Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

[EN] Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.This work received financial support from Spanish Ministerio de Economia y Competitividad and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), projects TEC2015-64678-R and DPI2017-83952-C3; from UPV/EHU through the grant PIF15/190 and through project GIU17/031; from the Basque Government through grant PRE-2016-1-0012; and from Junta de Comunidades de Castilla-La Mancha through SBPLY/17/180501/000411.Chicote, B.; Irusta, U.; Aramendi, E.; Alcaraz, R.; Rieta, JJ.; Isasi, I.; Alonso, D.... (2018). 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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 μV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.This work received financial support from Spanish Ministerio de Economia y Competitividad, projects TEC2013-31928 and TEC2014-52250-R, and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R; from Junta de Comunidades de Castilla La Mancha, project PPII-2014-026-P; and from UPV/EHU through the grant PIF15/190 and through its research unit UFI11/16.Chicote, B.; Irusta, U.; Alcaraz, R.; Rieta, JJ.; Aramendi, E.; Isasi, I.; Alonso, D.... (2016). Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy. 18(9):1-17. https://doi.org/10.3390/e18090313S11718

A predictive model for MSSW student success.

This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with the subjective experiences of academic stability and academic performance were studied. A hierarchical multiple regression analysis was used to determine the best predictors of final GPA. The best predictors were age, undergraduate GPA, differences between undergraduate and graduate institution size, continuity index, and the course completion ratio. A hierarchical logistic regression analysis was used to determine the best predictors of graduation with an MSSW degree. The best predictors were age, prerequisite classes, rural/metropolitan nature of hometown, continuity index, course completion ratio and full-time student status in the first semester. Potential interventions and policy changes are detailed at both entry into and during the MSSW program. There is a need for future research in subsequent years at the Kent School of Social Work and other schools of social work that offer Master\u27s degrees

Latent class growth analyses reveal overrepresentation of dysfunctional fear conditioning trajectories in patients with anxiety-related disorders compared to controls

Recent meta-analyses indicated differences in fear acquisition and extinction between patients with anxiety related disorders and comparison subjects. However, these effects are small and may hold for only a subsample of patients. To investigate individual trajectories in fear acquisition and extinction across patients with anxiety-related disorders (N = 104; before treatment) and comparison subjects (N = 93), data from a previous study (Duits et al., 2017) were re-analyzed using data-driven latent class growth analyses. In this explorative study, subjective fear ratings, shock expectancy ratings and startle responses were used as outcome measures. Fear and expectancy ratings, but not startle data, yielded distinct fear conditioning trajectories across participants. Patients were, compared to controls, overrepresented in two distinct dysfunctional fear conditioning trajectories: impaired safety learning and poor fear extinction to danger cues. The profiling of individual patterns allowed to determine that whereas a subset of patients showed trajectories of dysfunctional fear conditioning, a significant proportion of patients (?50 %) did not. The strength of trajectory analyses as opposed to group analyses is that it allows the identification of individuals with dysfunctional fear conditioning. Results suggested that dysfunctional fear learning may also be associated with poor treatment outcome, but further research in larger samples is needed to address this question

Giant Planet Formation, Evolution, and Internal Structure

The large number of detected giant exoplanets offers the opportunity to improve our understanding of the formation mechanism, evolution, and interior structure of gas giant planets. The two main models for giant planet formation are core accretion and disk instability. There are substantial differences between these formation models, including formation timescale, favorable formation location, ideal disk properties for planetary formation, early evolution, planetary composition, etc. First, we summarize the two models including their substantial differences, advantages, and disadvantages, and suggest how theoretical models should be connected to available (and future) data. We next summarize current knowledge of the internal structures of solar- and extrasolar- giant planets. Finally, we suggest the next steps to be taken in giant planet exploration.Comment: Accepted for publication as a chapter in Protostars and Planets VI, to be published in 2014 by University of Arizona Pres

Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients

Russian Roulette- Expenditure Inequality and Instability in Russia, 1994-1998

This paper uses the second phase of the Russian Longitudinal Monitoring Survey to investigate the changes in expenditure inequality and instability in Russia between the autumn of 1994 and the autumn of 1998. The expenditure distribution is stable in spite of the economic and political turmoil Russia is going through. However, that does not imply much economic stability. Households' expenditure fluctuated considerably, with over 60 percent of the population's expenditure either more than doubling or falling to less than half their previous levels. Only about 10 percent of all households experienced an expenditure shock of less than 10 percent. The measured level of expenditure mobility is very high. This raises the question whether the observed mobility is in fact the expenditure instability. Distinguishing between the two is crucial for policy makers. While the mobility is often viewed as favorable, the high instability may affect the incentives of Russians to support the economic reforms, acquire human capital, and undertake entrepreneurial activities.http://deepblue.lib.umich.edu/bitstream/2027.42/39742/3/wp358.pd