CPR-related injuries after non-traumatic out-of-hospital cardiac arrest: Survivors versus non-survivors.

AIM: There have been no direct comparisons of cardiopulmonary resuscitation (CPR)-related injuries between those who die during CPR and those who survive to intensive care unit (ICU) admission. This study aimed to compare the incidence, severity, and impact on survival rate of these injuries and potential influencing factors. METHOD: This retrospective multicenter study analyzed autopsy reports of patients who experienced out-of-hospital cardiac arrest (OHCA) and were not admitted to hospital. CPR-related injuries were compared to OHCA patients with clinical suspicion of CPR-related injury confirmed on imaging when admitted to the ICU. RESULTS: A total of 859 out-of-hospital cardiac arrests (OHCA) were divided into 2 groups: those who died during CPR and underwent autopsy (DEAD [n = 628]); and those who experienced return of spontaneous circulation and admitted to the ICU (ICU [n = 231]). Multivariable analyses revealed that independent factors of 30-day mortality included no bystander arrest, cardiac etiology, no shockable rhythm, and CPR-related injury. Trauma was independently associated with older age, bystander CPR, cardiac etiology, duration of CPR, and no defibrillation. CPR-related injury occurred in 30 (13%) patients in the ICU group and 547 (87%) in the DEAD group (p < 0.0001). Comparison of injuries revealed that those in the DEAD group experienced more thoracic injuries, rib(s) and sternal fractures, and fewer liver injuries compared to those in the ICU group, without differences in injury severity. CONCLUSION: CPR-related injuries were observed more frequently in those who died compared with those who survived to ICU admission. Injury was an independent factor of 30-day mortality.

From diffusion in compartmentalized media to non-Gaussian random walks.

In this work we establish a link between two different phenomena that were studied in a large and growing number of biological, composite and soft media: the diffusion in compartmentalized environment and the non-Gaussian diffusion that exhibits linear or power-law growth of the mean square displacement joined by the exponential shape of the positional probability density. We explore a microscopic model that gives rise to transient confinement, similar to the one observed for hop-diffusion on top of a cellular membrane. The compartmentalization of the media is achieved by introducing randomly placed, identical barriers. Using this model of a heterogeneous medium we derive a general class of random walks with simple jump rules that are dictated by the geometry of the compartments. Exponential decay of positional probability density is observed and we also quantify the significant decrease of the long time diffusion constant. Our results suggest that the observed exponential decay is a general feature of the transient regime in compartmentalized media.

Time-series methods in analysis of the optical tweezers recordings.

In this paper we treat optical tweezers as discrete-time linear filters and analyze the recorded trajectories of the trapped beads using time-series methods. Using these techniques we obtain a simple analytical formula for the aliased power-spectrum density. Moreover, we separate influences of the noise and blur induced by the video camera from the physical content of the measurements, providing simple tools to detect and account for these distortions. Finally, checking how our tools work on the real data, we identify what parameters of video camera calibration the blur is dominating and what the additive noise is dominating. We also detect a range where these two distortions cancel each other so that the data can be mistakenly classified as undisturbed.

From physical linear systems to discrete-time series. A guide for analysis of the sampled experimental data.

Modeling physical data with linear discrete-time series, namely, the autoregressive fractionally integrated moving average (ARFIMA) model, is a technique that has attracted attention in recent years. However, this model is used mainly as a statistical tool only, with weak emphasis on the physical background of the model. The main reason for this lack of attention is that the ARFIMA model describes discrete-time measurements, whereas physical models are formulated using continuous-time parameters. In order to eliminate this discrepancy, we show that time series of this type can be regarded as sampled trajectories of the coordinates governed by a system of linear stochastic differential equations with constant coefficients. The observed correspondence provides formulas linking ARFIMA parameters and the coefficients of the underlying physical stochastic system, thus providing a bridge between continuous-time linear dynamical systems and ARFIMA models.

Moving average process underlying the holographic-optical-tweezers experiments.

We study the statistical properties of recordings that contain time-dependent positions of a bead trapped in optical tweezers. Analysis of such a time series indicates that the commonly accepted model, i.e., the autoregressive process of first-order, is not sufficient to fit the data. We show the presence of a first-order moving average part in the dynamical model of the system. We explain the origin of this part as an influence of the high-frequency CCD camera on the measurements. We show that this influence evidently depends on the applied exposure time. The proposed autoregressive moving average model appears to reflect perfectly all statistical features of the high-frequency recording data.