Cardiac biomarkers and clinical scores for risk stratification in elderly patients with non-high-risk pulmonary embolism.

OBJECTIVE To determine the prognostic accuracy of cardiac biomarkers alone and in combination with clinical scores in elderly patients with non-high-risk pulmonary embolism (PE). DESIGN Ancillary analysis of a Swiss multicentre prospective cohort study. SUBJECTS A total of 230 patients aged ≥65 years with non-high-risk PE. MAIN OUTCOME MEASURES The study end-point was a composite of PE-related complications, defined as PE-related death, recurrent venous thromboembolism or major bleeding during a follow-up of 30 days. The prognostic accuracy of the Pulmonary Embolism Severity Index (PESI), the Geneva Prognostic Score (GPS), the precursor of brain natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) was determined using sensitivity, specificity, predictive values, receiver operating characteristic (ROC) curve analysis, logistic regression and reclassification statistics. RESULTS The overall complication rate during follow-up was 8.7%. hs-cTnT achieved the highest prognostic accuracy [area under the ROC curve: 0.75, 95% confidence interval (CI): 0.63-0.86, P < 0.001). At the predefined cut-off values, the negative predictive values of the biomarkers were above 95%. For levels above the cut-off, the risk of complications increased fivefold for hs-cTnT [odds ratio (OR): 5.22, 95% CI: 1.49-18.25] and 14-fold for NT-proBNP (OR: 14.21, 95% CI: 1.73-116.93) after adjustment for both clinical scores and renal function. Reclassification statistics indicated that adding hs-cTnT to the GPS or the PESI significantly improved the prognostic accuracy of both clinical scores. CONCLUSION In elderly patients with nonmassive PE, NT-proBNP or hs-cTnT could be an adequate alternative to clinical scores for identifying low-risk individuals suitable for outpatient management.

Methodology for GPS Synchronization Evaluation with High Accuracy

Clock synchronization in the order of nanoseconds is one of the critical factors for time-based localization. Currently used time synchronization methods are developed for the more relaxed needs of network operation. Their usability for positioning should be carefully evaluated. In this paper, we are particularly interested in GPS-based time synchronization. To judge its usability for localization we need a method that can evaluate the achieved time synchronization with nanosecond accuracy. Our method to evaluate the synchronization accuracy is inspired by signal processing algorithms and relies on fine grain time information. The method is able to calculate the clock offset and skew between devices with nanosecond accuracy in real time. It was implemented using software defined radio technology. We demonstrate that GPS-based synchronization suffers from remaining clock offset in the range of a few hundred of nanoseconds but the clock skew is negligible. Finally, we determine a corresponding lower bound on the expected positioning error.

Comparison of GOCE-GPS gravity fields derived by different approaches

Several techniques have been proposed to exploit GNSS-derived kinematic orbit information for the determination of long-wavelength gravity field features. These methods include the (i) celestial mechanics approach, (ii) short-arc approach, (iii) point-wise acceleration approach, (iv) averaged acceleration approach, and (v) energy balance approach. Although there is a general consensus that—except for energy balance—these methods theoretically provide equivalent results, real data gravity field solutions from kinematic orbit analysis have never been evaluated against each other within a consistent data processing environment. This contribution strives to close this gap. Target consistency criteria for our study are the input data sets, period of investigation, spherical harmonic resolution, a priori gravity field information, etc. We compare GOCE gravity field estimates based on the aforementioned approaches as computed at the Graz University of Technology, the University of Bern, the University of Stuttgart/Austrian Academy of Sciences, and by RHEA Systems for the European Space Agency. The involved research groups complied with most of the consistency criterions. Deviations only occur where technical unfeasibility exists. Performance measures include formal errors, differences with respect to a state-of-the-art GRACE gravity field, (cumulative) geoid height differences, and SLR residuals from precise orbit determination of geodetic satellites. We found that for the approaches (i) to (iv), the cumulative geoid height differences at spherical harmonic degree 100 differ by only ≈10 % ; in the absence of the polar data gap, SLR residuals agree by ≈96 % . From our investigations, we conclude that real data analysis results are in agreement with the theoretical considerations concerning the (relative) performance of the different approaches.

GOCE: assessment of GPS-only gravity field determination

The GOCE satellite was orbiting the Earth in a Sun-synchronous orbit at a very low altitude for more than 4 years. This low orbit and the availability of high-quality data make it worthwhile to assess the contribution of GOCE GPS data to the recovery of both the static and time-variable gravity fields. We use the kinematic positions of the official GOCE precise science orbit (PSO) product to perform gravity field determination using the Celestial Mechanics Approach. The generated gravity field solutions reveal severe systematic errors centered along the geomagnetic equator. Their size is significantly coupled with the ionospheric density and thus generally increasing over the mission period. The systematic errors may be traced back to the kinematic positions of the PSO product and eventually to the ionosphere-free GPS carrier phase observations used for orbit determination. As they cannot be explained by the current higher order ionospheric correction model recommended by the IERS Conventions 2010, an empirical approach is presented by discarding GPS data affected by large ionospheric changes. Such a measure yields a strong reduction of the systematic errors along the geomagnetic equator in the gravity field recovery, and only marginally reduces the set of useable kinematic positions by at maximum 6 % for severe ionosphere conditions. Eventually it is shown that GOCE gravity field solutions based on kinematic positions have a limited sensitivity to the largest annual signal related to land hydrology.

Satellite laser ranging to GPS and GLONASS

Satellite laser ranging (SLR) to the satellites of the global navigation satellite systems (GNSS) provides substantial and valuable information about the accuracy and quality of GNSS orbits and allows for the SLR-GNSS co-location in space. In the framework of the NAVSTAR-SLR experiment two GPS satellites of Block-IIA were equipped with laser retroreflector arrays (LRAs), whereas all satellites of the GLONASS system are equipped with LRAs in an operational mode. We summarize the outcome of the NAVSTAR-SLR experiment by processing 20 years of SLR observations to GPS and 12 years of SLR observations to GLONASS satellites using the reprocessed microwave orbits provided by the center for orbit determination in Europe (CODE). The dependency of the SLR residuals on the size, shape, and number of corner cubes in LRAs is studied. We show that the mean SLR residuals and the RMS of residuals depend on the coating of the LRAs and the block or type of GNSS satellites. The SLR mean residuals are also a function of the equipment used at SLR stations including the single-photon and multi-photon detection modes. We also show that the SLR observations to GNSS satellites are important to validate GNSS orbits and to assess deficiencies in the solar radiation pressure models. We found that the satellite signature effect, which is defined as a spread of optical pulse signals due to reflection from multiple reflectors, causes the variations of mean SLR residuals of up to 15 mm between the observations at nadir angles of 0∘ and 14∘. in case of multi-photon SLR stations. For single-photon SLR stations this effect does not exceed 1 mm. When using the new empirical CODE orbit model (ECOM), the SLR mean residual falls into the range 0.1–1.8 mm for high-performing single-photon SLR stations observing GLONASS-M satellites with uncoated corner cubes. For best-performing multi-photon stations the mean SLR residuals are between −12.2 and −25.6 mm due to the satellite signature effect.

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Real-time localization using software defined radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.