Neuroprotective effect of therapeutic hypothermia versus standard care alone after convulsive status epilepticus: protocol of the multicentre randomised controlled trial HYBERNATUS

Convulsive status epilepticus (CSE) is a major medical emergency associated with a 50 % morbidity rate. CSE guidelines have recommended prompt management for many years, but there is no evidence to date that they have significantly improved practices or outcomes. Developing neuroprotective strategies for use after CSE holds promise for diminishing morbidity and mortality rates. Hypothermia has been shown to afford neuroprotection in various health conditions. We therefore designed a trial to determine whether 90-day outcomes in mechanically ventilated patients with CSE requiring management in the intensive care unit (ICU) are improved by early therapeutic hypothermia (32–34 °C) for 24 h with propofol sedation. We are conducting a multicentre, open-label, parallel-group, randomised, controlled trial (HYBERNATUS) of potential neuroprotective effects of therapeutic hypothermia and routine propofol sedation started within 8 h after CSE onset in ICU patients requiring mechanical ventilation. Included patients are allocated to receive therapeutic hypothermia (32–34 °C) plus standard care or standard care alone. We plan to enrol 270 patients in 11 ICUs. An interim analysis is scheduled after the inclusion of 135 patients. The main study objective is to evaluate the effectiveness of therapeutic hypothermia (32–34 °C) for 24 h in diminishing 90-day morbidity and mortality (defined as a Glasgow Outcome Scale score <5). The HYBERNATUS trial is expected to a decreased proportion of patients with a Glasgow Outcome Scale score lower than 5 after CSE requiring ICU admission and mechanical ventilation. Trial registration Clinicaltrials.gov identifier NCT01359332 (registered on 23 May 2011

Neurological failure in ICU patients with hematological malignancies : a prospective cohort study

Background : Epidemiological studies of neurological complications in patients with hematological malignancies are scant. The objective of the study was to identify determinants of survival in patients with hematological malignancy and neurological failure. Methods : Post hoc analysis of a prospective study of adults with hematological malignancies admitted for any reason to one of 17 university or university-affiliated participating ICUs in France and Belgium (2010-2012). The primary outcome was vital status at hospital discharge. Results : Of the 1011 patients enrolled initially, 226 (22.4%) had neurological failure. Presenting manifestations were dominated by drowsiness or stupor (65%), coma (32%), weakness (26%), and seizures (19%). Neuroimaging, lumbar puncture, and electroencephalography were performed in 113 (50%), 73 (32%), and 63 (28%) patients, respectively. A neurosurgical biopsy was done in 1 patient. Hospital mortality was 50%. By multivariate analysis, factors independently associated with higher hospital mortality were poor performance status ( odds ratio [OR], 3.99; 95% CI, 1.82-9.39; P = 0.0009), non-Hodgkin's lymphoma ( OR, 2.60; 95% CI, 1.35-5.15; P = 0.005), shock ( OR, 1.95; 95% CI, 1.04-3.72; P = 0.04), and respiratory failure ( OR, 2.18; 95% CI, 1.140-4.25; P = 0.02); and factors independently associated with lower hospital mortality were GCS score on day 1 ( OR, 0.88/point; 95% CI, 0.81-0.95; P = 0.0009) and autologous stem cell transplantation ( OR, 0.25; 95% CI, 0.07-0.75; P = 0.02). Conclusions : In ICU patients with hematological malignancies, neurological failure is common and often fatal. Independent predictors of higher hospital mortality were type of underlying hematological malignancy, poor performance status, hemodynamic and respiratory failures, and severity of consciousness impairment. Knowledge of these risk factors might help to optimize management strategies

Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workload. Achieving the highest degree of isolation implies deploying a component exclusively for one tenant; which leads to high resource consumption and running cost per component. A low degree of isolation allows sharing of resources which could possibly reduce cost, but with known limitations of performance and security interference. This paper presents a model-based algorithm together with four variants of a metaheuristic that can be used with it, to provide near-optimal solutions for deploying components of a cloud-hosted application in a way that guarantees multitenancy isolation. When the workload changes, the model based algorithm solves an open multiclass QN model to determine the average number of requests that can access the components and then uses a metaheuristic to provide near-optimal solutions for deploying the components. Performance evaluation showed that the obtained solutions had low variability and percent deviation when compared to the reference/optimal solution. We also provide recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation

Meeting Deadlines Cheaply

We develop a computational framework for solving the problem of finding the cheapest configuration (in terms of the number of processors and their respective speeds) of a multiprocessor architecture on which a task graph can be scheduled within a given deadline. We then extend the problem in three orthogonal directions: taking communication volume into account, considering the case where a stream of instances of the task graph arrives periodically and reformulating the problem as a bi-criteria optimization for which we approximate the Pareto front

On universal search strategies for multi-criteria optimization using weighted sums

Abstract—We develop a stochastic local search algorithm for finding Pareto points for multi-criteria optimization problems. The algorithm alternates between different single-criterium op-timization problems characterized by weight vectors. The policy for switching between different weights is an adaptation of the universal restart strategy defined by [LSZ93] in the context of Las Vegas algorithms. We demonstrate the effectiveness of our algorithm on multi-criteria quadratic assignment problem benchmarks and prove some of its theoretical properties. I

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System

Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles. Memory-centric computing systems, i.e., with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of modern general-purpose PIM architectures to accelerate ML training. To do so, we (1) implement several representative classic ML algorithms (namely, linear regression, logistic regression, decision tree, K-Means clustering) on a real-world general-purpose PIM architecture, (2) rigorously evaluate and characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart implementations on CPU and GPU. Our evaluation on a real memory-centric computing system with more than 2500 PIM cores shows that general-purpose PIM architectures can greatly accelerate memory-bound ML workloads, when the necessary operations and datatypes are natively supported by PIM hardware. For example, our PIM implementation of decision tree is $27\times$ faster than a state-of-the-art CPU version on an 8-core Intel Xeon, and $1.34\times$ faster than a state-of-the-art GPU version on an NVIDIA A100. Our K-Means clustering on PIM is $2.8\times$ and $3.2\times$ than state-of-the-art CPU and GPU versions, respectively. To our knowledge, our work is the first one to evaluate ML training on a real-world PIM architecture. We conclude with key observations, takeaways, and recommendations that can inspire users of ML workloads, programmers of PIM architectures, and hardware designers & architects of future memory-centric computing systems

Delayed awakening after cardiac arrest: prevalence and risk factors in the Parisian registry

PURPOSE: Although prolonged unconsciousness after cardiac arrest (CA) is a sign of poor neurological outcome, limited evidence shows that a late recovery may occur in a minority of patients. We investigated the prevalence and the predictive factors of delayed awakening in comatose CA survivors treated with targeted temperature management (TTM). METHODS: Retrospective analysis of the Parisian Region Out-of-Hospital CA Registry (2008-2013). In adult comatose CA survivors treated with TTM, sedated with midazolam and fentanyl, time to awakening was measured starting from discontinuation of sedation at the end of rewarming. Awakening was defined as delayed when it occurred after more than 48 h. RESULTS: A total of 326 patients (71 % male, mean age 59 ± 16 years) were included, among whom 194 awoke. Delayed awakening occurred in 56/194 (29 %) patients, at a median time of 93 h (IQR 70-117) from discontinuation of sedation. In 5/56 (9 %) late awakeners, pupillary reflex and motor response were both absent 48 h after sedation discontinuation. In multivariate analysis, age over 59 years (OR 2.1, 95 % CI 1.0-4.3), post-resuscitation shock (OR 2.6 [1.3-5.2]), and renal insufficiency at admission (OR 3.1 [1.4-6.8]) were associated with significantly higher rates of delayed awakening. CONCLUSIONS: Delayed awakening is common among patients recovering from coma after CA. Renal insufficiency, older age, and post-resuscitation shock were independent predictors of delayed awakening. Presence of unfavorable neurological signs at 48 h after rewarming from TTM and discontinuation of sedation did not rule out recovery of consciousness in late awakeners

Effect of different methods of cooling for targeted temperature management on outcome after cardiac arrest : a systematic review and meta-analysis

Background Although targeted temperature management (TTM) is recommended in comatose survivors after cardiac arrest (CA), the optimal method to deliver TTM remains unknown. We performed a meta-analysis to evaluate the effects of different TTM methods on survival and neurological outcome after adult CA. Methods We searched on the MEDLINE/PubMed database until 22 February 2019 for comparative studies that evaluated at least two different TTM methods in CA patients. Data were extracted independently by two authors. We used the Newcastle-Ottawa Scale and a modified Cochrane ROB tools for assessing the risk of bias of each study. The primary outcome was the occurrence of unfavorable neurological outcome (UO); secondary outcomes included overall mortality. Results Our search identified 6886 studies; 22 studies (n = 8027 patients) were included in the final analysis. When compared to surface cooling, core methods showed a lower probability of UO (OR 0.85 [95% CIs 0.75-0.96]; p = 0.008) but not mortality (OR 0.88 [95% CIs 0.62-1.25]; p = 0.21). No significant heterogeneity was observed among studies. However, these effects were observed in the analyses of non-RCTs. A significant lower probability of both UO and mortality were observed when invasive TTM methods were compared to non-invasive TTM methods and when temperature feedback devices (TFD) were compared to non-TFD methods. These results were significant particularly in non-RCTs. Conclusions Although existing literature is mostly based on retrospective or prospective studies, specific TTM methods (i.e., core, invasive, and with TFD) were associated with a lower probability of poor neurological outcome when compared to other methods in adult CA survivors (CRD42019111021).Peer reviewe