Association between fluid balance and survival in critically ill patients.

OBJECTIVE: Although the consequences of chronic fluid retention are well known, those of iatrogenic fluid retention that occurs during critical illness have not been fully determined. Therefore, we investigated the association between fluid balance and survival in a cohort of almost 16,000 individuals who survived an intensive care unit (ICU) stay in a large, urban, tertiary medical centre. DESIGN: Longitudinal analysis of fluid balance at ICU discharge and 90-day post-ICU survival. MEASUREMENTS: Associations between fluid balance during the ICU stay, determined from the electronic bedside record, and survival were tested using Cox proportional hazard models adjusted for severity of critical illness. RESULTS: There were 1827 deaths in the first 90 days after ICU discharge. Compared with the lowest quartile of discharge fluid balance [median (interquartile range) -1.5 (-3.1, -0.7) L], the highest quartile [7.6 (5.7, 10.8) L] was associated with a 35% [95% confidence interval (CI) 1.13-1.61)] higher adjusted risk of death. Fluid balance was not associated with outcome amongst individuals without congestive heart failure or renal dysfunction. Amongst patients with either comorbidity, however, fluid balance was strongly associated with outcome, with the highest quartile having a 55% (95% CI 1.24-1.95) higher adjusted risk of death than the lowest quartile. Isotonic fluid balance, defined as the difference between intravenous isotonic fluid administration and urine output, was similarly associated with 90-day outcomes. CONCLUSION: Positive fluid balance at the time of ICU discharge is associated with increased risk of death, after adjusting for markers of illness severity and chronic medical conditions, particularly in patients with underlying heart or kidney disease. Restoration of euvolaemia prior to discharge may improve survival after acute illness.

ß-Glucan-Induced Immuno-Modulation: A Role for the Intestinal Microbiota and Short-Chain Fatty Acids in Common Carp.

Dietary supplementation of fish with ß-glucans has been commonly associated with immunomodulation and generally accepted as beneficial for fish health. However, to date the exact mechanisms of immunomodulation by ß-glucan supplementation in fish have remained elusive. In mammals, a clear relation between high-fibre diets, such as those including ß-glucans, and diet-induced immunomodulation via intestinal microbiota and associated metabolites has been observed. In this study, first we describe by 16S rRNA sequencing the active naive microbiota of common carp intestine. Based on the abundance of the genus Bacteroides, well known for their capacity to degrade and ferment carbohydrates, we hypothesize that common carp intestinal microbiota could ferment dietary ß-glucans. Indeed, two different ß-glucan preparations (curdlan and MacroGard®) were both fermented in vitro, albeit with distinct fermentation dynamics and distinct production of short-chain fatty acids (SCFA). Second, we describe the potential immunomodulatory effects of the three dominant SCFAs (acetate, butyrate, and propionate) on head kidney leukocytes, showing effects on both nitric oxide production and expression of several cytokines (il-1b, il-6, tnfα, and il-10) in vitro. Interestingly, we also observed a regulation of expression of several gpr40L genes, which were recently described as putative SCFA receptors. Third, we describe how a single in vivo oral gavage of carp with MacroGard® modulated simultaneously, the expression of several pro-inflammatory genes (il-1b, il-6, tnfα), type I IFN-associated genes (tlr3.1, mx3), and three specific gpr40L genes. The in vivo observations provide indirect support to our in vitro data and the possible role of SCFAs in ß-glucan-induced immunomodulation. We discuss how ß-glucan-induced immunomodulatory effects can be explained, at least in part, by fermentation of MacroGard® by specific bacteria, part of the naive microbiota of common carp intestine, and how a subsequent production of SFCAs could possibly explain immunomodulation by ß-glucan via SCFA receptors present on leukocytes.

EpCAM-Binding DARPins for Targeted Photodynamic Therapy of Ovarian Cancer.

Ovarian cancer is the most lethal gynecological malignancy due to late detection associated with dissemination throughout the abdominal cavity. Targeted photodynamic therapy (tPDT) aimed at epithelial cell adhesion molecule (EpCAM), overexpressed in over 90% of ovarian cancer metastatic lesions, is a promising novel therapeutic modality. Here, we tested the specificity and activity of conjugates of EpCAM-directed designed ankyrin repeat proteins (DARPins) with the photosensitizer IRDye 700DX in in vitro and in vivo ovarian cancer models. EpCAM-binding DARPins (Ec1: Kd = 68 pM; Ac2: Kd = 130 nM) and a control DARPin were site-specifically functionalized with fluorophores or IRDye 700DX. Conjugation of anti-EpCAM DARPins with fluorophores maintained EpCAM-specific binding in cell lines and patient-derived ovarian cancer explants. Penetration of DARPin Ec1 into tumor spheroids was slower than that of Ac2, indicative of a binding site barrier effect for Ec1. DARPin-IRDye 700DX conjugates killed EpCAM-expressing cells in a highly specific and illumination-dependent fashion in 2D and 3D cultures. Furthermore, they effectively homed to EpCAM-expressing subcutaneous OV90 xenografts in mice. In conclusion, the high activity and specificity observed in preclinical ovarian cancer models, combined with a high specificity in patient material, warrant a further investigation of EpCAM-targeted PDT for ovarian cancer.

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.

Physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the heart rate (HR) and ABP. A robust HR estimation method is described that compensates for these problems. The method is based upon the concept of fusing multiple signal quality indices (SQIs) and HR estimates derived from multiple electrocardiogram (ECG) leads and an invasive ABP waveform recorded from ICU patients. Physiological SQIs were obtained by analyzing the statistical characteristics of each waveform and their relationships to each other. HR estimates from the ECG and ABP are tracked with separate Kalman filters, using a modified update sequence based upon the individual SQIs. Data fusion of each HR estimate was then performed by weighting each estimate by the Kalman filters' SQI-modified innovations. This method was evaluated on over 6000 h of simultaneously acquired ECG and ABP from a 437 patient subset of ICU data by adding real ECG and realistic artificial ABP noise. The method provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia.

The Role of Baroreflex Sensitivity in Acute Hypotensive Episodes Prediction in the Intensive Care Unit.

A life threatening condition in Intensive Care Unit (ICU) is the Acute Hypotensive Episode (AHE). Patients experiencing an AHE may suffer from irreversible organ damage associated with increased mortality. Predicting the onset of AHE could be of pivotal importance to establish appropriate and timely interventions. We propose a method that, using waveforms widely acquired in ICU, like Arterial Blood Pressure (ABP) and Electrocardiogram (ECG), will extract features relative to the cardiac system to predict whether or not a patient will experience a hypotensive episode. Specifically, we want to assess if there are hidden patterns in the dynamics of baroreflex able to improve the prediction of AHEs. We will investigate the predictive power of features related to the baroreflex by performing classifications with and without them. Results are obtained using 17 classifiers belonging to different model families: classification trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs) replicated with different set of hyper-parameters and logistic regression. On average, the use of baroreflex features in the AHE prediction process increases the Area Under the Curve (AUC) by 10%.

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.

Cycle-averaged dynamics of a periodically driven, closed-loop circulation model.

Time-varying elastance models have been used extensively in the past to simulate the pulsatile nature of cardiovascular waveforms. Frequently, however, one is interested in dynamics that occur over longer time scales, in which case a detailed simulation of each cardiac contraction becomes computationally burdensome. In this paper, we apply circuit-averaging techniques to a periodically driven, closed-loop, three-compartment recirculation model. The resultant cycle-averaged model is linear and time invariant, and greatly reduces the computational burden. It is also amenable to systematic order reduction methods that lead to further efficiencies. Despite its simplicity, the averaged model captures the dynamics relevant to the representation of a range of cardiovascular reflex mechanisms.

Functional electrical stimulation of the latissimus dorsi muscle for use in cardiac assist.

Direct and nondirect nerve stimulation modes of the thoraco-dorsal nerve (TDN) leading to the latissimus dorsi muscle (LDM) were evaluated by using nerve cuff electrodes (NCE) and intramuscular electrodes (IME), respectively. Following electrode implantation, the LDM was chronically stimulated for two months to induce muscle transformation to oxidative, fatigue-resistant type I muscle fibers. Threshold and impedance values were measured regularly to establish the stability of the implants. The LDM was then dissected, shaped into a ventricle, subjected to a hydraulic load and stimulated using a controlled-voltage pulse-train stimulator with adjustable parameters. Electrical input and hydraulic output variables were measured to obtain the recruitment characteristics and to compare the efficiency of the two types of electrodes. Results indicate a tradeoff between the NCE's lower threshold, higher recruitment, and lower energy consumption at saturation, and the IME's greater mechanical stability and better long-term reproducibility.

Analysis of arterial waves by the single-pulse-response method.

This study introduces an improved method to determine experimentally the characteristic arterial impedance, and the forward and backward wave components in the carotid artery of the rabbit. This method is based on the concept of the "single-pulse response," i.e., the pressure and flow responses to a single cardiac contraction.

Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals.

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. We have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). We conclude that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals we have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.

Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure.

The paper presents an algorithm for reducing false alarms related to changes in arterial blood pressure (ABP) in intensive care unit (ICU) monitoring. The algorithm assesses the ABP signal quality, analyses the relationship between the electrocardiogram and ABP using a fuzzy logic approach and post-processes (accepts or rejects) ABP alarms produced by a commercial monitor. The algorithm was developed and evaluated using unrelated sets of data from the MIMIC database. By rejecting 98.2% (159 of 162) of the false ABP alarms produced by the monitor using the test set of data, the algorithm was able to reduce the false ABP alarm rate from 26.8% to 0.5% of ABP alarms, while accepting 99.8% (441 of 442) of true ABP alarms. The results show that the algorithm is effective and practical, and its use in future patient monitoring systems is feasible.

Analysis of the ST-T complex of the electrocardiogram using the Karhunen--Loève transform: adaptive monitoring and alternans detection.

The Karhunen-Loève transform (KLT) is applied to study the ventricular repolarisation period as reflected in the ST-T complex of the surface ECG. The KLT coefficients provide a sensitive means of quantitating ST-T shapes. A training set of ST-T complexes is used to derive a set of KLT basis vectors that permits representation of 90% of the signal energy using four KLT coefficients. As a truncated KLT expansion tends to favor representation of the signal over any additive noise, a time series of KLT coefficients obtained from successive ST-T complexes is better suited for representation of both medium-term variations (such as ischemic changes) and short-term variations (such as ST-T alternans) than discrete parameters such as the ST level or other local indices. For analysis of ischemic changes, an adaptive filter is described that can be used to estimate the KLT coefficient, yielding an increase in the signal-to-noise ratio of 10 dB (u = 0.1), with a convergence time of about three beats. A beat spectrum of the unfiltered KLT coefficient series is used for detection of ST-T alterans. These methods are illustrated with examples from the European ST-T Database. About 20% of records revealed quasi-periodic salvos of ischemic ST-T change episodes and another 20% exhibit repetitive, but not clearly periodic patterns of ST-T change episodes. About 5% of ischemic episodes were associated with ST-T alterans.

Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia.

The long-term ST database is the result of a multinational research effort. The goal was to develop a challenging and realistic research resource for development and evaluation of automated systems to detect transient ST segment changes in electrocardiograms and for supporting basic research into the mechanisms and dynamics of transient myocardial ischaemia. Twenty-four hour ambulatory ECG records were selected from routine clinical practice settings in the USA and Europe, between 1994 and 2000, on the basis of occurrence of ischaemic and non-ischaemic ST segment changes. Human expert annotators used newly developed annotation protocols and a specially developed interactive graphic editor tool (SEMIA) that supported paperless editing of annotations and facilitated international co-operation via the Internet. The database contains 86 two- and three-channel 24 h annotated ambulatory records from 80 patients and is stored on DVD-ROMs. The database annotation files contain ST segment annotations of transient ischaemic (1155) and heart-rate related ST episodes and annotations of non-ischaemic ST segment events related to postural changes and conduction abnormalities. The database is intended to complement the European Society of Cardiology ST-T database and the MIT-BIH and AHA arrhythmia databases. It provides a comprehensive representation of 'real-world' data, with numerous examples of transient ischaemic and non-ischaemic ST segment changes, arrhythmias, conduction abnormalities, axis shifts, noise and artifacts.

bpshape wk4: a computer program that implements a physiological model for analyzing the shape of blood pressure waveforms.

We describe the theory and computer implementation of a newly-derived mathematical model for analyzing the shape of blood pressure waveforms. Input to the program consists of an ECG signal, plus a single continuous channel of peripheral blood pressure, which is often obtained invasively from an indwelling catheter during intensive-care monitoring or non-invasively from a tonometer. Output from the program includes a set of parameter estimates, made for every heart beat. Parameters of the model can be interpreted in terms of the capacitance of large arteries, the capacitance of peripheral arteries, the inertance of blood flow, the peripheral resistance, and arterial pressure due to basal vascular tone. Aortic flow due to contraction of the left ventricle is represented by a forcing function in the form of a descending ramp, the area under which represents the stroke volume. Differential equations describing the model are solved by the method of Laplace transforms, permitting rapid parameter estimation by the Levenberg-Marquardt algorithm. Parameter estimates and their confidence intervals are given in six examples, which are chosen to represent a variety of pressure waveforms that are observed during intensive-care monitoring. The examples demonstrate that some of the parameters may fluctuate markedly from beat to beat. Our program will find application in projects that are intended to correlate the details of the blood pressure waveform with other physiological variables, pathological conditions, and the effects of interventions.

A STOIC-based application language for muscle mechanics research.

An application language for the control and analysis of isolated cardiac muscle experiments is described. It is defined using an extensible language, STOIC (derived from FORTH), which consists of a set of basic operations called words. The basic words are readily combined to form higher level words which perform more complex operations. A suitable set of higher level words forms an application language. The resulting language takes full advantage of system hardware capabilities, is easily used by those with little programming experience, and provides flexibility in a research environment where experimental protocols frequently change.

Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information within an expert system.

The authors are developing an expert-system electrocardiogram (ECG) arrhythmia detector (HOBBES) for automated, long-term rhythm analysis. HOBBES employs rules and procedures that emulate how human experts analyze ECGs. This paper describes methods that HOBBES employs for improving error detection and correction in processing noisy ECGs. During periods of clean data, HOBBES develops a knowledge base that describes typical beat shapes, typical interbeat intervals between beats of different types, and patterns of beat sequences that it has observed. During periods of noisy data, HOBBES applies the information learned from the clean data to reject artifact and classify beats. HOBBES was evaluated in a noise-stress test using 35 half-hour ECG records containing a mixture of supraventricular and ventricular ectopy in normal sinus rhythm. In comparison with a classical arrhythmia detector (ARISTOTLE), HOBBES increased the number of correctly classified beats and enhanced the rejection of artifact.