Epidemiological trends in nosocomial candidemia in intensive care

BACKGROUND: Infection represents a frequent complication among patients in Intensive Care Units (ICUs) and mortality is high. In particular, the incidence of fungal infections, especially due to Candida spp., has been increasing during the last years. METHODS: In a retrospective study we studied the etiology of candidemia in critically ill patients over a five-year period (1999–2003) in the ICU of the San Martino University Hospital in Genoa, Italy. RESULTS: In total, 182 episodes of candidaemia were identified, with an average incidence of 2.22 episodes/10 000 patient-days/year (range 1.25–3.06 episodes). Incidence of candidemia increased during the study period from 1.25 in 1999 to 3.06/10 000 patient-days/year in 2003. Overall, 40% of the fungemia episodes (74/182) were due to C.albicans, followed by C. parapsilosis(23%), C.glabrata (15%), C.tropicalis (9%) and other species (13%). Candidemia due to non-albicans species increased and this was apparently correlated with an increasing use of azoles for prophylaxis or empirical treatment. CONCLUSION: The study demonstrates a shift in the species of Candida causing fungemia in a medical and surgical ICU population during a 5 year period. The knowledge of the local epidemiological trends in Candida species isolated in blood cultures is important to guide therapeutic choices

Epidemiology, Species Distribution, Antifungal Susceptibility and Outcome of Nosocomial Candidemia in a Tertiary Care Hospital in Italy

Candida is an important cause of bloodstream infections (BSI), causing significant mortality and morbidity in health care settings. From January 2008 to December 2010 all consecutive patients who developed candidemia at San Martino University Hospital, Italy were enrolled in the study. A total of 348 episodes of candidaemia were identified during the study period (January 2008–December 2010), with an incidence of 1,73 episodes/1000 admissions. Globally, albicans and non-albicans species caused around 50% of the cases each. Non-albicans included Candida parapsilosis (28.4%), Candida glabrata (9.5%), Candida tropicalis (6.6%), and Candida krusei (2.6%). Out of 324 evaluable patients, 141 (43.5%) died within 30 days from the onset of candidemia. C. parapsilosis candidemia was associated with the lowest mortality rate (36.2%). In contrast, patients with C. krusei BSI had the highest mortality rate (55.5%) in this cohort. Regarding the crude mortality in the different units, patients in Internal Medicine wards had the highest mortality rate (54.1%), followed by patients in ICU and Hemato-Oncology wards (47.6%)

Estimation of Time Series Models via Robust Wavelet Variance

A robust approach to the estimation of time series models is proposed. Taking from a new estimation method called the Generalized Method of Wavelet Moments (GMWM) which is an indirect method based on the Wavelet Variance (WV), we replace the classical estimator of the WV with a recently proposed robust M-estimator to obtain a robust version of the GMWM. The simulation results show that the proposed approach can be considered as a valid robust approach to the estimation of time series and state-space models

Robust Inference for Time Series Models: a Wavelet-Based Framework

We present a new framework for the robust estimation of time series models which is fairly general and, for example, covers models going from ARMA to state-space models. This approach provides estimators which are (i) consistent and asymptotically normally distributed, (ii) applicable to a broad spectrum of time series models, (iii) straightforward to implement and (iv) computationally efficient. The framework is based on the recently developed Generalized Method of Wavelet Moments and a new robust estimator of the wavelet variance. Compared to existing methods, the latter directly estimates the quantity of interest while performing better in finite samples and using milder conditions for its asymptotic properties to hold. Hence, not only does this paper provide an alternative estimator which allows to perform wavelet variance analysis when data are contaminated but also a general approach to robustly estimate the parameters of a variety of time series models. The simulation studies carried out confirm the better performance of the proposed estimators and the usefulness and broadness of the proposed methodology is shown using practical examples from the domains of hydrology and engineering with sample sizes up to 500,000

An Algorithm for Automatic Inertial Sensors Calibration : Proceedings of the ION GNSS 2013

We present an algorithm for determining the nature of stochastic processes together with its parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations when several stochastic processes are superposed. In such cases, classical approaches based on the analysis of Allan variance or PSD are likely to fail due to the difficulty of separating the underlying error-processes in the spectral domain. The developed alternative is based on the recently proposed method called the Generalized Method of Wavelet Moments (GMWM), whose resulting estimator was proven to be consistent and asymptotically normally distributed. The principle of this method is to match the empirical and model-based wavelet variances (WV). In this study we propose a goodness-of-fit criterion which can be used to determine the suitability of a candidate model and apply it to low-cost inertial sensors. The suggested approach of model selection relies on an unbiased estimate of the distance between the theoretical WV and the empirical WV which would be obtained on an independent sample issued from the stochastic process of interest. Such goodness-of-fit criterion is however “penalized” by the complexity of the model. In some sense, the proposed methodology is a generalization of Mallow's Cp applied to models estimated by the GMWM. By allowing to rank candidate models, this approach permits to construct an algorithm for automatic model identification and determination. The benefits of this methodology are highlighted by providing practical examples of model selection for two types of MEMS-IMUs, the latter of higher quality

An algorithm for automatic inertial sensor calibration

We present an algorithm for determining the nature of stochastic processes together with its parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations when several stochastic processes are superposed. In such cases, classical approaches based on the analysis of Allan variance or PSD are likely to fail due to the difficulty of separating the underlying error-processes in the spectral domain. The developed alternative is based on the recently proposed method called the Generalized Method of Wavelet Moments (GMWM), which estimator was proven to be consistent and asymptotically normally distributed. The principle of this method is to match the empirical and model-based wavelet variances (WV). In this study we propose a goodness-of-fit criterion which can be used to determine the suitability of a model candidate and apply it to low-cost inertial sensors. The suggested approach of model selection relies on an unbiased estimate of the distance between the theoretical WV and the empirical WV which would be obtained on an independent sample issued from the stochastic process of interest. Such goodness-of-fit criterion is however “penalized” by the complexity of the model. In some sense, the proposed methodology is a generalization of Mallow’s Cp applied to models estimated by the GMWM. By allowing to rank candidate models, this approach permits to construct an algorithm for automatic model identification and determination. The benefits of this methodology are highlighted by providing practical examples of model selection for two types of MEMS- IMUs, the latter of higher quality

Study of mems-based inertial sensors operating in dynamic conditions

This paper aims at studying the behaviour of the errors coming from inertial sensors when measured in dynamic conditions. After proposing a method for constructing the error process, the properties of these errors are estimated via the Generalized Method of Wavelets Moments methodology. The developed model parameters are compared to those obtained under static conditions. Finally an attempted is presented to find the link between the encountered dynamic of the vehicle and error-model parameters

Damage Tolerance Assessment of Near Edge Impacts in CFRP Structures

This work deals with the experimental evaluation of near-edge impacts on the damage tolerant properties of CFRP structures. Though many examples of central impacts exist in literature, very few can be found regarding edge impacts. This experimental activity had the objective of residual strength characterization of the damaged coupons in comparison to the pristine ones. Different types of damages were inserted in the specimens (transverse and near-edge impact) for two different impact energy levels (3J and 5J). Subsequently, compression after impact tests were performed. A total of 25 specimens were tested. The results of this campaign indicate the higher criticality of near-edge impacts in respect to central impacts and they contribute to better understanding of the behaviour of CFRP materials subject to impact loads

Number of death and 30 days/mortality for various <i>Candida spp</i>. and in the different hospital wards.

<p>Number of death and 30 days/mortality for various <i>Candida spp</i>. and in the different hospital wards.</p