Biomedical subjects
Publications and source records attributed to G D Swanson.
Analysis of 2 x 2 contingency tables is not as trivial as it appears. The choice of the statistical test can affect the inferences resulting from data analysis, especially at small sample sizes. Canned statistical programs do not necessarily lead to an appropriate test. These points are demonstrated using examples from the literature.
Pseudorandom binary sequence (PRBS) exercise tests involve repeated switching between two work rates (WR) according to a computer-generated pattern. This paper presents an approach to analysis of O2 uptake (VO2) in the time domain. First, the autocorrelation function (ACF) of the input WR was recognized to be a triangular-shaped pulse that can be taken to be equivalent to a ramp increase followed by a ramp decrease in WR. Then the cross-correlation function of the input (WR) and the output (VO2) was treated as if it were the response to a triangular-shaped pulse. The cross-correlation function was analyzed by fitting a linear summation of the ramp form of a two-component exponential function to this triangular pulse. VO2 responses of eight subjects were obtained from two different PRBS tests, as well as step changes in WR. The first PRBS test consisted of 15 units, each 30 s in duration. Its ACF had a base width of 60 s. The ramp increase-ramp decrease model fit the data throughout the range of response. The second PRBS test had 63 units, each 5 s in duration; thus its ACF base width was 10 s. Again, the ramp model fit adequately. The data from the second PRBS test could be fit by the impulse form of the two-component exponential equation, although the fit in the first 30 s tended to be poorer. The time constants of VO2 dynamics estimated from step and PRBS tests were not significantly different. PRBS tests can be analyzed in the time domain, and the indicators of system dynamics reflect physiological properties similar to those investigated during step changes in WR.
A general probabilistic approach is applied to the single-point, single-dose method for estimating individual infusion rates and serum drug concentrations. By using transformations of probability density functions, the effects of variations in the elimination rate constant upon pharmacokinetic variables may be studied and optimal sampling times may be chosen. Although this study treats the case of error-free sampling in a single-compartment model with a normal distribution of rate constants, the methods presented can be extended to more general situations.
The dynamic response of oxygen uptake (VO2) was investigated with two different cycle ergometer tests in which the work rate changed as a pseudorandom binary sequence (PRBS). One sequence had 15 units, each of 30-s duration for a total of 450 s (PRBS1). The second had 63 units, each of 5-s duration for a total of 315 s (PRBS2). The useful range of frequencies available for investigation of the dynamic characteristics of the VO2 response as described by their bandwidth were 0.002-0.013 Hz for PRBS1 and 0.003-0.089 Hz for PRBS2. Eight subjects each completed both PRBS tests. Data from four or five consecutive sequences were ensemble averaged to reduce the biological noise. A Fourier analysis was then conducted, with the range of frequencies investigated spanning those of the bandwidth for PRBS2. This was up to the 28th harmonic. For PRBS1, the VO2 response could be adequately reconstructed by including Fourier coefficients only up to the 5th harmonic. In contrast, for PRBS2, there was still a clear pattern in the residuals at the 5th harmonic. The data were not adequately reconstructed until higher-frequency components up to the 28th harmonic were included. Evidence for this came from analysis of the mean square error. The mean square error at the 28th harmonic was reduced to 83 +/- 8% of the mean square error at the 5th harmonic for PRBS1 and to 31 +/- 3% for PRBS2 (P less than 0.0001). These data obtained by Fourier analysis and reconstructed for comparison with the original VO2 response indicate the presence of a high-frequency component that was not apparent when a test with a smaller bandwidth was used as the work rate forcing.
A model abstracts certain features of real systems, which are consistent with the purpose intended for the model. Therefore, models are classified with respect to intended purpose. A structural model predicts behavior based on a hypothetical physiological structure. An empirical model summarizes observed behavior. A functional model attempts to relate observed behavior to physiological structure. It differs from structural model since it includes only structural aspects that are essential for describing the system response. This model classification provides for the interaction among experimental data, underlying physiological hypotheses and experimental design. In this paper, we illustrate the utility of this modeling process for studying pulmonary gas exchange dynamic control processes during exercise. The modeling process is applied to the problem of estimating breath-by-breath gas exchange data, to the problem of selecting appropriate models for characterizing the response to dynamic work rate inputs, and finally to the problem of design of the dynamic aspects of the work rate input.
The precision of an interpretation of gas exchange records in progressive exercise is limited by the typical breath-to-breath variation in the data. Recently, two procedures have been proposed for minimizing the "noise" in the estimates of alveolar gas exchange time series data. One approach utilizes an estimate of pulmonary blood flow (Q) for smoothing purposes. The other approach utilizes an estimate of effective lung volume (V'L) for smoothing purposes. In this paper, we formulate the smoothing problem as a general linear model and demonstrate the concurrent estimates of both V'L and Q. Furthermore, we investigate the interaction between V'L and Q. Specifically, when a high value of lung volume is used (such as the subject's resting functional residual capacity) in the alveolar gas exchange algorithm, the estimate of Q is biased low and the result is a less effective smoothing of the data. In addition, we demonstrate how the Q estimate can be improved by utilizing more appropriate estimates of arterial carbon dioxide tension.
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It may be possible to estimate a nominal pulmonary blood flow (Q) during an exercise stress test via the algorithm used to estimate breath-by-breath alveolar CO2 production. Recently it has been demonstrated that by relating breath-to-breath fluctuations in alveolar CO2 production to breath-to-breath fluctuations in end-tidal CO2, an optimizing parameter related to Q can be determined that can be used to process the CO2 production fluctuations and minimize their variation. However, the reported values of Q using this procedure appear to be biased low. Using a computer simulation of gas exchange, we demonstrate that the estimate of Q is biased low when the nominal lung volume used in the alveolar gas exchange algorithm is too large. Furthermore, alveolar CO2 transport is determined by an integral of alveolar CO2 over the breath time and, thus, is a path-dependent quantity. The use of end-tidal CO2 fluctuations to approximate fluctuations in this integral contributes to an error in the estimation of Q which yields estimates that are biased low. Alternatively, the use of mean alveolar CO2 fluctuations yield more appropriate Q estimates. These results suggest practical implications for estimating effective pulmonary blood flow during an exercise stress test by using breath-to-breath estimates of mean alveolar CO2.
Respiratory gas-exchange data from progressive exercise tests are typically interpreted by visual inspection. Attempts to objectify such interpretation have applied particular parametric models which limit the measures which can be studied and the inferences which can be made. We use a known spline-smoothing procedure which fits a continuous curve to such data, yielding confidence intervals for the curve and for its first and second derivatives. Rules can be made which use the derivatives to infer features of a curve's shape and to relate features from different curves in the same data set. In this way complex interpretations can be made objective, so that they may be adequately tested.
The constancy of the time course (i.e., dynamic linearity) of the O2 uptake (VO2) response to exercise was examined by testing the law of superposition on data from impulse and step work rate forcings. Two impulses (10 s at a 235-W increase above a 25-W base line, I-235; and 5 s at a 475-W increase above a 25-W base line, I-475), four steps (ST) (25-65 W, ST1; 65-105 W, ST2; 25-105 W, ST3; and 25-145 W, ST4), and the corresponding off-transient responses were performed six to eight times by each of five subjects. The integrated area (G) of the VO2 response for I-235 was similar to that of ST1 and ST2 (P greater than 0.05); the I-475 G was significantly greater (P less than 0.05). The time constant of VO2 during the step function on-transient response for the second exponential component was significantly faster for ST1 and significantly slower for I-235 and I-475 than for ST2, ST3, and ST4 (P less than 0.05). However, I-235 and I-475 time constants for VO2 were not different from the ST off-transient values. Attempts to superimpose the integral of the impulse on the ST data showed that the early rapid increase in VO2 in the ST was underpredicted by the impulse and that the impulse response lagged behind the ST at all points before steady state. It can be concluded that VO2 kinetics failed the test of superposition and are therefore described by a nonlinear dynamic system.
Ramp work rate tests have been used to estimate aerobic parameters in exercise stress testing. Previous studies have suggested an assumption of a linear dynamic system for O2 uptake kinetics. The implication is that model parameters estimated from ramp tests should be similar to those estimated from other dynamic tests. In nine healthy subjects, we found that model parameters used to characterize O2 consumption ramp data were not consistent with those used to characterize step data, when the comparison was made on a subject-to-subject basis. Furthermore the ramp data model parameter values were highly dependent (P less than 0.0001) on the ramp slope. A linear dynamic system interpretation of the ramp data model does not appear to be appropriate, suggesting that caution is needed in the interpretation of ramp data aerobic parameters. The data may be better described by nonlinear or higher order function. Ramp exercise testing is not suitable for assessing dynamic control properties of the cardiorespiratory response to exercise.
Anesthesiologists are concerned with the effect of various anesthetics on a patient's central nervous ventilatory control. The most widely accepted method of determining the effect of a drug is to compare carbon dioxide response curves (delta VE/delta PETCO2, where VE = minute ventilation [in L/min] and PETCO2 = end-tidal carbon dioxide [in mm Hg]) measured before and after administration of the drug. Additional information concerning neuromechanical control can be obtained by also including a measure of the airway occlusion pressure (generally measured 100 ms after occlusion, i.e., P100). To facilitate these measurements we have developed a portable, computer-controlled data acquisition system. It includes an Apple II+ computer and measures VE, PETCO2, and P100. Each subject rebreathes exhaled carbon dioxide through a two-way breathing valve attached to a 9-liter reservoir, which is initially filled with 5% carbon dioxide and balance oxygen. Exhaled carbon dioxide concentrations are measured with an infrared medical gas analyzer on samples taken through a catheter connected at the mouthpiece. The exhaled flow is measured with a pneumotachograph in conjunction with a differential pressure transducer, and P100 is determined with a Validyne MP45 pressure transducer.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Recent evidence suggests that there is a persistence of elevated respiratory center activity for many respiratory cycles after the cessation of a neural stimulus. To explore the theoretical possibility that this behavior may be a consequence of the neural oscillator that dictates respiratory rhythm, the transient response of a mathematical model of a medullary respiratory oscillator recently described by Geman and Miller was examined using computer simulation. This concept was motivated by the presence of a persistent transient response behavior inherent in oscillators from mathematical physics. The results indicate that a transient persistence effect is evident in the model behavior under some conditions, and this effect as well as the steady-state amplitude is markedly sensitive to the shape of the saturation function that interconnects the neuron populations. Furthermore, this behavior is initiated by either an abrupt decrease in a tonic input or an abrupt decrease in the synaptic weights connecting subpopulations of neurons.
The demonstration that the rate of rise of the alveolar fraction of nitrous oxide is enhanced when the inspired N2O concentration is high is termed the "concentration effect." A similar effect on a second gas has been termed the "second gas effect". These effects have been observed in open systems and attributed to differential changes between inspired and expired ventilation. The purpose of this investigation is to study these effects in a closed system. A breath-holding maneuver was utilized with a high and a low N2O concentration in argon and oxygen. The results indicate that breath holding with a high N2O concentration "concentrates" both the alveolar fraction of N2O and argon. These results are attributable to alveolar volume shrinkage as a consequence of the large absorption of N2O by the pulmonary blood. A mathematical model verifies this interpretation and suggests that volume shrinkage can be important in breath-holding maneuvers designed for noninvasive measurement of cardiac output and lung tissue volume.
Practically every respiratory physiologist of the last 100 years has studied the ventilatory response to exercise. Yet we still do not know the cause of increased ventilation associated with exercise. This overview considers the problem from a broad observational point of view. Data from studies combining exercise with continuous inspired CO2 and "slug" CO2 breathing imply a feed-forward/feed-back structure for the ventilatory controller. The feed-forward stimulus is correlated to CO2 production. Feed-back senses arterial CO2 tension and acts to minimize the effects of correlation errors and correlation slope errors in the feed-forward path. This feed-forward/feed-back structure yields a regulated arterial CO2 and a tight coupling of ventilation to CO2 production. The feed-back mechanism acts via the carotid body, and indirectly via the central chemoreceptor. A variety of mechanisms are discussed that may be involved in providing the feed-forward stimulus. In particular, the intravenous loading experimental results are considered in terms of an appropriate feed-forward stimulus.