Frederick M. Bennett

Appropriate use of antitussives and protussives. A practical review.

SummaryAs a symptom of an underlying condition, cough is one of the most common reasons patients see physicians. To the majority, a cough means that ‘something is wrong’ and it causes exhaustion and/or self-consciousness. Patients find these reasons as well as effects on lifestyle, fear of cancer and/or AIDS or tuberculosis to be the most troublesome concerns for which they seek medical attention.The treatment of cough can be divided into two main categories: (a) therapy that controls, prevents or eliminates cough (i.e. antitussive); and (b) therapy that makes cough more effective (i.e. protussive).Antitussive therapy can be either specific or nonspecific. Definitive or specific antitussive therapy depends on determining the aetiology or operant pathophysiological mechanism, and then initiating specific treatment. Since the cause of chronic cough can almost always be determined, it is possible to prescribe specific therapy that can be almost uniformly successful. Nonspecific antitussive therapy is directed at the symptom; it is indicated when definitive therapy cannot be given. Practically speaking, the efficacy of nonspecific therapy must be evaluated in double-blind, placebo-controlled, randomised studies of pathological cough in humans.Such studies have demonstrated the efficacy of dextromethorphan, codeine and ipratropium bromide aerosol in patients with chronic bronchitis. While the preferred treatment for patients with cough due to angiotensin converting enzyme (ACE) inhibitor therapy is withdrawal of the offending drugs, it may be possible to ameliorate the cough by adding nifedipine, sulindac or indomethacin to the treatment regimen.The efficacy of protussive therapy has not been well documented. Although hypertonic saline aerosol and erdosteine in patients with bronchitis, and amiloride aerosol in patients with cystic fibrosis have been shown to improve mucus clearance, their clinical utility has not been adequately studied.

Time series modeling of heart rate dynamics

Autoregressive (AR), autoregressive-moving average (ARMA), bilinear (BL), and polynomial autoregressive (PAR) models were fit to heart rate time series obtained from 9 subjects in the supine position. For each data set and model structure, model order was determined by the Akaike Information Criteria (AIC). For all data sets, the nonlinear BL model had a lower residual variance and AIC compared to AR models. In most cases, BL models provided a better fit to the data than either ARMA or PAR models. For most data sets, the nonlinear BL model provides a more accurate representation of HR dynamics compared to the other model structures tested.<<ETX>>

Influence of autoregressive model parameter uncertainty on spectral estimates of heart rate dynamics

Linear autoregressive (AR) model-based heart rate (HR) spectral analysis has been widely used to study HR dynamics. Owing to system and measurement noise, the parameters of an AR model have intrinsic statistical uncertainty. In this study, we evaluate how this AR parameter uncertainty can translate to uncertainty in HR power spectra. HR time series, obtained from seven subjects in supine standing positions, were fitted to AR models by least squares minimization via singular value decomposition. Spectral uncertainty due to inexact parameter estimation was assessed through a Monte Carlo study in which the AR model parameters were varied randomly according to their Gaussian distributions. Histogram techniques were used to evaluate the distribution of 50,000 AR spectral estimates of each HR time series. These Monte Carlo uncertainties were found to exceed those predicted by previous theoretical approximations. It was determined that the uncertainty of AR HR spectral estimates, particularly the locations and magnitudes of spectral peaks, can often be large. The same Monte Carlo analysis was applied to synthetic AR time series and found levels of spectral uncertainty similar to that of the HR data, thus suggesting that the results of this study are not specific to experimental HR data. Therefore, AR spectra may be unreliable, and one must be careful in assigning pathophysiological origins to specific spectral features of any one spectrum.

Regulation of PaCO2 during rest and exercise: a modeling study.

AbstractA nonlinear mathematical model of the CO2 control system was used to examine a number of issues concerning the regulation of PaCO2 during rest and exercise. To gain insight to the regulatory properties of the respiratory system, the open loop gain (Gl) and closed loop sensitivities Si=ξPaCO2/ξPiCO2 and

Analysis of heart rate dynamics during spinal and epidural anesthesia

Sv = \partial PaCO_2 /\partial \dot Vco_2

Clinical Investigations: Miscellaneous: Clinical Trial: Journal Article: Randomized Controlled TrialChronic Cough Due to Gastroesophageal Reflux: Clinical, Diagnostic, and Pathogenetic Aspects

were calculated. Gl indicates the ability of a control system to regulate the controlled variable, PaCO2 in the model. Si and Sv represent the change in PaCO2 to unit changes in PiCO2 and

Fetal data processing system and method employing a time-frequency representation of fetal heart rate

\dot Vco_2