Neonatal ICU antibiotic use trends within an integrated delivery network.

BACKGROUND AND OBJECTIVES: There is a need for robust antibiotic stewardship programs (ASPs) in the neonatal population. This study's objectives were to assess neonatal antibiotic use practices over an extended period across an integrated delivery network (IDN), including six Neonatal Intensive Care Units (NICUs), to identify those most successful practices reducing use rates. METHODS: A retrospective cohort study was conducted, including 15,015 NICU admissions from an integrated delivery network, across six hospitals over eight years (50% Level III and 50% Level II) computing antibiotic use rates (AURs) stratified by usage: in the first few days of the stay vs. later in the stay and by gestational age. Several metrics were examined for assumptions of strong correlation with AUR: (1) the percentage of infants given antibiotics early in their stays and (2) durations of courses of antibiotics. RESULTS: Results conclude a wide variation in AURs and trends that these rates followed over time. However, there was a decrease in overall AUR from 15.7-16.6 to 10.1-10.8%, with four of the six NICUs recording statistically significant reductions in AUR vs. their first year of measurement. Specifically, the level III NICUs overall AUR decreases from 15.1-16.22 to 8.6-9.4%, and level II NICUs overall AUR 20.3-24.4 to 14.1-16.1%. A particularly successful level II NICU decreased its AUR from 22.9-30.6 to 5.9-9.4%. CONCLUSION: To our knowledge, this is the first study to utilize data analytics at an IDN level to identify trends in AUR, We have identified practices that allowed an institution to reduce NICU AURs significantly, and which, if done as a standard practice, could be replicated on a broader scale.

A Retrospective Cohort Study of Clinical Factors Associated with Transitions of Care among COVID-19 Patients.

Coronavirus Disease 2019 (COVID-19) is an international health crisis. In this article, we report on patient characteristics associated with care transitions of: 1) hospital admission from the emergency department (ED) and 2) escalation to the intensive care unit (ICU). Analysis of data from the electronic medical record (EMR) was performed for patients with COVID-19 seen in the ED of a large Western U.S. Health System from April to August of 2020, totaling 10,079 encounters. Of these, 5172 resulted in admission as an inpatient within 72 h. Inpatient encounters (n = 6079) were also considered for patients with positive COVID-19 test results, of which 970 resulted in a transfer to the ICU or in-hospital mortality. Laboratory results, vital signs, symptoms, and comorbidities were investigated for each of these care transitions. Different top risk factors were found, but two factors common to hospital admission and ICU transfer were respiratory rate and the need for oxygen support. Comorbidities common to both settings were cerebrovascular disease and congestive heart failure. Regarding laboratory results, the neutrophil-to-lymphocyte ratio was associated with transitions to higher levels of care, along with the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT).

Image Registration for Microwave Tomography of the Breast Using Priors From Nonsimultaneous Previous Magnetic Resonance Images.

Microwave imaging is a low-cost imaging method that has shown promise for breast imaging and, in particular, neoadjuvant chemotherapy monitoring. The early studies of microwave imaging in the therapy monitoring setting are encouraging. For the neoadjuvant therapy application, it would be desirable to achieve the most accurate possible characterization of the tissue properties. One method to achieve increased resolution and specificity in microwave imaging reconstruction is the use of a soft prior regularization. The objective of this study is to develop a method to use magnetic resonance (MR) images, taken in a different imaging configuration, as this soft prior. To enable the use of the MR images as a soft prior, it is necessary to register the MR images to the microwave imaging space. Registration fiducials were placed around the breast that are visible in both the MRI and with an optical scanner integrated into the microwave system. Utilizing these common registration locations, numerical algorithms have been developed to warp the original breast MR images into a geometry closely resembling that in which the breast is pendant in the microwave system.

Efficient Simultaneous Reconstruction of Time-Varying Images and Electrode Contact Impedances in Electrical Impedance Tomography.

OBJECTIVE: In electrical impedance tomography (EIT), we apply patterns of currents on a set of electrodes at the external boundary of an object, measure the resulting potentials at the electrodes, and, given the aggregate dataset, reconstruct the complex conductivity and permittivity within the object. It is possible to maximize sensitivity to internal conductivity changes by simultaneously applying currents and measuring potentials on all electrodes but this approach also maximizes sensitivity to changes in impedance at the interface. METHODS: We have, therefore, developed algorithms to assess contact impedance changes at the interface as well as to efficiently and simultaneously reconstruct internal conductivity/permittivity changes within the body. We use simple linear algebraic manipulations, the generalized singular value decomposition, and a dual-mesh finite-element-based framework to reconstruct images in real time. We are also able to efficiently compute the linearized reconstruction for a wide range of regularization parameters and to compute both the generalized cross-validation parameter as well as the L-curve, objective approaches to determining the optimal regularization parameter, in a similarly efficient manner. RESULTS: Results are shown using data from a normal subject and from a clinical intensive care unit patient, both acquired with the GE GENESIS prototype EIT system, demonstrating significantly reduced boundary artifacts due to electrode drift and motion artifact.

Detection of small bleeds in the brain with electrical impedance tomography.

In this paper, we describe and assess feasibility of instrumentation and algorithms for detecting bleeding due to hemorrhagic strokes and traumatic brain injury using electrical impedance tomography, a novel biomedical diagnostic modality in which the body is probed noninvasively with generally imperceptible alternating currents applied in patterns to a set of electrodes placed in contact with the skin. We focus on the GENESIS instrument developed by GE Global Research and on the achievability of our goal to detect a bleed in the center of the head with a volume of several ml. Our main topic is compensation for the large changes in voltages that tend to occur when the electrodes are in contact with biological media, specifically either human subjects or with vegetable matter proxies which seem to exhibit the same 'drift' phenomenon. We show that these changes in voltages can be modeled by assuming that each electrode is attached to the body via a discrete complex impedance whose value is time-varying and describe how this discrete component value can be estimated and largely compensated-for. We compare this discrete model with changes in contact impedances estimated using the complete electrode model showing that the two models are roughly comparable in their ability to explain the data from a single human subject experiment with electrodes attached to the head. In a simulation study, we demonstrate that it is possible to detect a small bleed in the center of the head even in the case of large changes in electrode impedances, which can be treated as nuisance parameters.

Multi-channel electrical impedance tomography for regional tissue hydration monitoring.

Poor assessment of hydration status during hemodialysis can lead to under- or over-hydration in patients with consequences of increased morbidity and mortality. In current practice, fluid management is largely based on clinical assessments to estimate dry weight (normal hydration body weight). However, hemodialysis patients usually have co-morbidities that can make the signs of fluid status ambiguous. Therefore, achieving normal hydration status remains a major challenge for hemodialysis therapy. Electrical impedance technology has emerged as a promising method for hydration monitoring due to its non-invasive nature, low cost and ease-of-use. Conventional electrical impedance-based hydration monitoring systems employ single-channel current excitation (either 2-electrode or 4-electrode methods) to perturb and extract averaged impedance from bulk tissue and use generalized models from large populations to derive hydration estimates. In the present study, a prototype, single-frequency electrical impedance tomography (EIT) system with simultaneous multi-channel current excitation was used to enable regional hydration change detection. We demonstrated the capability to detect a difference in daily impedance change between left leg and right leg in healthy human subjects, who wore a compression sock only on one leg to reduce daily gravitational fluid accumulation. The impedance difference corresponded well with the difference of lower leg volume change between left leg and right leg measured by volumetry, which on average is ~35 ml, accounting for 0.7% of the lower leg volume. We have demonstrated the feasibility of using multi-channel EIT to extract hydration information in different tissue layers with minimal skin interference. Our simultaneous, multi-channel current excitation approach provides an effective method to separate electrode contact impedance and skin condition artifacts from hydration signals. The prototype system has the potential to be used in clinical settings for helping optimize patient fluid management during hemodialysis as well as for home monitoring of patients with congestive heart failure, chronic kidney disease, diabetes and other diseases with peripheral edema symptoms.

Real-time 3D electrical impedance imaging for ventilation and perfusion of the lung in lateral decubitus position.

We report a prototype Electrical Impedance Imaging System. It is able to detect the gravity-induced changes in the distributions of perfusion and ventilation in the lung between supine and lateral decubitus positions. Impedance data were collected on healthy volunteer subjects and 3D reconstructed images were produced in real-time, 20 frames per second on site, without using averaging or a contrast agent. Imaging data also can be reconstructed offline for further analysis.

Prediction of mortality from respiratory distress among long-term mechanically ventilated patients.

With the advent of inexpensive storage, pervasive networking, and wireless devices, it is now possible to store a large proportion of the medical data that is collected in the intensive care unit (ICU). These data sets can be used as valuable resources for developing and validating predictive analytics. In this report, we focus on the problem of prediction of mortality from respiratory distress among long-term mechanically ventilated patients using data from the publicly-available MIMIC-II database. Rather than only reporting p-values for univariate or multivariate regression, as in previous work, we seek to generate sparsest possible model that will predict mortality. We find that the presence of severe sepsis is highly associated with mortality. We also find that variables related to respiration rate have more predictive accuracy than variables related to oxygenation status. Ultimately, we have developed a model which predicts mortality from respiratory distress in the ICU with a cross-validated area-under-the-curve (AUC) of approximately 0.74. Four methodologies are utilized for model dimensionality-reduction: univariate logistic regression, multivariate logistic regression, decision trees, and penalized logistic regression.

Real-time 3D electrical impedance imaging for ventilation monitoring of the lung: Pilot study.

We report an Electrical Impedance Tomography device capable of detecting gravity-induced regional ventilation changes in real-time without averaging or using a contrast medium. Changes in lung ventilation are demonstrated in right and left lateral decubitus position and compared to those seen in an upright and supine normal subject.

Combined optical and X-ray tomosynthesis breast imaging.

PURPOSE: To explore the optical and physiologic properties of normal and lesion-bearing breasts by using a combined optical and digital breast tomosynthesis (DBT) imaging system. MATERIALS AND METHODS: Institutional review board approval and patient informed consent were obtained for this HIPAA-compliant study. Combined optical and tomosynthesis imaging analysis was performed in 189 breasts from 125 subjects (mean age, 56 years ± 13 [standard deviation]), including 138 breasts with negative findings and 51 breasts with lesions. Three-dimensional (3D) maps of total hemoglobin concentration (Hb(T)), oxygen saturation (So(2)), and tissue reduced scattering coefficients were interpreted by using the coregistered DBT images. Paired and unpaired t tests were performed between various tissue types to identify significant differences. RESULTS: The estimated average bulk Hb(T) from 138 normal breasts was 19.2 µmol/L. The corresponding mean So(2) was 0.73, within the range of values in the literature. A linear correlation (R = 0.57, P < .0001) was found between Hb(T) and the fibroglandular volume fraction derived from the 3D DBT scans. Optical reconstructions of normal breasts revealed structures corresponding to chest-wall muscle, fibroglandular, and adipose tissues in the Hb(T), So(2), and scattering images. In 26 malignant tumors of 0.6-2.5 cm in size, Hb(T) was significantly greater than that in the fibroglandular tissue of the same breast (P = .0062). Solid benign lesions (n = 17) and cysts (n = 8) had significantly lower Hb(T) contrast than did the malignant lesions (P = .025 and P = .0033, respectively). CONCLUSION: The optical and DBT images were structurally consistent. The malignant tumors and benign lesions demonstrated different Hb(T) and scattering contrasts, which can potentially be exploited to reduce the false-positive rate of conventional mammography and unnecessary biopsies.

Methods for compensating for variable electrode contact in EIT.

Electrical impedance tomography (EIT) is an imaging modality that currently shows promise for the detection and characterization of breast cancer. A very significant problem in EIT imaging is the proper modeling of the interface between the body and the electrodes. We have found empirically that it is very difficult, in a clinical setting, to assure that all electrodes make satisfactory contact with the body. In addition, we have observed a capacitive effect at the skin/electrode boundary that is spatially heterogeneous. To compensate for these problems, we have developed a hybrid nonlinear-linear reconstruction algorithm using the complete electrode model in which we first estimate electrode surface impedances, by means of a Levenberg-Marquardt iterative optimization procedure with an analytically computed Jacobian matrix. We, subsequently, use a linearized algorithm to perform a 3-D reconstruction of perturbations in both contact impedances, and in the spatial distributions of conductivity and permittivity. Results show that, with this procedure, artifacts due to electrodes making poor contact can be greatly reduced. If the experimental apparatus physically applies voltages and measures currents, we show that it is preferable to compute the reconstruction with respect to the Dirichlet-to-Neumann map rather than the Neumann-to-Dirichlet map if there is a significant possibility that electrodes will be fully disconnected. Finally, we test our electrode compensation algorithms for a set of clinical data, showing that we can significantly improve the fit of our model to the measurements by allowing the electrode surface impedances to vary.

A two-layered forward model of tissue for electrical impedance tomography.

Electrical impedance tomography is being explored as a technique to detect breast cancer, exploiting the differences in admittivity between normal tissue and tumors. In this paper, the geometry is modeled as an infinite half space under a hand-held probe. A forward solution and a reconstruction algorithm for this geometry were developed previously by Mueller et al (1999 IEEE Trans. Biomed. Eng. 46 1379). In this paper, we present a different approach which uses the decomposition of the forward solution into its Fourier components to obtain the forward solution and the reconstructions. The two approaches are compared in terms of the forward solutions and the reconstructions of experimental tank data. We also introduce a two-layered model to incorporate the presence of the skin that surrounds the body area being imaged. We demonstrate an improvement in the reconstruction of a target in a layered medium using this layered model with finite difference simulated data. We then extend the application of our layered model to human subject data and estimate the skin and the tissue admittivities for data collected on the human abdomen using an ultrasound-like hand-held EIT probe. Lastly, we show that for this set of human subject data, the layered model yields an improvement in predicting the measured voltages of around 81% for the lowest temporal frequency (3 kHz) and around 61% for the highest temporal frequency (1 MHz) applied when compared to the homogeneous model.

An implementation of CalderOn's method for 3-D limited-view EIT.

Mathematical interest in electrical impedance tomography has been strong since the publication of CalderOn's foundational paper. This paper introduced the idea of applying external voltage patterns to a medium such that, assuming that the medium is sufficiently close to a constant admittivity, the reconstruction can be accomplished directly by inverse Fourier transform. Motivated by CalderOn's method, we have developed a variant of the algorithm which is applicable to the case of measurement on only a part of the boundary and on discrete electrodes. Here we determine voltage or current patterns to apply to the electrodes which optimally approximate CalderOn's special functions in the interior. Furthermore, in three dimensions and higher, CalderOn's method allows each point in Fourier space to be computed in a multiplicity of ways. We show that by making use of the inherent redundancy in our measurements, we can significantly improve the quality of the static images produced by our algorithm.

Regional admittivity spectra with tomosynthesis images for breast cancer detection: preliminary patient study.

It has been known for some time that many tumors have a significantly different conductivity and permittivity from surrounding normal tissue. This high "contrast" in tissue electrical properties, occurring between a few kilohertz and several megahertz, may permit differentiating malignant from benign tissues. Here we show the ability of electrical impedance spectroscopy (EIS) to roughly localize and clearly distinguish cancers from normal tissues and benign lesions. Localization of these lesions is confirmed by simultaneous, in register digital breast tomosynthesis (DBT) mammography or 3-D mammograms.

Robust linearized image reconstruction for multifrequency EIT of the breast.

Electrical impedance tomography (EIT) is a developing imaging modality that is beginning to show promise for detecting and characterizing tumors in the breast. At Rensselaer Polytechnic Institute, we have developed a combined EIT-tomosynthesis system that allows for the coregistered and simultaneous analysis of the breast using EIT and X-ray imaging. A significant challenge in EIT is the design of computationally efficient image reconstruction algorithms which are robust to various forms of model mismatch. Specifically, we have implemented a scaling procedure that is robust to the presence of a thin highly-resistive layer of skin at the boundary of the breast and we have developed an algorithm to detect and exclude from the image reconstruction electrodes that are in poor contact with the breast. In our initial clinical studies, it has been difficult to ensure that all electrodes make adequate contact with the breast, and thus procedures for the use of data sets containing poorly contacting electrodes are particularly important. We also present a novel, efficient method to compute the Jacobian matrix for our linearized image reconstruction algorithm by reducing the computation of the sensitivity for each voxel to a quadratic form. Initial clinical results are presented, showing the potential of our algorithms to detect and localize breast tumors.

An analytical layered forward model for breasts in electrical impedance tomography.

Electrical impedance tomography (EIT) can be used to determine the admittivity distribution within the breast from measurements made on its surface. It has been reported that the electrical impedance spectrum of normal breast tissue is significantly different from that of malignant tissue, making EIT a candidate technology for breast cancer detection. The inhomogeneous structure of breasts, with thin low-admittivity skin layers covering the relatively high-admittivity tissue inside, makes the breast imaging problem difficult. In addition, studies show that the electrical properties of skin vary considerably over frequency. This paper proposes a layered forward model which incorporates the presence of skin. Our layered model has three layers, thin low-admittivity top and bottom layers representing skin and a thicker high-admittivity middle layer representing breast tissue. We solve for the forward solution of the layered geometry and compare its behavior with the previously used homogeneous model. Next we develop an iterative method to estimate the skin and breast tissue admittivities from the measured data, and study the robustness and accuracy of the method for various simulated and experimental data. We then look at the reconstruction of a target embedded in a layered body when the homogeneous forward solution is replaced by the layered forward solution. Lastly, we demonstrate the improvement that the layered forward model produces over the homogeneous model when working with clinical data.

Estimation and statistical bounds for three-dimensional polar shapes in diffuse optical tomography.

Voxel-based reconstructions in diffuse optical tomography (DOT) using a quadratic regularization functional tend to produce very smooth images due to the attenuation of high spatial frequencies. This then causes difficulty in estimating the spatial extent and contrast of anomalous regions such as tumors. Given an assumption that the target image is piecewise constant, we can employ a parametric model to estimate the boundaries and contrast of an inhomogeneity directly. In this paper, we describe a method to directly reconstruct such a shape boundary from diffuse optical measurements. We parameterized the object boundary using a spherical harmonic basis, and derived a method to compute sensitivities of measurements with respect to shape parameters. We introduced a centroid constraint to ensure uniqueness of the combined shape/center parameter estimate, and a projected Newton method was utilized to optimize the object center position and shape parameters simultaneously. Using the shape Jacobian, we also computed the Cramér-Rao lower bound on the theoretical estimator accuracy given a particular measurement configuration, object shape, and level of measurement noise. Knowledge of the shape sensitivity matrix and of the measurement noise variance allows us to visualize the shape uncertainty region in three dimensions, giving a confidence region for our shape estimate. We have implemented our shape reconstruction method, using a finite-difference-based forward model to compute the forward and adjoint fields. Reconstruction results are shown for a number of simulated target shapes, and we investigate the problem of model order selection using realistic levels of measurement noise. Assuming a signal-to-noise ratio in the amplitude measurements of 40 dB and a standard deviation in the phase measurements of 0.1 degrees , we are able to estimate an object represented with an eighth-order spherical harmonic model having an absorption contrast of 0.15 cm(-1) and a volume of 4.82 cm(3) with errors of less than 10% in object volume and absorption contrast. We also investigate the robustness of our shape-based reconstruction approach to a violation of the assumption that the medium is purely piecewise constant.

The complete electrode model for imaging and electrode contact compensation in electrical impedance tomography.

Electrical Impedance Tomography (EIT) is an imaging modality which currently shows promise for the detection and characterization of breast cancer. A very significant problem in EIT imaging is the proper modeling of the interface between the body and the electrodes. We have found empirically that it is very difficult, in a clinical setting, to assure that all electrodes make satisfactory contact with the body. In addition, we have observed a capacitive effect at skin/electrode boundary that is spatially heterogeneous. To compensate for these problems, we have developed a hybrid nonlinear-linear reconstruction algorithm in which we first estimate electrode surface impedances, using a Newton-type iterative optimization procedure with an analytically computed Jacobian matrix. We subsequently make use of a linearized algorithm to perform a three-dimensional reconstruction of perturbations in both contact impedances and in the spatial distributions of conductivity and permittivity. Results show that, using this procedure, artifacts due to electrodes making poor contact can be greatly reduced.

Regional admittivity spectra with tomosynthesis images for breast cancer detection.

Because the electrical properties of many breast tumors are different from those of surrounding, normal tissue, imaging these properties may provide useful diagnostic information. At the present time, X-ray mammography is the standard imaging modality used for breast cancer screening. The interpretation of EIT imaging is thus enhanced by its use together with x-ray mammography in the same geometry. This paper reports the ability of Electrical Impedance Spectroscopy (EIS) to localize and distinguish cancers from normal tissues. These findings are confirmed by simultaneous, co-registered 3-D mammograms or tomosynthesis images and are verified with biopsy reports.

Layered model for breasts in electrical impedance tomography.

We are presently using Electrical Impedance Tomography as a technique for breast cancer imaging, determining the admittivity distribution inside the breast. The admittivities we observed in compressed breasts in EIT were lower than those seen in earlier studies involving whole chest imaging. We attribute this to a thin low admittance skin layer which dominates in compressed breasts. To more accurately model breasts, we have developed a layered analytical forward model. Our layered model has three layers, thin low admittivity top and bottom layers representing skin and a thicker high admittivity middle layer representing breast tissue. In this paper we derive the forward solution for this layered geometry and compare it to the forward solution for the homogeneous case. We also demonstrate the improvement in reconstruction of a target embedded in a layered body when the homogeneous forward solution is replaced by the layered forward solution.