BACKGROUND: Measurement of cardiac output and extravascular lung water in critically ill patients using femoral artery double-indicator dilution involves femoral artery catheterization. The potential ...risk of vascular compromise to the limb may be exacerbated in patients receiving vasopressors. The utility of scanning laser Doppler flowmetry to measure changes in pedal perfusion following catheterization was assessed. RESULTS: There were no significant changes in mean occlusion pressures or in cutaneous perfusion between either leg or between measurement time points, immediately after or 24 h following insertion of the catheters. CONCLUSIONS: Scanning laser Doppler flowmetry is easily used to assess changes in foot perfusion and the effect of interventions that may reduce blood flow to the skin of the foot. Femoral artery catheterization for double-indicator dilution measurements does not reduce calf occlusion pressures or foot skin perfusion in patients receiving vasopressor drugs.
Over 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear ...which algorithm performs the best. Our primary aim was to determine how closely algorithms agreed with a gold standard RR measure when operating under ideal conditions. Secondary aims were: (i) to compare algorithm performance with IP, the clinical standard for continuous respiratory rate measurement in spontaneously breathing patients; (ii) to compare algorithm performance when using ECG and PPG; and (iii) to provide a toolbox of algorithms and data to allow future researchers to conduct reproducible comparisons of algorithms. Algorithms were divided into three stages: extraction of respiratory signals, estimation of RR, and fusion of estimates. Several interchangeable techniques were implemented for each stage. Algorithms were assembled using all possible combinations of techniques, many of which were novel. After verification on simulated data, algorithms were tested on data from healthy participants. RRs derived from ECG, PPG and IP were compared to reference RRs obtained using a nasal-oral pressure sensor using the limits of agreement (LOA) technique. 314 algorithms were assessed. Of these, 270 could operate on either ECG or PPG, and 44 on only ECG. The best algorithm had 95% LOAs of −4.7 to 4.7 bpm and a bias of 0.0 bpm when using the ECG, and −5.1 to 7.2 bpm and 1.0 bpm when using PPG. IP had 95% LOAs of −5.6 to 5.2 bpm and a bias of −0.2 bpm. Four algorithms operating on ECG performed better than IP. All high-performing algorithms consisted of novel combinations of time domain RR estimation and modulation fusion techniques. Algorithms performed better when using ECG than PPG. The toolbox of algorithms and data used in this study are publicly available.
Although increased temperatures are known to reinforce the effects of habitat destruction at local to landscape scales, evidence of their additive or interactive effects is limited, particularly over ...larger spatial extents and longer timescales. To address these deficiencies, we created a dataset of land-use changes over 75 years, documenting the loss of over half (>3000 km
) the semi-natural grassland of Great Britain. Pairing this dataset with climate change data, we tested for relationships to distribution changes in birds, butterflies, macromoths, and plants (n = 1192 species total). We show that individual or additive effects of climate warming and land conversion unambiguously increased persistence probability for 40% of species, and decreased it for 12%, and these effects were reflected in both range contractions and expansions. Interactive effects were relatively rare, being detected in less than 1 in 5 species, and their overall effect on extinction risk was often weak. Such individualistic responses emphasise the importance of including species-level information in policies targeting biodiversity and climate adaptation.
Vibrational spectroscopy is one of the most well-established and important techniques for characterizing chemical systems. To aid the interpretation of experimental infrared and Raman spectra, we ...report on recent theoretical developments in the ChemShell computational chemistry environment for modelling vibrational signatures. The hybrid quantum mechanical and molecular mechanical approach is employed, using density functional theory for the electronic structure calculations and classical forcefields for the environment. Computational vibrational intensities at chemical active sites are reported using electrostatic and fully polarizable embedding environments to achieve more realistic vibrational signatures for materials and molecular systems, including solvated molecules, proteins, zeolites and metal oxide surfaces, providing useful insight into the effect of the chemical environment on the signatures obtained from experiment. This work has been enabled by the efficient task-farming parallelism implemented in ChemShell for high-performance computing platforms. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.
There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of ...standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.
Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or ...the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission.
This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined.
This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There is conflicting evidence on harm related to exposure to supraphysiologic Pa
(hyperoxemia) in critically ill patients.
To examine the association between longitudinal exposure to hyperoxemia and ...mortality in patients admitted to ICUs in five United Kingdom university hospitals.
A retrospective cohort of ICU admissions between January 31, 2014, and December 31, 2018, from the National Institute of Health Research Critical Care Health Informatics Collaborative was studied. Multivariable logistic regression modeled death in ICU by exposure to hyperoxemia.
Subsets with oxygen exposure windows of 0 to 1, 0 to 3, 0 to 5, and 0 to 7 days were evaluated, capturing 19,515, 10,525, 6,360, and 4,296 patients, respectively. Hyperoxemia dose was defined as the area between the Pa
time curve and a boundary of 13.3 kPa (100 mm Hg) divided by the hours of potential exposure (24, 72, 120, or 168 h). An association was found between exposure to hyperoxemia and ICU mortality for exposure windows of 0 to 1 days (odds ratio OR, 1.15; 95% compatibility interval CI, 0.95-1.38;
= 0.15), 0 to 3 days (OR 1.35; 95% CI, 1.04-1.74;
= 0.02), 0 to 5 days (OR, 1.5; 95% CI, 1.07-2.13;
= 0.02), and 0 to 7 days (OR, 1.74; 95% CI, 1.11-2.72;
= 0.02). However, a dose-response relationship was not observed. There was no evidence to support a differential effect between hyperoxemia and either a respiratory diagnosis or mechanical ventilation.
An association between hyperoxemia and mortality was observed in our large, unselected multicenter cohort. The absence of a dose-response relationship weakens causal interpretation. Further experimental research is warranted to elucidate this important question.
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A ...plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
Objective: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by ...several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. Approach: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. Main results: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of <250 Hz and <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. Significance: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available.
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