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Biomedical subjects

J E Beneken

Publications and source records attributed to J E Beneken.

At least 19 recordsLinked to original sources
PubMed2001

A new approach to mechanical simulation of lung behaviour: pressure-controlled and time-related piston movement.

A mechanical lung simulator is described (an extension of a previous mechanical simulator) which simulates normal breathing and artificial ventilation in patients. The extended integration of hardware and software offers many new possibilities and advantages over the former simulator. The properties of components which simulate elastance and airway resistance of the lung are defined in software rather than by the mechanical properties of the components alone. Therefore, a more flexible simulation of non-linear behaviour and the cross-over effects of lung properties is obtained. Furthermore, the range of lung compliance is extended to simulate patients with emphysema. The dependency of airway resistance on lung recoil pressure and transmural pressure of the airways can also be simulated. The new approach enables one to incorporate time-related mechanics such as the influence of lung viscosity or cardiac oscillation. The different relations defined in the software can be changed from breath to breath. Three simulations are presented: (1) computer-controlled expiration in the artificially ventilated lung; (2) simulation of normal breathing; and (3) simulation of viscoelastance and cardiac influences during artificial ventilation. The mechanical simulator provides a reproducible and flexible environment for testing new software and equipment in the lung function laboratory and in intensive care, and can be used for instruction and training.

Biomechanical Phenomena↗
PubMed1998

Long-term postoperative cognitive dysfunction in the elderly ISPOCD1 study. ISPOCD investigators. International Study of Post-Operative Cognitive Dysfunction.

BACKGROUND: Long-term postoperative cognitive dysfunction may occur in the elderly. Age may be a risk factor and hypoxaemia and arterial hypotension causative factors. We investigated these hypotheses in an international multicentre study. METHODS: 1218 patients aged at least 60 years completed neuropsychological tests before and 1 week and 3 months after major non-cardiac surgery. We measured oxygen saturation by continuous pulse oximetry before surgery and throughout the day of and the first 3 nights after surgery. We recorded blood pressure every 3 min by oscillometry during the operation and every 15-30 min for the rest of that day and night. We identified postoperative cognitive dysfunction with neuropsychological tests compared with controls recruited from the UK (n=176) and the same countries as study centres (n=145). FINDINGS: Postoperative cognitive dysfunction was present in 266 (25.8% [95% CI 23.1-28.5]) of patients 1 week after surgery and in 94 (9.9% [8.1-12.0]) 3 months after surgery, compared with 3.4% and 2.8%, respectively, of UK controls (p<0.0001 and p=0.0037, respectively). Increasing age and duration of anaesthesia, little education, a second operation, postoperative infections, and respiratory complications were risk factors for early postoperative cognitive dysfunction, but only age was a risk factor for late postoperative cognitive dysfunction. Hypoxaemia and hypotension were not significant risk factors at any time. INTERPRETATION: Our findings have implications for studies of the causes of cognitive decline and, in clinical practice, for the information given to patients before surgery.

Abdomen↗
PubMed1998

Modeling in anesthesia.

A model can be defined as an abstraction of reality which accounts for those properties ofa phenomenon that are pertinent to the purpose of the model. Models are used in anesthesia to understand the various physiologic, pharmacological and physical processes that occur during anesthesia. Indeed, many different types of models that comply with our definition can be distinguished. Early models consisted of electrical models of the arterial blood dynamics and cardiovascular system. Physical models of drug uptake and distribution have been developed to explain the kinetics of volatile anesthetics in the body. The goal of this paper is to introduce the reader to some of the types of models that been used to facilitate education and research in anesthesia. These examples will elucidate the steps involved in developing a model and the various types of models that have proven useful.

Algorithms↗
PubMed1996

Midlatency auditory evoked potentials as indicators of perceptual processing during general anaesthesia.

We tested the hypothesis that midlatency auditory evoked potentials (MLAEP) can predict the occurrence of long latency AEP components (LLAEP), which are taken as evidence for perceptual processing. Forty-one patients undergoing cardiac surgery were anaesthetized with propofol and alfentanil. During several periods of surgery we recorded LLAEP. Peak-to-peak amplitude measures were used to determine if a particular LLAEP recording trace contained a recognizable waveform. Both before and after each LLAEP recording epoch, MLAEP and the spontaneous electroencephalogram (EEG) were recorded. Peak latencies and amplitudes of brainstem peak V and midlatency peaks Na, Pa, Nb, Pb and Nc, characteristic frequencies from the spontaneous EEG, mean arterial pressure (MAP) and nasopharyngeal temperature (7) were compared between recording epochs with and without clear LLAEP waveforms. These variables were also used in a discriminant analysis to predict the occurrence of an LLAEP waveform. Pa and Nb latencies were significantly shorter both before and after recording epochs in which an LLAEP waveform occurred, compared with epochs in which no LLAEP waveform occurred. Using a combination of up to six EEG, MLAEP, MAP and T measures, it was possible to predict the occurrence or absence of an LLAEP waveform with a sensitivity of 89% and specificity of 86%. We conclude that MLAEP components provide information on the possibility of perceptual processing during general anaesthesia, and thus may be relevant for monitoring depth of anaesthesia.

Adult↗
PubMed1995

Computer-controlled mechanical lung model for application in pulmonary function studies.

A computer controlled mechanical lung model has been developed for testing lung function equipment, validation of computer programs and simulation of impaired pulmonary mechanics. The construction, function and some applications are described. The physical model is constructed from two bellows and a pipe system representing the alveolar lung compartments of both lungs and airways, respectively. The bellows are surrounded by water simulating pleural and interstitial space. Volume changes of the bellows are accomplished via the fluid by a piston. The piston is driven by a servo-controlled electrical motor whose input is generated by a microcomputer. A wide range of breathing patterns can be simulated. The pipe system representing the trachea connects both bellows to the ambient air and is provided with exchangeable parts with known resistance. A compressible element (CE) can be inserted into the pipe system. The fluid-filled space around the CE is connected with the water compartment around the bellows; The CE is made from a stretched Penrose drain. The outlet of the pipe system can be interrupted at the command of an external microcomputer system. An automatic sequence of measurements can be programmed and is executed without the interaction of a technician.

Humans↗
PubMed1993

Artifact detection and removal during auditory evoked potential monitoring.

Various artifacts can distort or obscure evoked potential waveforms. The algorithms presented in this paper scan the output electroencephalographic signal for artifacts during evoked potential recordings. If possible, the artifact is removed; if not possible, that sweep is excluded from the averaging process required to raise the evoked response above the background electroencephalographic activity. An artifact is detected if 1 or more amplitude or frequency parameters exceed a threshold. These thresholds have been determined after constructing histograms of the parameters concerned using a number of control evoked potential recordings containing no visually recognizable artifacts. The distributions of the parameters shown by these histograms give information about their normal range. The method improves the quality of the waveform in many cases, but its effectiveness strongly depends on the characteristics of the artifacts concerned.

Algorithms↗
PubMed1991

Serial lung model for simulation and parameter estimation in body plethysmography.

A serial lung model with a compressible segment has been implemented to simulate different types of lung and airway disorders such as asthma, emphysema, fibrosis and upper airway obstruction. The model described can be used during normal breathing, and moreover the compliant segment is structured according to more recent physiological data. A parameter estimation technique was applied and its reliability and uniqueness were tested by means of sine wave input signals. The characteristics of the alveolar pressure/flow patterns simulated with the model agree to a great extent with those found in the literature. In the case of absence of noise the parameter estimation routine produced unique solutions for different simulated pathologic classes. The sensitivity of the different parameters depended on the values belonging to each class of pathology. Some more simplified models are presented and their advantages over the complex model in special types of pathology are demonstrated. Noise added to the simulated flow appeared to have no influence on the estimated parameters, in contradiction to the effects with noise added to the pressure signal. In that case effective resistance was accurately estimated. Where parameters had no influence, as for instance upper airway resistance in emphysema or peripheral airway resistance in upper airway obstruction, the measurement accuracy was less. In all other cases, a satisfactory accuracy could be obtained.

Humans↗
PubMed1989

Sampling intervals for clinical monitoring of variables during anesthesia.

Although five minutes is the sampling interval mentioned by the American Society of Anesthesiologists for monitoring blood pressure and heart rate during anesthesia, most patients are monitored more closely by continuous auscultation and with the help of automated instruments. Yet this difference between the interval recommended and that actually used indicates that sampling intervals are not defined clearly enough. Therefore, we present three methods with which to determine sampling intervals during monitoring. To explore the feasibility of these methods we examined data gathered every 7.5 seconds during three typical, noncatastrophic physiologic perturbations induced in an anesthetized dog. We chose hypercapnia secondary to rebreathing, hypotension secondary to deep anesthesia, and hypoxemia secondary to a low concentration of inspired oxygen as realistic examples of what can occur during operation and anesthesia. We studied three variables: respired carbon dioxide, femoral arterial blood pressure, and oxygen saturation of hemoglobin (pulse oximeter). The data obtained during monitoring were subjected to three methods of analysis: (1) recording of sets of data, with various starting times, at five-minute intervals only (moving grid); (2) Fourier analysis; and (3) analysis of slopes. For the data of the experiment, the Fourier analysis yielded, on average, longer sampling intervals than did the analysis of slopes.

Anesthesia, General↗
PubMed1989

Alarms and their limits in monitoring.

The need to incorporate alarms in monitoring systems is related to the growing complexity of monitoring and the large number of variables. For sophisticated alarms, information about the inputs to the patient is of importance; for example, clinical interventions such as drug administration and ventilation readjustment need to be known to the monitoring system. Alarms are triggered by signals or signal features that exceed thresholds. Each threshold must be seen as a level that needs to be set, either manually or automatically. The large number of levels to be set creates an extra workload for the clinician. Approaches to determine such levels automatically are discussed in this article. Most promising seems the multiple signal approach using an expert system. It seems reasonable to expect that information concerning alarm limits, needed for the operation of knowledge-based alarm systems, may come from integrated departmental data bases.

Anesthesia, General↗
PubMed1989

Pulmonary shunting after cardiopulmonary bypass.

The effect of cardiopulmonary bypass (CPB) on pulmonary function was investigated in 32 adult patients, including 23 patients undergoing coronary artery bypass grafting and nine patients undergoing heart-valve replacement. Clinical indicators for pulmonary insufficiency, such as chest X-ray, gas exchange and lung function tests were measured. Transthoracic electrical impedances were measured, and the mean specific thoracic impedance (RHO) was calculated. (RHO is an accurate indicator for the intrathoracic fluid content; low RHO values correspond with high intrathoracic fluid content.) Significant postoperative decreases in RHO were paralleled by a significant impairment of gas exchange. Chest X-rays demonstrated accumulation of intrathoracic fluid. Lung function tests showed significant postoperative decreases in lung volumes and vital capacity. These findings are consistent with the concept that CPB provokes an inflammatory reaction in the lung. The non-invasive RHO measurement proved to be simple and in good agreement with clinical indicators. This method may be a real asset in the prevention and treatment of pulmonary dysfunction after CPB. The possibility of calibrating RHO with respect to absolute values of intrathoracic fluid content should be investigated.

Cardiography, Impedance↗
PubMed1987

Capnography and the Bain circuit II: Validation of a computer model.

Validation of a computer model is described. The behavior of this model is compared both with mechanical ventilation of a test lung in a laboratory setup that uses a washout method and with manual ventilation. A comparison is also made with results obtained from a volunteer breathing spontaneously through a Bain circuit and with results published in the literature. This computer model is a multisegment representation of the Bain circuit and connecting tubing. For each segment, gas pressure, gas volume flow, and partial pressure of carbon dioxide are calculated for any number of breaths wanted. As a result, the time course of these variables can be generated for any location or, conversely, the carbon dioxide distribution in the system can be calculated for any time instant. A test lung, the human lungs, the ventilator bellows, and the reservoir bag are each represented by a single segment. The shapes of pressure and flow curves and of the capnograms taken at different locations in the Bain tubing are in good agreement. The washout study permits measurement of the time delay between the first expiration and the arrival of carbon dioxide at a particular location. The carbon dioxide level in the test lung decreases during inspiration and is stable during expiration. Quantitative agreement between model and experimental transport delays and carbon dioxide levels is such that the differences can be explained by the inaccuracy of the measurement. This is concluded from a sensitivity analysis. The study of the effect of segment size shows an almost optimal agreement between model behavior and experimental results for a 36-segment model. Execution of a thorough validation is imperative before such models can be used for clinical management and decision making or for teaching.

Airway Resistance↗
PubMed1985

Capnography and the Bain circuit I: A computer model.

The Mapleson D anesthesia breathing system has no valves and allows rebreathing of carbon dioxide. Its coaxial version is known as the Bain system. The interpretation of capnograms obtained during its use requires an understanding of the interrelationships of patient and system variables. Toward that end, a systematic description of mechanical ventilation with the Bain circuit was undertaken based on the physical laws of gas transport. The mathematical formulation of the model contains the relations between pressure, flow, and volume in the tube, alveolar space, and ventilator. The flows, calculated from these relations, are used to determine the CO2 concentrations in the different parts of the model. Two sets of data are used--patient and system. The patient data, used to solve the equations numerically, are lung-thorax compliance, CO2 inflow into alveolar space (CO2 production), functional residual capacity, dead space volume, airway resistance, and respiratory quotient. The ventilation system data comprise the dimensions and volumes of the Bain circuit, ventilator, connectors, and tubes; spill valve pressure; resistances to flow in the individual tube parts; ventilator settings; and fresh-gas flow rates. After incorporation of a volunteer's respiratory variables into the model, capnograms obtained from the model compared well with those obtained from the volunteer. The structure of the model is such that it permits easy introduction or changes of patient and system variables to obtain individual results or model specific circumstances. This flexibility makes it a useful tool for understanding the properties of the Bain circuit under a variety of clinical circumstances. The results may be displayed in a number of different ways.

Anesthesia, General↗
PubMed1985

Factors influencing capnography in the Bain circuit.

The Bain circuit provides continuous fresh gas flow near the airway. The potential mixing of this fresh gas with expired gas may prevent reliable analysis of expired gas. We therefore investigated the interaction of sampling site, fresh gas flow rate, expiratory flow rate, and sampling flow rate on expiratory capnography. Sampling near the fresh gas outlet yielded inaccurate results under several of these conditions. The magnitude of the error was related to the fresh gas and expiratory flow rates. A reliable sampling region near the endotracheal tube was identified.

Anesthesiology↗
PubMed1979

Prognosis, trend and prediction in patient management.

A survey was conducted to get to know the 'state of the art' in trend prediction as a basis for optimal therapy, with emphasis on research being done in the countries of the European Community. Special areas of interest are quantitative prognosis (prognostic indices), the detection and use of trends in the patient's state, the use of models in prediction and their possible use for deciding which therapy is optimal for a specific patient. An extensive list of centres working in these fields as well as an extensive reference list is included.

Computers↗