Left-ventricular unloading in extracorporeal cardiopulmonary resuscitation due to acute myocardial infarction - A multicenter study.

BACKGROUND: Guidelines advocate the use of extracorporeal cardio-pulmonary resuscitation with veno-arterial extracorporeal membrane oxygenation (VA-ECMO) in selected patients with cardiac arrest. Effects of concomitant left-ventricular (LV) unloading with Impella® (ECMELLA) remain unclear. This is the first study to investigate whether treatment with ECMELLA was associated with improved outcomes in patients with refractory cardiac arrest caused by acute myocardial infarction (AMI). METHODS: This study was approved by the local ethical committee. Patients treated with ECMELLA at three centers between 2016 and 2021 were propensity score (PS)-matched to patients receiving VA-ECMO based on age, electrocardiogram rhythm, cardiac arrest location and Survival After Veno-Arterial ECMO (SAVE) score. Cox proportional-hazard and Poisson regression models were used to analyse 30-day mortality rate (primary outcome), hospital and intensive care unit (ICU) length of stay (LOS) (secondary outcomes). Sensitivity analyses on patient demographics and cardiac arrest parameters were performed. RESULTS: 95 adult patients were included in this study, out of whom 34 pairs of patients were PS-matched. ECMELLA treatment was associated with decreased 30-day mortality risk (Hazard Ratio [HR] 0.53 [95% Confidence Interval (CI) 0.31-0.91], P = 0.021), prolonged hospital (Incidence Rate Ratio (IRR) 1.71 [95% CI 1.50-1.95], P < 0.001) and ICU LOS (IRR 1.81 [95% CI 1.57-2.08], P < 0.001). LV ejection fraction significantly improved until ICU discharge in the ECMELLA group. Especially patients with prolonged low-flow time and high initial lactate benefited from additional LV unloading. CONCLUSIONS: LV unloading with Impella® concomitant to VA-ECMO therapy in patients with therapy-refractory cardiac arrest due to AMI was associated with improved patient outcomes.

Olfactory Navigation and the Receptor Nonlinearity.

The demands on a sensory system depend not only on the statistics of its inputs but also on the task. In olfactory navigation, for example, the task is to find the plume source; allocation of sensory resources may therefore be driven by aspects of the plume that are informative about source location, rather than concentration per se. Here we explore the implications of this idea for encoding odor concentration. To formalize the notion that sensory resources are limited, we considered coding strategies that partitioned the odor concentration range into a set of discriminable intervals. We developed a dynamic programming algorithm that, given the distribution of odor concentrations at several locations, determines the partitioning that conveys the most information about location. We applied this analysis to planar laser-induced fluorescence measurements of spatiotemporal odor fields with realistic advection speeds (5-20 cm/s), with or without a nearby boundary or obstacle. Across all environments, the optimal coding strategy allocated more resources (i.e., more and finer discriminable intervals) to the upper end of the concentration range than would be expected from histogram equalization, the optimal strategy if the goal were to reconstruct the plume, rather than to navigate. Finally, we show that ligand binding, as captured by the Hill equation, transforms odorant concentration into response levels in a way that approximates information maximization for navigation. This behavior occurs when the Hill dissociation constant is near the mean odor concentration, an adaptive set-point that has been observed in the olfactory system of flies.SIGNIFICANCE STATEMENT The first step of olfactory processing is receptor binding, and the resulting relationship between odorant concentration and the bound receptor fraction is a saturating one. While this Hill nonlinearity can be viewed as a distortion that is imposed by the biophysics of receptor binding, here we show that it also plays an important information-processing role in olfactory navigation. Specifically, by combining a novel dynamic-programming algorithm with physical measurements of turbulent plumes, we determine the optimal strategy for encoding odor concentration when the goal is to determine location. This strategy is distinct from histogram equalization, the strategy that maximizes information about plume concentration, and is closely approximated by the Hill nonlinearity when the binding constant is near the ambient mean.

Information-theoretic analysis of realistic odor plumes: What cues are useful for determining location?

Many species rely on olfaction to navigate towards food sources or mates. Olfactory navigation is a challenging task since odor environments are typically turbulent. While time-averaged odor concentration varies smoothly with the distance to the source, instaneous concentrations are intermittent and obtaining stable averages takes longer than the typical intervals between animals' navigation decisions. How to effectively sample from the odor distribution to determine sampling location is the focus in this article. To investigate which sampling strategies are most informative about the location of an odor source, we recorded three naturalistic stimuli with planar lased-induced fluorescence and used an information-theoretic approach to quantify the information that different sampling strategies provide about sampling location. Specifically, we compared multiple sampling strategies based on a fixed number of coding bits for encoding the olfactory stimulus. When the coding bits were all allocated to representing odor concentration at a single sensor, information rapidly saturated. Using the same number of coding bits in two sensors provides more information, as does coding multiple samples at different times. When accumulating multiple samples at a fixed location, the temporal sequence does not yield a large amount of information and can be averaged with minimal loss. Furthermore, we show that histogram-equalization is not the most efficient way to use coding bits when using the olfactory sample to determine location.