obtained during progressive LBNP with simultaneous assessments of StO2, PmO2, and muscle oxygen saturation (SmO2). Forearm SmO2 and PmO2 were determined with a novel near infrared spectroscopic measurement device (UMMS) and compared with thenar StO2 measured by a commercial device (HT). All values were normalized to the duration of LBNP exposure required for cardiovascular collapse in each subject (i.e., LBNP maximum). Stroke volume was significantly decreased at 25% of LBNP maximum, whereas blood pressure was a late indicator of imminent cardiovascular collapse. PmO2 (UMMS) was significantly decreased at 50% of maximum LBNP while SmO2 (UMMS) decreased at 75% of maximum LBNP. Thenar StO2 (HT) showed no statistical change throughout the entire LBNP protocol. CONCLUSIONS: Spectroscopic assessment of forearm muscle PO2 and SmO2 provides noninvasive and continuous measures that are early indicators of impending cardiovascular collapse resulting from progressive reductions in central blood volume.

Noninvasively determined muscle oxygen saturation is an early indicator of central hypovolemia in humans.

Ten healthy human volunteers were subjected to progressive lower body negative pressure (LBNP) to the onset of cardiovascular collapse to compare the response of noninvasively determined skin and fat corrected deep muscle oxygen saturation (SmO2) and pH to standard hemodynamic parameters for early detection of imminent hemodynamic instability. Muscle SmO2 and pH were determined with a novel near infrared spectroscopic (NIRS) technique. Heart rate (HR) was measured continuously via ECG, and arterial blood pressure (BP) and stroke volume (SV) were obtained noninvasively via Finometer and impedance cardiography on a beat-to-beat basis. SmO2 and SV were significantly decreased during the first LBNP level (-15 mmHg), whereas HR and BP were late indicators of impending cardiovascular collapse. SmO2 declined in parallel with SV and inversely with total peripheral resistance, suggesting, in this model, that SmO2 is an early indicator of a reduction in oxygen delivery through vasoconstriction. Muscle pH decreased later, suggesting an imbalance between delivery and demand. Spectroscopic determination of SmO2 is noninvasive and continuous, providing an early indication of impending cardiovascular collapse resulting from progressive reduction in central blood volume.

Noninvasive in vivo measurement of venous blood pH during exercise using near-infrared reflectance spectroscopy.

Blood pH is an important indicator of anaerobic metabolism in exercising muscle. This paper demonstrates multivariate calibration techniques that can be used to produce a general pH model that can be applied to spectra from any new subject without significant prediction error. Tissue spectra (725 approximately 880 nm) were acquired through the skin overlying the flexor digitorum profundus muscle on the forearms of eight healthy subjects during repetitive hand-grip exercise and referenced to the pH of venous blood drawn from a catheter placed in a vein close to the muscle. Calibration models were developed using multi-subject partial least squares (PLS) and validated using subject-out cross-validation after the subject-to-subject spectral variations were corrected by mathematical preprocessing methods. A combination of standard normal variate (SNV) scaling and principal component analysis loading correction (PCALC) successfully removed most of the subject-to-subject variations and provided the most accurate prediction results.

Removal of analyte-irrelevant variations in near-infrared tissue spectra.

This paper describes mathematical techniques to correct for analyte-irrelevant optical variability in tissue spectra by combining multiple preprocessing techniques to address variability in spectral properties of tissue overlying and within the muscle. A mathematical preprocessing method called principal component analysis (PCA) loading correction is discussed for removal of inter-subject, analyte-irrelevant variations in muscle scattering from continuous-wave diffuse reflectance near-infrared (NIR) spectra. The correction is completed by orthogonalizing spectra to a set of loading vectors of the principal components obtained from principal component analysis of spectra with the same analyte value, across different subjects in the calibration set. Once the loading vectors are obtained, no knowledge of analyte values is required for future spectral correction. The method was tested on tissue-like, three-layer phantoms using partial least squares (PLS) regression to predict the absorber concentration in the phantom muscle layer from the NIR spectra. Two other mathematical methods, short-distance correction to remove spectral interference from skin and fat layers and standard normal variate scaling, were also applied and/or combined with the proposed method prior to the PLS analysis. Each of the preprocessing methods improved model prediction and/or reduced model complexity. The combination of the three preprocessing methods provided the most accurate prediction results. We also performed a preliminary validation on in vivo human tissue spectra.

Influence of a fat layer on the near infrared spectra of human muscle: quantitative analysis based on two-layered Monte Carlo simulations and phantom experiments.

The influence of fat thickness on the diffuse reflectance spectra of muscle in the near infrared (NIR) region is studied by Monte Carlo simulations of a two-layer structure and with phantom experiments. A polynomial relationship was established between the fat thickness and the detected diffuse reflectance. The influence of a range of optical coefficients (absorption and reduced scattering) for fat and muscle over the known range of human physiological values was also investigated. Subject-to-subject variation in the fat optical coefficients and thickness can be ignored if the fat thickness is less than 5 mm. A method was proposed to correct the fat thickness influence.

Skin color correction for tissue spectroscopy: demonstration of a novel approach with tissue-mimicking phantoms.

The application of partial least squares (PLS) regression to visible-near-infrared (VIS-NIR) spectroscopy for modeling important blood and tissue parameters is generally complicated by the variation in skin pigmentation (melanin) across the human population. An orthogonal correction method for removing the influence of skin pigmentation has been demonstrated in diffuse reflectance spectra from two-layer tissue-mimicking phantoms. The absorption properties of the phantoms were defined by lyophilized human hemoglobin (bottom layer) and synthetic melanin (top layer). Tissue-like scattering was simulated in both layers with intralipid. The approach uses principal components analysis (PCA) loading vectors from a separate set of phantom spectra that encode the unwanted melanin variation to remove the effect of melanin from the test phantoms. The preprocessing of phantom spectra using this orthogonal correction method resulted in PLS models with reduced complexity and enhanced prediction performance. Preliminary results from a separate study that evaluates the feasibility of defining skin color variation in an experiment with a single human subject are also presented.

Simultaneous correction of the influence of skin color and fat on tissue spectroscopy by use of a two-distance fiber-optic probe and orthogonalization technique.

We have demonstrated simultaneous correction for the optical interference of skin and fat in tissue spectra by using a two-distance fiber-optic probe. We obtained the correction by orthogonalizing the spectra collected at a long source-detector distance (SD) to the spectra collected at a short SD and mapped to the long SD space. The method was validated in tissuelike three-layer phantoms as well as preliminarily in human tissue. After the correction, a partial-least-squares model of the phantoms showed enhanced prediction performance.

Design of angle-tolerant multivariate optical elements for chemical imaging.

Multivariate optical elements (MOEs) are multilayer optical interference coatings with arbitrary spectral profiles that are used in multivariate pattern recognition to perform the task of projecting magnitudes of special basis functions (regression vectors) out of optical spectra. Because MOEs depend on optical interference effects, their performance is sensitive to the angle of incidence of incident light. This angle dependence complicates their use in imaging applications. We report a method for the design of angle-insensitive MOEs based on modification of a previously described nonlinear optimization algorithm. This algorithm operates when the effects of deviant angles of incidence are simulated prior to optimization, which treats the angular deviation as an interferent in the measurement. To demonstrate the algorithm, a 13-layer imaging MOE (IMOE, with alternating layers of high-index Nb2O5 and low-index SiO2) for the determination of Bismarck Brown dye in mixtures of Bismarck Brown and Crystal Violet, was designed and its performance simulated. For angles of incidence that range from 42 degrees to 48 degrees, the IMOE has an average standard error of prediction (SEP) of 0.55 microM for Bismarck Brown. This compares with a SEP of 2.8 microM for a MOE designed by a fixed-angle algorithm.