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. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events

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. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).Intel Science and Technology Center for Big DataNational Institutes of Health. (U.S.). National Library of Medicine (Biomedical Informatics Research Training Grant NIH/NLM 2T15 LM007092-22)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 Grant EB001659)Singapore. Agency for Science, Technology and Research (Graduate Scholarship

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. We computed individual patient NLR and categorized patients by quartile of this ratio. The association of NLR quartiles and 28-day mortality was assessed using multivariable logistic regression. Secondary outcomes included mortality in the ICU, in-hospital mortality and 1-year mortality. An a priori subgroup analysis of patients with versus without sepsis was performed to assess any differences in the relationship between the NLR and outcomes in these cohorts. Results A total of 5,056 patients were included. Their 28-day mortality rate was 19%. The median age of the cohort was 65 years, and 47% were female. The median NLR for the entire cohort was 8.9 (interquartile range, 4.99 to 16.21). Following multivariable adjustments, there was a stepwise increase in mortality with increasing quartiles of NLR (first quartile: reference category; second quartile odds ratio (OR) = 1.32; 95% confidence interval (CI), 1.03 to 1.71; third quartile OR = 1.43; 95% CI, 1.12 to 1.83; 4th quartile OR = 1.71; 95% CI, 1.35 to 2.16). A similar stepwise relationship was identified in the subgroup of patients who presented without sepsis. The NLR was not associated with 28-day mortality in patients with sepsis. Increasing quartile of NLR was statistically significantly associated with secondary outcome. Conclusion The NLR is associated with outcomes in unselected critically ill patients. In patients with sepsis, there was no statistically significant relationship between NLR and mortality. Further investigation is required to increase understanding of the pathophysiology of this relationship and to validate these findings with data collected prospectively.National Institutes of Health (U.S.) (Grant R01 EB017205-01A1

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. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).Intel Science and Technology Center for Big DataNational Institutes of Health. (U.S.). National Library of Medicine (Biomedical Informatics Research Training Grant NIH/NLM 2T15 LM007092-22)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 Grant EB001659)Singapore. Agency for Science, Technology and Research (Graduate Scholarship

Lightning risk warnings based on atmospheric electric field measurements in Brazil

This paper presents a methodology that employs the electrostatic field variations caused by thundercloud formation or displacement to generate lightning warnings over a region of interest in Southeastern Brazil. These warnings can be used to prevent accidents during hazardous operations, such as the manufacturing, loading, and test of motor-rockets. In these cases, certain equipment may be moved into covered facilities and personnel are required to take shelter. It is also possible to avoid the threat of natural and triggered lightning to launches. The atmospheric electric field database, including the summer seasons of 2007/2008 and 2008/2009 (from November to February), and, for the same period and region, the cloud-to-ground lightning data provided by the Brazilian lightning detection network – BrasilDAT – were used in order to perform a comparative analysis between the lightning warnings and the cloud-to- ground lightning strikes that effectively occurred inside the area of concern. The analysis was done for three areas surrounding the sensor installation defined as circles with 5, 10 and 15 km of radius to determine the most effective detection range. For each area it was done using several critical electric field thresholds: +/- 0.5; +/- 0.8; +/- 0.9; +/- 1.0; +/- 1.2; and +/- 1.5 kV/m. As a result of the reduction of atmospheric electric field data provided by the sensor installed in area of concern and lightning provided by BrasilDAT, it was possible, for each of the areas of alert proposals, to obtain the following parameters: the number of effective alarms; the number of false alarms; and the number of failure to warning. From the analysis of these parameters, it was possible to conclude that, apparently, the most interesting critical electric field threshold to be used is the level of 0.9 kV/m in association with a distance range of 10 km around the point where the sensor is installed

Functional brain perfusion evaluation with Arterial Spin Labeling at 3 Tesla

Dissertation submitted in Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa for the degree of Master of Biomedical EngineeringBackground: The new clinically available arterial spin labelling (ASL) sequences present some advantages relatively to the commonly used blood oxygenation level dependent (BOLD) method for functional brain studies using magnetic resonance imaging (MRI), namely the fact of being potentially quantitative and more reproducible. Purpose: The main aim of this work was to evaluate the functional use of a commercial ASL sequence implemented on a 3 Tesla MRI system (Siemens, Verio) in the Imaging Department of Hospital da Luz. The first aim was to obtain a functional validation of this technique by comparison with the BOLD contrast, using a number of different approaches. The second aim was to accomplish perfusion quantification, by resolving some important quantification issues. Materials and Methods: Fifteen adult volunteers participated in a single functional imaging session using three different protocols: one using BOLD and two using ASL. The subjects performed a motor finger tapping task and the data analysis was performed using Siemens Neuro3D and FSL (FMRIB’s Software Library). The location and variability of the activated areas were analysed in MNI (Montereal Neurological Institute) standard space. Results: Topographic agreement between the activated regions obtained by BOLD and ASL was found. However, the results show that inter-subject variability and distance to the hand motor cortex were smaller when measured with ASL as compared with BOLD fMRI. Quantitative studies revealed that ASL allows the calculation of cerebral blood flow (CBF), both at baseline and upon functional activation. Conclusion: The results suggest that the functional imaging protocols using ASL produce comparable results to a conventional BOLD protocol, with the additional advantages of reduced inter-subject variability, better spatial specificity and quantification possibilities

Trazodona para o tratamento de distúrbios do sono em demência: um estudo aberto, observacional e de revisão

Sleep disorders (SD) in patients with dementia are very common in clinical practice. The use of antidepressants with hypnotic actions, such as trazodone, plays an important role in these cases. The aim of this study is to present a profile of the use of trazodone in demented patients with SD, as well as a review of trazodone hydrochloride in SD. We evaluated 178 elderly patients with Alzheimer’s disease and other dementias, clinically presenting SD and treated with hypnosedative medications. In the one-year period comprising the study, 68 (38.2%) of the 178 had sleep disorders. Most patients (114; 64%) had a diagnosis of Alzheimer’s disease. Approximately 85% of patients with SD used hypnosedative drugs. Trazodone was the most commonly used drug among patients (N = 35), with an effectiveness of 65.7%. Trazodone has been shown to be a good option for treatment of the elderly with dementia and associated SD. _________________________________________________________________________________________________________________ RESUMODistúrbios do sono (DS) em pacientes com demência são muito comuns na prática clínica. O uso de antidepressivos com ação hipnótica, como a trazodona, tem um papel importante nesses casos. O objetivo desse estudo é apresentar um perfil do uso da trazodona em pacientes com demência e com DS, bem como revisar o cloridrato de trazodona no DS. Nós avaliamos 178 idosos com doença de Alzheimer (DA) e outras demências, clinicamente apresentando DS e que foram tratados com medicações hipnossedativas. No período de um ano de estudo, 68 (38,2%) tiveram DS. A maioria (114; 64%) tinham diagnóstico de DA. Aproximadamente 85% usaram fármacos hipnossedativos. A trazodona foi a mais utilizada (N=35), com evidência de melhora de 65,7%. A trazodona mostrou-se ser uma boa opção no tratamento de idosos com demência e DS associado

A comparison of the ability of the National Early Warning Score and the National Early Warning Score 2 to identify patients at risk of in-hospital mortality: a multi-centre database study

AIMS:To compare the ability of the National Early Warning Score (NEWS) and the National Early Warning Score 2 (NEWS2) to identify patients at risk of in-hospital mortality and other adverse outcomes. METHODS:We undertook a multi-centre retrospective observational study at five acute hospitals from two UK NHS Trusts. Data were obtained from completed adult admissions who were not fit enough to be discharged alive on the day of admission. Diagnostic coding and oxygen prescriptions were used to identify patients with type II respiratory failure (T2RF). The primary outcome was in-hospital mortality within 24 h of a vital signs observation. Secondary outcomes included unanticipated intensive care unit admission or cardiac arrest within 24 h of a vital signs observation. Discrimination was assessed using the c-statistic. RESULTS:Among 251,266 adult admissions, 48,898 were identified to be at risk of T2RF by diagnostic coding. In this group, NEWS2 showed statistically significant lower discrimination (c-statistic, 95% CI) for identifying in-hospital mortality within 24 h (0.860, 0.857-0.864) than NEWS (0.881, 0.878-0.884). For 1394 admissions with documented T2RF, discrimination was similar for both systems: NEWS2 (0.841, 0.827-0.855), NEWS (0.862, 0.848-0.875). For all secondary endpoints, NEWS2 showed no improvements in discrimination. CONCLUSIONS:NEWS2 modifications to NEWS do not improve discrimination of adverse outcomes in patients with documented T2RF and decrease discrimination in patients at risk of T2RF. Further evaluation of the relationship between SpO2 values, oxygen therapy and risk should be investigated further before wide-scale adoption of NEWS2.Marco A.F. Pimentel, Oliver C. Redfern, Stephen Gerry, Gary S. Collins, James Malycha, David Prytherch ... et al

Data Fusion Techniques for Early Warning of Clinical Deterioration

Algorithms for identification of deteriorating patients from electronic health records (EHRs) fuse vital sign data, which can be measured at the bedside, with additional physiological data from the EHR. It has been observed that these algorithms provide improved performance over traditional early warning scores (EWSs), which are restricted to the use of vital signs alone. This case study demonstrates the development of an algorithm which uses logistic regression to fuse vital signs with additional physiological parameters commonly found in an EHR to predict deterioration