A 4-year randomized trial comparing three outreach interventions to promote screening mammograms.

As population health has become a focus of health care payers and providers, interest has grown in mail, phone, and other forms of outreach for improving population rates of cancer screening. Translational research is needed to compare the effectiveness and cost of low- and high-intensity behavioral outreach interventions for promoting cancer screening. The purpose of the article is to compare the effectiveness in promoting biannual mammograms of three interventions delivered over 4 years to a primary care population with a high baseline mammography adherence of 83.3%. We randomized women aged 40-84 to reminder letter only (LO arm), letter + reminder call (RC arm), and two letters + counseling call (CC arm) involving tailored education and motivational interviewing. Mammography adherence (≥1 mammogram in the previous 24 months) at four time points was determined from insurance claims records. Over 4 years, 30,162 women were randomized. At the end of 4 years, adherence was highest in the RC arm (83.0%) compared with CC (80.8%) and LO (80.8%) arms (p = .03). Only 23.5% of women in the CC arm were reached and accepted full counseling. The incremental cost per additional mammogram for RC arm women was $30.45 over the LO arm cost. A simple reminder call can increase screening mammogram adherence even when baseline adherence is high. Some more complex behavioral interventions delivered by mail and phone as in this study may be less effective, due to limited participation of patients, a focus on ambivalence, lack of follow-up, and other factors.

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.

Quantitative measurement of muscle oxygen saturation without influence from skin and fat using continuous-wave near infrared spectroscopy.

A method to non-invasively and quantitatively measure muscle oxygen saturation (SmO(2)) using broadband continuous-wave diffuse reflectance near infrared (NIR) spectroscopy is presented. The method obtained SmO(2) by first correcting NIR spectra for absorption and scattering of skin pigment and fat, then fitting to a Taylor expansion attenuation model. A non-linear least squares optimization algorithm with set boundary constraints on the fitting parameters was used to fit the model to the acquired spectra. A data preprocessing/optimization scheme for accurately determining the initial values needed for the optimization was also employed. The method was evaluated on simulated muscle spectra with 4 different scattering properties, as well as on in vivo forearm spectra from 5 healthy volunteer subjects during arterial occlusion. Measurement repeatability was assessed on 24 healthy volunteers with 5 repeated measurements, each separated by at least 48 hours.

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.

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.

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.