The association between sodium fluctuations and mortality in surgical patients requiring intensive care.

Purpose: Serum sodium derangement is the most common electrolyte disturbance among patients admitted to intensive care. This study aims to validate the association between dysnatremia and serum sodium fluctuation with mortality in surgical intensive care patients.

Method: We performed a retrospective analysis of the Medical Information Mart for Intensive Care II database.

Bayesian Gaussian processes for identifying the deteriorating patient.

The step-down unit (SDU) is a high-acuity hospital environment, to which patients may be sent after discharge from the intensive care unit (ICU). About 1- in-7 patients will deteriorate in the SDU and require emergency readmission to the ICU. Upon readmission, these patients experience significantly higher mortality risks and lengths of stay.

Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices.

Respiratory rate (RR) is one of the most informative indicators of a patient's health status. However, automated, non-invasive measurements of RR are insufficiently robust for use in clinical practice. A number of methods have been described in the literature to estimate RR from both photo-plethysmography (PPG) and electrocardiography (ECG) based on three physiological modulations of respiration: amplitude modulation (AM), frequency modulation (FM), and baseline wander (BW).

Outcome Prediction for Patients with Traumatic Brain Injury with Dynamic Features from Intracranial Pressure and Arterial Blood Pressure Signals: A Gaussian Process Approach.

Previous work has been demonstrated that tracking features describing the dynamic and time-varying patterns in brain monitoring signals provide additional predictive information beyond that derived from static features based on snapshot measurements. To achieve more accurate predictions of outcomes of patients with traumatic brain injury (TBI), we proposed a statistical framework to extract dynamic features from brain monitoring signals based on the framework of Gaussian processes (GPs). GPs provide an explicit probabilistic, nonparametric Bayesian approach to metric regression problems.

Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram.

Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive RR estimation suffer from a lack of robustness, resulting in the fact that they are not used in clinical practice.

The association between the neutrophil-to-lymphocyte ratio and mortality in critical illness: an observational cohort study.

Introduction: The neutrophil-to-lymphocyte ratio (NLR) is a biological marker that has been shown to be associated with outcomes in patients with a number of different malignancies. The objective of this study was to assess the relationship between NLR and mortality in a population of adult critically ill patients.

Methods: We performed an observational cohort study of unselected intensive care unit (ICU) patients based on records in a large clinical database.

Physiological trajectory of patients pre and post ICU discharge.

The intensive care unit (ICU) admits the most severely ill patients, and the goal of the unit can be interpreted as stabilizing patient physiology. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff. Early detection of physiological deterioration has been highlighted as a key step to reduce ICU readmission and improve patient outcomes.

A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data.

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity.

Multitask Gaussian processes for multivariate physiological time-series analysis.

Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools.

Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors.

The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring.

Probabilistic estimation of respiratory rate using Gaussian processes.

The presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function to model the frequency- and amplitude-modulation time series derived from the ECG, where the hyperparameters of the process are used to derive the respiratory rate.

Modelling physiological deterioration in post-operative patient vital-sign data.

Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients' vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a "normal" recovery was constructed using a kernel density estimate, and tested with "abnormal" data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit.

Gaussian process regression in vital-sign early warning systems.

The current standard of clinical practice for patient monitoring in most developed nations is connection of patients to vital-sign monitors, combined with frequent manual observation. In some nations, such as the UK, manual early warning score (EWS) systems have been mandated for use, in which scores are assigned to the manual observations, and care escalated if the scores exceed some pre-defined threshold. We argue that this manual system is far from ideal, and can be improved using machine learning techniques.

Gaussian processes for personalized e-health monitoring with wearable sensors.

Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into "intelligent" analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner.

Phosphine-induced walking response of the lesser grain borer (Rhyzopertha dominica).

Background: In spite of the intensive worldwide use of phosphine against stored-product insects, its potential sublethal effects on targeted insect species is seldom recognised and may compromise the efficacy of this fumigant, particularly against phosphine-resistant insects. Therefore, the behavioural response of three populations of the lesser grain borer Rhyzopertha dominica (Coleoptera: Bostrichidae) to sublethal phosphine exposure was assessed.

Results: Concentration-mortality bioassays with phosphine confirmed the resistance status of the insect populations studied, with levels of phosphine resistance of 40.

Localization of the hand motor area by arterial spin labeling and blood oxygen level-dependent functional magnetic resonance imaging.

The new clinically available arterial spin labeling (ASL) perfusion imaging sequences present some advantages relatively to the commonly used blood oxygen level-dependent (BOLD) method for functional brain studies using magnetic resonance imaging (MRI). In particular, regional cerebral blood flow (CBF) changes are thought to be more directly related with neuronal activation. In this study, we aimed to investigate the accuracy of the functional localization of the hand motor area obtained by simultaneous CBF and BOLD contrasts provided by ASL functional MRI (fMRI) and compare it with a standard BOLD fMRI protocol.

Spread of phosphine resistance among brazilian populations of three species of stored product insects.

The resistance to fumigant insecticides in stored-products insects is often recorded. Several factors influence the evolution of insecticide resistance. Among these, the frequency of applications and the migration of resistant populations are of primary importance for the stored-product insects.

Phosphine resistance, respiration rate and fitness consequences in stored-product insects.

Resistance to fumigants has been frequently reported in insect pests of stored products and is one of the obstacles in controlling these pests. The authors studied phosphine resistance and its physiological basis in adult insects of 12 populations of Tribolium castaneum (Herbst) (Tenebrionidae), ten populations of Rhyzopertha dominica (F.) (Bostrichidae) and eight populations of Oryzaephilus surinamensis L.

[Biological efficacy and persistence of biphenthrin sprayed on maize at different grain temperatures].

The objective of this work was to evaluate the immediate and latent effects of the grain temperature, during the spraying process, on the persistence and biological efficacy of the biphenthrin insecticide against Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) and Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae). For such, biphenthrin was sprayed on the grain at the temperatures: 25, 30, 35, 40 and 45 degrees C. To access the persistence of biphenthrin, insecticide residue analyses were carried out monthly, just after spraying until 90 days of storage.