A randomized trial examining the effect of predictive analytics and tailored interventions on the cost of care.

This two-arm randomized controlled trial evaluated the impact of a Stepped-Care intervention (predictive analytics combined with tailored interventions) on the healthcare costs of older adults using a Personal Emergency Response System (PERS). A total of 370 patients aged 65 and over with healthcare costs in the middle segment of the cost pyramid for the fiscal year prior to their enrollment were enrolled for the study. During a 180-day intervention period, control group (CG) received standard care, while intervention group (IG) received the Stepped-Care intervention. The IG had 31% lower annualized inpatient cost per patient compared with the CG (3.7 K, $8.1 K vs. $11.8 K, p = 0.02). Both groups had similar annualized outpatient costs per patient ($6.1 K vs. $5.8 K, p = 0.10). The annualized total cost reduction per patient in the IG vs. CG was 20% (3.5 K, $17.7 K vs. $14.2 K, p = 0.04). Predictive analytics coupled with tailored interventions has great potential to reduce healthcare costs in older adults, thereby supporting population health management in home or community settings.

Validation of a fluid-structure interaction model of solute transport in pores of cyclically deformed tissue scaffolds.

Convection induced by repetitive compression of porous tissue scaffolds enhances solute transport inside the scaffold. Our previous experiments have shown that pore size, shape, and orientation with respect to strain direction greatly influence loading-induced solute transport. The objective of this study was to develop a computational model of deformation-induced solute transport in porous tissue scaffolds, which included the pore geometry of the scaffold. This geometry consisted of a cubic scaffold with single channel in the middle of the scaffold, immersed in a fluid reservoir. Cylindrical pores with circular or elliptic cross section, and spheroid pores were modeled. The scaffold was cyclically compressed from one side, causing fluid motion and dispersion of solute inside the scaffold pore. Scaffold deformation was solved using the finite element method, and fluid flow and solute transport were solved using the finite volume method. The distortion of the scaffold-fluid interface was transferred as a boundary condition to the fluid flow solver. Both convection and diffusion were included in the computations. The solute transport rates in the different scaffold pore geometries agreed well with our previous experimental results obtained with X-ray microimaging. This model will be used to explore transport properties of a spectrum of novel scaffold designs.

Cyclic deformation-induced solute transport in tissue scaffolds with computer designed, interconnected, pore networks: experiments and simulations.

Nutrient supply and waste removal in porous tissue engineering scaffolds decrease from the periphery to the center, leading to limited depth of ingrowth of new tissue into the scaffold. However, as many tissues experience cyclic physiological strains, this may provide a mechanism to enhance solute transport in vivo before vascularization of the scaffold. The hypothesis of this study was that pore cross-sectional geometry and interconnectivity are of major importance for the effectiveness of cyclic deformation-induced solute transport. Transparent elastic polyurethane scaffolds, with computer-programmed design of pore networks in the form of interconnected channels, were fabricated using a 3D printing and injection molding technique. The scaffold pores were loaded with a colored tracer for optical contrast, cyclically compressed with deformations of 10 and 15% of the original undeformed height at 1.0 Hz. Digital imaging was used to quantify the spatial distribution of the tracer concentration within the pores. Numerical simulations of a fluid-structure interaction model of deformation-induced solute transport were compared to the experimental data. The results of experiments and modeling agreed well and showed that pore interconnectivity heavily influences deformation-induced solute transport. Pore cross-sectional geometry appears to be of less relative importance in interconnected pore networks. Validated computer models of solute transport can be used to design optimal scaffold pore geometries that will enhance the convective transport of nutrients inside the scaffold and the removal of waste, thus improving the cell survivability deep inside the scaffold.

Solute transport in cyclically deformed porous tissue scaffolds with controlled pore cross-sectional geometries.

The objective of this study was to investigate the influence of pore geometry on the transport rate and depth after repetitive mechanical deformation of porous scaffolds for tissue engineering applications. Flexible cubic imaging phantoms with pores in the shape of a circular cylinder, elliptic cylinder, and spheroid were fabricated from a biodegradable polymer blend using a combined 3D printing and injection molding technique. The specimens were immersed in fluid and loaded with a solution of a radiopaque solute. The solute distribution was quantified by recording 20 microm pixel-resolution images in an X-ray microimaging scanner at selected time points after intervals of dynamic straining with a mean strain of 8.6+/-1.6% at 1.0 Hz. The results show that application of cyclic strain significantly increases the rate and depth of solute transport, as compared to diffusive transport alone, for all pore shapes. In addition, pore shape, pore size, and the orientation of the pore cross-sectional asymmetry with respect to the direction of strain greatly influence solute transport. Thus, pore geometry can be tailored to increase transport rates and depths in cyclically deformed scaffolds, which is of utmost importance when thick, metabolically functional tissues are to be engineered.

Bayesian tracking of a nonlinear model of the capnogram.

Capnography, the monitoring of expired carbon dioxide (CO2) has been employed clinically as a non-invasive measure for the adequacy of ventilation of the alveoli of the lung. In combination with air flow measurements, the capnogram can be used to estimate the partial pressure of CO2 in the alveolar sacs. In addition, physiologically relevant parameters, such as the extent of CO2 rebreathing, the airway dead space, and the metabolic CO2 production can be predicted. To calculate these parameters, mathematical models have been previously formulated and applied to experimental data using off-line optimization procedures. Unfortunately, this does not permit online identification of the capnogram to detect changes in the physiological model parameters. In the present study, a Bayesian method for breath-by-breath identification of the volumetric capnogram is presented. The method integrates a model of CO2 exchange in the lungs, which is nonlinear due to the nature of human tidal breathing, with a particle filtering algorithm for estimation of the model parameters and changes therein. In addition, this allowed for a dynamic prediction of the unmeasured alveolar CO2 tension. The method is demonstrated using simulations of the capnogram. The proposed method could aid the clinician in the interpretation of the capnogram.