Detection of delirium by family members in the intensive care unit: Translation, Cross-Cultural adaptation and validation of the Family Confusion Assessment Method for the German-Speaking area

Aim: The aim of this study was the translation, cross-cultural adaptation and validation of the Family Confusion Assessment Method in critically ill patients. Background: Delirium is a frequently unrecognized disorder in critically ill patients. Visiting family members might be the first to notice subtle changes in a patient's cognition and behaviour. The Family Confusion Assessment Method was developed to detect delirium by family members, but has not been available for the German-speaking area yet. Design: A prospective validation study was conducted between January 2020 and October 2020. Methods: The Family Confusion Assessment Method was translated into German according to the Principles of Good Practice for the Translation and Cultural Adaptation Process for Patient-Reported Outcomes. Subsequently, we compared the Family Confusion Assessment Method with the Confusion Assessment Method for the Intensive Care Unit in critically ill patients and their family members in a medical intensive care unit in Germany. Results: We included 50 dyads of critically ill patients and their family members. The prevalence of delirium measured by Confusion Assessment Method for the Intensive Care Unit was 44%. Cohen's kappa coefficient was 0.84. The German Family Confusion Assessment Method had a high sensitivity of 95.5% and specificity of 89.3%. The positive predictive value and negative predictive value were 87.5% and 96.2% respectively. Conclusions: These findings suggest that the German Family Confusion Assessment Method is an accurate assessment tool for delirium detection in the intensive care unit by family members. Furthermore, the results indicate that family members may identify delirium by the Family Confusion Assessment Method without prior training. Impact: Collaborating medical staff with patients' family members to detect delirium in the intensive care unit may lead to early recognition of delirium. Keywords: Family Confusion Assessment Method; delirium; family members; intensive care unit; nurses; validation study

Pharmacokinetics of Haloperidol in Critically Ill Patients:Is There an Association with Inflammation?

Haloperidol is considered the first-line treatment for delirium in critically ill patients. However, clinical evidence of efficacy is lacking and no pharmacokinetic studies have been performed in intensive care unit (ICU) patients. The aim of this study was to establish a pharmacokinetic model to describe the PK in this population to improve insight into dosing. One hundred and thirty-nine samples from 22 patients were collected in a single-center study in adults with ICU delirium who were treated with low-dose intravenous haloperidol (3–6 mg per day). We conducted a population pharmacokinetic analysis using Nonlinear Mixed Effects Modelling (NONMEM). A one-compartment model best described the data. The mean population estimates were 51.7 L/h (IIV 42.1%) for clearance and 1490 L for the volume of distribution. The calculated half-life was around 22 h (12.3–29.73 h) for an average patient. A negative correlation between C-Reactive Protein (CRP) and haloperidol clearance was observed, where clearance decreased significantly with increasing CRP up to a CRP concentration of 100 mg/L. This is the first step towards haloperidol precision dosing in ICU patients and our results indicate a possible role of inflammation

Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences

International audienceWe present and evaluate a person re-identification scheme for multi-camera surveillance system. Our approach uses matching of signatures based on interest-points descriptors collected on short video sequences. One of the originalities of our method is to accumulate interest points on several sufficiently time-spaced images during person tracking within each camera, in order to capture appearance variability. A first experimental evaluation conducted on a publicly available set of low-resolution videos in a commercial mall shows very promising inter-camera person re-identification performances (a precision of 82% for a recall of 78%). It should also be noted that our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making reidentification among hundreds of persons computationally feasible in less than ~ 1/5 second

Visual on-line learning in distributed camera networks

Automatic detection of persons is an important application in visual surveillance. In general, state-of-the-art systems have two main disadvantages: First, usually a general detector has to be learned that is applicable to a wide range of scenes. Thus, the training is time-consuming and requires a huge amount of labeled data. Second, the data is usually processed centralized, which leads to a huge network traffic. Thus, the goal of this paper is to overcome these problems, which is realized by a person detection system, that is based on distributed smart cameras (DSCs). Assuming that we have a large number of cameras with partly overlapping views, the main idea is to reduce the model complexity of the detector by training a specific detector for each camera. These detectors are initialized by a pre-trained classifier, that is then adapted for a specific camera by co-training. In particular, for co-training we apply an on-line learning method (i.e., boosting for feature selection), where the information exchange is realized via mapping the overlapping views onto each other by using a homography. Thus, we have a compact scenedependent representation, which allows to train and to evaluate the classifiers on an embedded device. Moreover, since the information transfer is reduced to exchanging positions the required network-traffic is minimal. The power of the approach is demonstrated in various experiments on different publicly available data sets. In fact, we show that on-line learning and applying DSCs can benefit from each other. Index Terms — visual on-line learning, object detection, multi-camera networks 1

gaps in pain agitation and delirium management in intensive care outputs from a nurse workshop

Abstract Significant improvements in our understanding of pain, agitation, and delirium management within the Intensive Care Unit have been made in recent years. International guidelines and implementation bundles have become more evidence-based, patient-centred, and provide clear recommendations on the best-practice management of critically ill patients. However, the intensive care community has highlighted the need for higher-order evidence in several areas of pain, agitation and delirium research and studies suggest that a significant number of intensive care patients still receive outdated treatment as a consequence of inadequate guideline implementation. Where do the gaps exist in pain, agitation and delirium management, what are the barriers to guideline implementation and how can these problems be addressed to ensure patients receive optimised care? As an international professional consensus exercise, a panel of seven European intensive care nurses convened to discuss how to address these questions and establish how the provision of pain, agitation and delirium management can be improved in the intensive care unit

Recalibration of the delirium prediction model for ICU patients (PRE-DELIRIC): a multinational observational study

Purpose Recalibration and determining discriminative power, internationally, of the existing delirium prediction model (PRE-DELIRIC) for intensive care patients. Methods A prospective multicenter cohort study was performed in eight intensive care units (ICUs) in six countries. The ten predictors (age, APACHE-II, urgent and admission category, infection, coma, sedation, morphine use, urea level, metabolic acidosis) were collected within 24 h after ICU admission. The confusion assessment method for the intensive care unit (CAM-ICU) was used to identify ICU delirium. CAM-ICU screening compliance and inter-rater reliability measurements were used to secure the quality of the data. Results A total of 2,852 adult ICU patients were screened of which 1,824 (64 %) were eligible for the study. Main reasons for exclusion were length of stay <1 day (19.1 %) and sustained coma (4.1 %). CAM-ICU compliance was mean (SD) 82 ± 16 % and inter-rater reliability 0.87 ± 0.17. The median delirium incidence was 22.5 % (IQR 12.8–36.6 %). Although the incidence of all ten predictors differed significantly between centers, the area under the receiver operating characteristic (AUROC) curve of the eight participating centers remained good: 0.77 (95 % CI 0.74–0.79). The linear predictor and intercept of the prediction rule were adjusted and resulted in improved re-calibration of the PRE-DELIRIC model. Conclusions In this multinational study, we recalibrated the PRE-DELIRIC model. Despite differences in the incidence of predictors between the centers in the different countries, the performance of the PRE-DELIRIC-model remained good. Following validation of the PRE-DELIRIC model, it may facilitate implementation of strategies to prevent delirium and aid improvements in delirium management of ICU patients

The prognostic value of neurofilament levels in patients with sepsis-associated encephalopathy - A prospective, pilot observational study

Sepsis-associated encephalopathy (SAE) contributes to mortality and neurocognitive impairment of sepsis patients. Neurofilament (Nf) light (NfL) and heavy (NfH) chain levels as biomarkers for neuroaxonal injury were not evaluated in cerebrospinal fluid (CSF) and plasma of patients with sepsis-associated encephalopathy (SAE) before. We conducted a prospective, pilot observational study including 20 patients with septic shock and five patients without sepsis serving as controls. The assessment of SAE comprised a neuropsychiatric examination, electroencephalography (EEG), magnetic resonance imaging (MRI) and delirium screening methods including the confusion assessment method for the ICU (CAM-ICU) and the intensive care delirium screening checklist (ICDSC). CSF Nf measurements in sepsis patients and longitudinal plasma Nf measurements in all participants were performed on days 1, 3 and 7 after study inclusion. Plasma NfL levels increased in sepsis patients over time (p = 0.0063) and remained stable in patients without sepsis. Plasma NfL values were significantly higher in patients with SAE (p = 0.011), significantly correlated with the severity of SAE represented by ICDSC values (R = 0.534, p = 0.022) and correlated with a poorer functional outcome after 100 days (R = -0.535, p = 0.0003). High levels of CSF Nf were measured in SAE patients. CSF NfL levels were higher in non-survivors (p = 0.012) compared with survivors and correlated with days until death (R = -0.932, p<0.0001) and functional outcome after 100 days (R = -0.749, p<0.0001). The present study showed for the first time that Nf levels provide complementary prognostic information in SAE patients indicating a higher chance of death and poorer functional/cognitive outcome in survivors

Video Sensor Architecture for Surveillance Applications

This paper introduces a flexible hardware and software architecture for a smart video sensor. This sensor has been applied in a video surveillance application where some of these video sensors are deployed, constituting the sensory nodes of a distributed surveillance system. In this system, a video sensor node processes images locally in order to extract objects of interest, and classify them. The sensor node reports the processing results to other nodes in the cloud (a user or higher level software) in the form of an XML description. The hardware architecture of each sensor node has been developed using two DSP processors and an FPGA that controls, in a flexible way, the interconnection among processors and the image data flow. The developed node software is based on pluggable components and runs on a provided execution run-time. Some basic and application-specific software components have been developed, in particular: acquisition, segmentation, labeling, tracking, classification and feature extraction. Preliminary results demonstrate that the system can achieve up to 7.5 frames per second in the worst case, and the true positive rates in the classification of objects are better than 80%. © 2012 by the authors; licensee MDPI, Basel, Switzerland.This work has been partially supported by SENSE project (Specific Targeted Research Project within the thematic priority IST 2.5.3 of the 6th Framework Program of the European Commission: IST Project 033279), and has been also co-funded by the Spanish research projects SIDIRELI: DPI2008-06737-C02-01/02 and COBAMI: DPI2011-28507-C02-02, both partially supported with European FEDER funds.Sánchez Peñarroja, J.; Benet Gilabert, G.; Simó Ten, JE. (2012). Video Sensor Architecture for Surveillance Applications. Sensors. 12(2):1509-1528. https://doi.org/10.3390/s120201509S15091528122Batlle, J. (2002). A New FPGA/DSP-Based Parallel Architecture for Real-Time Image Processing. Real-Time Imaging, 8(5), 345-356. doi:10.1006/rtim.2001.0273Foresti, G. L., Micheloni, C., Piciarelli, C., & Snidaro, L. (2009). Visual Sensor Technology for Advanced Surveillance Systems: Historical View, Technological Aspects and Research Activities in Italy. Sensors, 9(4), 2252-2270. doi:10.3390/s90402252Bramberger, M., Doblander, A., Maier, A., Rinner, B., & Schwabach, H. (2006). Distributed Embedded Smart Cameras for Surveillance Applications. Computer, 39(2), 68-75. doi:10.1109/mc.2006.55Foresti, G. L., Micheloni, C., Snidaro, L., Remagnino, P., & Ellis, T. (2005). Active video-based surveillance system: the low-level image and video processing techniques needed for implementation. IEEE Signal Processing Magazine, 22(2), 25-37. doi:10.1109/msp.2005.1406473Fuentes, L. M., & Velastin, S. A. (2003). Tracking People for Automatic Surveillance Applications. Lecture Notes in Computer Science, 238-245. doi:10.1007/978-3-540-44871-6_28García, J., Pérez, O., Berlanga, A., & Molina, J. M. (2007). Video tracking system optimization using evolution strategies. International Journal of Imaging Systems and Technology, 17(2), 75-90. doi:10.1002/ima.20100Xu, H., Lv, J., Chen, X., Gong, X., & Yang, C. (2007). Design of video processing and testing system based on DSP and FPGA. 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment. doi:10.1117/12.783790Sanfeliu, A., Andrade-Cetto, J., Barbosa, M., Bowden, R., Capitán, J., Corominas, A., … Spaan, M. T. J. (2010). Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas. Sensors, 10(3), 2274-2314. doi:10.3390/s100302274http://www.sense-ist.orgXu, H., Lv, J., Chen, X., Gong, X., & Yang, C. (2007). Design of video processing and testing system based on DSP and FPGA. 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment. doi:10.1117/12.78379

Power Management in Sensing Subsystem of Wireless Multimedia Sensor Networks

A wireless sensor network consists of sensor nodes deployed over a geographical area for monitoring physical phenomena like temperature, humidity, vibrations, seismic events, and so on. Typically, a sensor node is a tiny device that includes three basic components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, and a wireless communication subsystem for data transmission. In addition, a power source supplies the energy needed by the device to perform the programmed task. This power source often consists of a battery with a limited energy budget. In addition, it is usually impossible or inconvenient to recharge the battery, because nodes are deployed in a hostile or unpractical environment. On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. Accordingly, energy conservation in nodes and maximization of network lifetime are commonly recognized as a key challenge in the design and implementation of WSNs. Experimental measurements have shown that generally data transmission is very expensive in terms of energy consumption, while data processing consumes significantly less (Raghunathan et al., 2002). The energy cost of transmitting a single bit of information is approximately the same as that needed for processing a thousand operations in a typical sensor node (Pottie & Kaiser, 2000). The energy consumption of the sensing subsystem depends on the specific sensor type. In some cases of scalar sensors, it is negligible with respect to the energy consumed by the processing and, above all, the communication subsystems. In other cases, the energy expenditure for data sensing may be comparable to, or even greater (in the case of multimedia sensing) than the energy needed for data transmission. In general, energy-saving techniques focus on two subsystems: the communication subsystem (i.e., energy management is taken into account in the operations of each single node, as well as in the design of networking protocols), and the sensing subsystem (i.e., techniques are used to reduce the amount or frequency of energy-expensive samples).Postprint (published version