Long-term outcome of COVID-19 patients with acute kidney injury requiring kidney replacement therapy.

BACKGROUND: Limited data existed on the burden of coronavirus disease 2019 (COVID-19) renal complications and the outcomes of the most critical patients who required kidney replacement therapy (KRT) during intensive care unit (ICU) stay. We aimed to describe mortality and renal function at 90 days in patients admitted for COVID-19 and KRT. METHODS: A retrospective cohort study of critically ill patients admitted for COVID-19 and requiring KRT from March 2020 to January 2022 was conducted in an Italian ICU from a tertiary care hospital. Primary outcome was mortality at 90 days and secondary outcome was kidney function at 90 days. RESULTS: A cohort of 45 patients was analyzed. Mortality was 60% during ICU stay and increased from 64% at the time of hospital discharge to 71% at 90 days. Among 90-day survivors, 31% required dialysis, 38% recovered incompletely, and 31% completely recovered renal function. The probability of being alive and dialysis-free at 3 months was 22%. CONCLUSIONS: Critically ill patients with COVID-19 disease requiring KRT during ICU stay had elevated mortality rate at 90 days, with low probability of being alive and dialysis-free at 3 months. However, a non-negligible number of patients completely recovered renal function.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes.

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Sphaleron Rate of N_{f}=2+1 QCD.

We compute the sphaleron rate of N_{f}=2+1 QCD at the physical point for a range of temperatures 200 MeV≲T≲600 MeV. We adopt a strategy recently applied in the quenched case, based on the extraction of the rate via a modified version of the Backus-Gilbert method from finite-lattice-spacing and finite-smoothing-radius Euclidean topological charge density correlators. The physical sphaleron rate is finally computed by performing a continuum limit at fixed physical smoothing radius, followed by a zero-smoothing extrapolation. Dynamical fermions were discretized using the staggered formulation, which is known to yield large lattice artifacts for the topological susceptibility. However, we find them to be rather mild for the sphaleron rate.

Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals.

BACKGROUND: Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise. AIM: We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. METHODOLOGY: Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic maps that included autoregressive, logistic, and Pomeau-Manneville systems under various conditions. Noise estimation is performed on 70 heart rate variability series from healthy and pathological subjects, and 32 electroencephalographic (EEG) healthy series. RESULTS: Our results showed that the proposed model-free method can discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounts for around 11% of the overall power observed in EEG signals and approximately 32% to 65% of the power related to heartbeat dynamics. Cardiovascular noise increases in pathological conditions compared to healthy dynamics, and cortical brain noise increases during mental arithmetic computations over the prefrontal and occipital regions. Brain noise is differently distributed across cortical regions. CONCLUSION: Physiological noise is very part neurobiological dynamics and can be measured using the proposed framework in any biomedical series.

Characterization of Physiological Noise in Complex Cardiovascular Variability Series.

The cardiovascular system can be analyzed using spectral, nonlinear, and complexity metrics. Nevertheless, dynamical noise may significantly impact these quantifiers. To our knowledge, there has been no attempt to quantify the intrinsic cardiovascular system noise driving heartbeat dynamics. To this end, this study presents a novel, model-free framework to define and quantify physiological noise using nonlinear Approximate Entropy profile. The framework was tested using analytical noisy series and then applied to real Heart Rate Variability (HRV) series gathered from a publicly-available dataset of recordings from 19 young and 19 elderly subjects watching the movie "Fantasia". Results suggest that physiological noise may account for over 15% of cardiovascular dynamics and is influenced by aging, with decreased cardiac noise in the elderly compared to the young subjects. Our findings indicate that physiological noise is a crucial factor in characterizing cardiovascular dynamics, and current spectral, nonlinear, and complexity assessments should take into account underlying dynamical noise estimates.

Multiscale partition-based Kolmogorov-Sinai Entropy: a preliminary HRV study on Heart Failure vs. Atrial Fibrillation.

Several approaches for estimating complexity in physiological time series at various time scales have recently been developed, with a special focus on heart rate variability (HRV) series. While numerous multiscale complexity quantifiers have been investigated, a multiscale Kolmogorov-Sinai (K-S) entropy for the characterization of cardiovascular dynamics still has to be properly assessed. In this pilot study, we investigate the Algorithmic Information Content, which is calculated using an effective compression algorithm, to quantify multiscale partition- based K-S entropy on experimental HRV series. Data were gathered from publicly available datasets comprising long-term, unstructured recordings from 10 healthy subjects, as well as 10 patients with congestive heart failure (CHF) and 10 patients with atrial fibrillation. Results show that multiple time scales and domain partitions statistically discern healthy vs. pathological cardiovascular dynamics. We conclude that the proposed multiscale partition-based K-S entropy may constitute a viable tool for the complexity assessment of cardiovascular variability series.

A Multiscale Partition-Based Kolmogorov-Sinai Entropy for the Complexity Assessment of Heartbeat Dynamics.

BACKGROUND: Several methods have been proposed to estimate complexity in physiological time series observed at different time scales, with a particular focus on heart rate variability (HRV) series. In this frame, while several complexity quantifiers defined in the multiscale domain have already been investigated, the effectiveness of a multiscale Kolmogorov-Sinai (K-S) entropy has not been evaluated yet for the characterization of heartbeat dynamics. METHODS: The use of the algorithmic information content, which is estimated through an effective compression algorithm, is investigated to quantify multiscale partition-based K-S entropy on publicly available experimental HRV series gathered from young and elderly subjects undergoing a visual elicitation task (Fantasia). Moreover, publicly available HRV series gathered from healthy subjects, as well as patients with atrial fibrillation and congestive heart failure in unstructured conditions have been analyzed as well. RESULTS: Elderly people are associated with a lower HRV complexity and a more predictable cardiovascular dynamics, with significantly lower partition-based K-S entropy than the young adults. Major differences between these groups occur at partitions greater than six. In case of partition cardinality greater than 5, patients with congestive heart failure show a minimal predictability, while atrial fibrillation shows a higher variability, and hence complexity, which is actually reduced by the time coarse-graining procedure. CONCLUSIONS: The proposed multiscale partition-based K-S entropy is a viable tool to investigate complex cardiovascular dynamics in different physiopathological states.

Quantifying partition-based Kolmogorov-Sinai Entropy on Heart Rate Variability: a young vs. elderly study.

In the last decades, a considerable effort has been devoted to quantify complexity in physiological time series, with a particular focus on heart rate variability (HRV). To this end, exemplary quantifiers including Approximate Entropy and Sample Entropy have successfully been applied by leveraging on statistical approximation and further parametrization through the definition of tolerance and embedding dimension, among others. In this study, we investigate the use of the Algorithmic Information Content, which is estimated through an effective compression algorithm, to quantify partition-based Kolmogorov-Sinai (K-S) entropy on HRV series. We test such a K-S estimate on real data gathered from the Fantasia database, aiming to discern young vs. elderly complex dynamics. Experimental results show that elderly people are associated with a lower HRV complexity and a more predictable behavior, with significantly lower partition-based K-S entropy than the young adults. We conclude that partition-based K-S entropy may effectively be used to investigate pathological conditions in the cardiovascular system, complementing state-of-the-art methods for complexity assessment.

Pharmacokinetics of lidocaine after bilateral ESP block.

INTRODUCTION: Erector spinae plane (ESP) block is an emerging interfascial block with a wide range of indications for perioperative analgesia and chronic pain treatment. Recent studies have focused their attention on mechanisms of action of ESP block. However, the pharmacokinetics of drugs injected in ESP is, as of now, uninvestigated. The aim of this brief report is to investigate the pharmacokinetics of lidocaine in a series of 10 patients. METHODS: We are reporting a case series of 10 patients undergoing bilateral ESP block for multilevel lumbar spine surgery.ESP was performed with 3.5 mg/kg of lidocaine based on ideal body weight. Lidocaine concentration was dosed at 5, 15, 30 min and at 1, 2 and 3 hours. RESULTS: Tmax was 5 min for all the patients. Cmax ranged from 1.2 to 3.8 mg/L (mean: 2.59 mg/L). AUC0-3 was high (76%, on average) suggesting an almost complete bioavailability. Age had a negative correlation with T½ of lidocaine. CONCLUSIONS: Lidocaine pharmacokinetic after ESP block is well-described by a two-compartment model with a rapid and extensive rate of absorption. Nevertheless, its peak concentrations never exceeded the accepted toxicity limit. Elimination half-life was slightly prolonged, probably due to the advanced age of some patients.

PECS II block is associated with lower incidence of chronic pain after breast surgery.

Background: Breast cancer is complicated by a high incidence of chronic postoperative pain (25%-60%). Regional anesthesia might play an important role in lowering the incidence of chronic pain; however it is not known if the pectoral nerve block (PECS block), which is commonly used for breast surgery, is able to prevent this complication. Our main objective was therefore to detect any association between the PECS block and chronic pain at 3, 6, 9, and 12 months in patients undergoing breast surgery. Methods: We conducted a prospective, monocentric, observational study. We enrolled 140 consecutive patients undergoing breast surgery and divided them in patients receiving a PECS block and general anesthesia (PECS group) and patients receiving only general anesthesia (GA group). Then we considered both intraoperative variables (intravenous opioids administration), postoperative data (pain suffered by the patients during the first 24 postoperative hours and the need for additional analgesic administration) and development and persistence of chronic pain (at 3, 6, 9, and 12 mo). Results: The PECS group had a lower incidence of chronic pain at 3 months (14.9% vs. 31.8%, P = 0.039), needed less intraoperative opioids (fentanyl 1.61 µg/kg/hr vs. 3.3 µg/kg/hr, P < 0.001) and had less postoperative pain (3 vs. 4, P = 0.017). Conclusions: The PECS block might play an important role in lowering incidence of chronic pain, but further studies are needed.

Comparison of complications and outcomes to one year of transcatheter aortic valve implantation versus surgical aortic valve replacement in patients with severe aortic stenosis.

Comparisons of transcatheter aortic valve implantation (TAVI) to surgical aortic valve replacement (SAVR) in patients with severe aortic stenosis remain sparse or limited by a short follow-up. We sought to evaluate early and midterm outcomes of consecutive patients (n = 618) undergoing successful TAVI (n = 218) or isolated SAVR (n = 400) at 2 centers. The primary end point was incidence of Valvular Academic Research Consortium-defined major adverse cerebrovascular and cardiac events (MACCEs) up to 1 year. Control of potential confounders was attempted with extensive statistical adjustment by covariates and/or propensity score. In-hospital MACCEs occurred in 73 patients (11.8%) and was more frequent in patients treated with SAVR compared to those treated with TAVI (7.8% vs 14.0%, p = 0.022). After addressing potential confounders using 3 methods of statistical adjustment, SAVR was consistently associated with a higher risk of MACCEs than TAVI, with estimates of relative risk ranging from 2.2 to 2.6 at 30 days, 2.3 to 2.5 at 6 months, and 2.0 to 2.2 at 12 months. This difference was driven by an adjusted increased risk of life-threatening bleeding at 6 and 12 months and stroke at 12 months with SAVR. Conversely, no differences in adjusted risk of death, stroke and myocardial infarction were noted between TAVI and SAVR at each time point. In conclusion, in a large observational registry with admitted potential for selection bias and residual confounding, TAVI was not associated with a higher risk of 1-year MACCEs compared to SAVR.

Algorithmic information for interval maps with an indifferent fixed point and infinite invariant measure.

Measuring the average information that is necessary to describe the behavior of a dynamical system leads to a generalization of the Kolmogorov-Sinai entropy. This is particularly interesting when the system has null entropy and the information increases less than linearly with respect to time. We consider a class of maps of the interval with an indifferent fixed point at the origin and an infinite natural invariant measure. We show that the average information that is necessary to describe the behavior of the orbits increases with time n approximately as nalpha, where alpha < 1 depends only on the asymptotic behavior of the map near the origin.