Vasomotor instability preceding tilt-induced syncope: does respiration play a role?

Lipsitz, Lewis A., Raymond Morin, Margaret Gagnon, DanKiely, and Aharon Medina. Vasomotor instability precedingtilt-induced syncope: does respiration play a role? J. Appl. Physiol. 83(2): 383-390, 1997.This studyaimed to determine whether alterations in cardiovascular dynamicsbefore syncope are related to changes in spontaneous respiration.Fifty-two healthy subjects underwent continuous heart rate (HR),arterial blood pressure (BP), and respiratory measurements during10-min periods of spontaneous and paced breathing (0.25 Hz) in thesupine and 60° head-up tilt positions. Data were evaluated by powerspectrum and transfer function analyses. During tilt, 27 subjectsdeveloped syncope or presyncope and 25 remained asymptomatic. Subjectswith tilt-induced syncope had significantly greater increases inlow-frequency (0.04-0.15 Hz) systolic BP, diastolic BP, and HRpower during tilt than the asymptomatic subjects(P  0.01). This difference waspresent during spontaneous but not paced breathing. However, averagetidal volume, respiratory rate, minute ventilation, proportion ofbreaths below 0.15 Hz, and low-frequency respiratory power during tilt did not differ between syncopal and nonsyncopal subjects. Transfer magnitudes between low-frequency respiration and BP, and between BP andinterbeat interval, were also similar between groups. Thus vasomotorinstability before syncope is not related to alterations in respirationor the cardiovagal baroreflex but may reflect oscillating centralsympathetic outflow to the vasculature.

     

Methods for estimating the parameters of a linear model for ordered categorical data.

In many empirical analyses, the response of interest is categorical with an ordinal scale attached. Many investigators prefer to formulate a linear model, assigning scores to each category of the ordinal response and treating it as continuous. When the covariates are categorical, Haber (1985, Computational Statistics and Data Analysis 3, 1-10) has developed a method to obtain maximum likelihood (ML) estimates of the parameters of the linear model using Lagrange multipliers. However, when the covariates are continuous, the only method we found in the literature is ordinary least squares (OLS), performed under the assumption of homogeneous variance. The OLS estimates are unbiased and consistent but, since variance homogeneity is violated, the OLS estimates of variance can be biased and may not be consistent. We discuss a variance estimate (White, 1980, Econometrica 48, 817-838) that is consistent for the true variance of the OLS parameter estimates. The possible bias encountered by using the naive OLS variance estimate is discussed. An estimated generalized least squares (EGLS) estimator is proposed and its efficiency relative to OLS is discussed. Finally, an empirical comparison of OLS, EGLS, and ML estimators is made.     

Selection models and pattern-mixture models for incomplete data with covariates

Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.     

Multimodal pressure-flow method to assess dynamics of cerebral autoregulation in stroke and hypertension

Background

This study evaluated the effects of stroke on regulation of cerebral blood flow in response to fluctuations in systemic blood pressure (BP). The autoregulatory dynamics are difficult to assess because of the nonstationarity and nonlinearity of the component signals.

Methods

We studied 15 normotensive, 20 hypertensive and 15 minor stroke subjects (48.0 ± 1.3 years). BP and blood flow velocities (BFV) from middle cerebral arteries (MCA) were measured during the Valsalva maneuver (VM) using transcranial Doppler ultrasound.

Results

A new technique, multimodal pressure-flow analysis (MMPF), was implemented to analyze these short, nonstationary signals. MMPF analysis decomposes complex BP and BFV signals into multiple empirical modes, representing their instantaneous frequency-amplitude modulation. The empirical mode corresponding to the VM BP profile was used to construct the continuous phase diagram and to identify the minimum and maximum values from the residual BP (BP_R) and BFV (BFV_R) signals. The BP-BFV phase shift was calculated as the difference between the phase corresponding to the BP_R and BFV_R minimum (maximum) values. BP-BFV phase shifts were significantly different between groups. In the normotensive group, the BFV_R minimum and maximum preceded the BP_R minimum and maximum, respectively, leading to large positive values of BP-BFV shifts.

Conclusion

In the stroke and hypertensive groups, the resulting BP-BFV phase shift was significantly smaller compared to the normotensive group. A standard autoregulation index did not differentiate the groups. The MMPF method enables evaluation of autoregulatory dynamics based on instantaneous BP-BFV phase analysis. Regulation of BP-BFV dynamics is altered with hypertension and after stroke, rendering blood flow dependent on blood pressure.

     

Predicting the dynamic postural control response from quiet-stance behavior in elderly adults

Human postural sway, as measured by fluctuations of the center of pressure (COP) under the feet of a quietly standing individual, can be characterized as a stochastic process. The fluctuation-dissipation theorem (FDT) provides a linear relationship between the fluctuations of a quasi-static, stochastic system to the same system's relaxation to equilibrium following a perturbation. We applied a similar linear relationship, based on the FDT, to the human postural control system to explore whether anterior-posterior (AP) fluctuations of the COP during quiet stance can be used to predict the AP response of the postural control system to a weak posteriorly directed mechanical perturbation (tug or pull at the waist). We tested 10 healthy elderly (mean age of 69yr) and 10 healthy young (mean age of 25yr) adult subjects. We found that this linear relationship was applicable to the postural control system of all 10 young and eight of the 10 elderly adult subjects. These results suggest that it is possible to predict an individual's dynamic response to a mild perturbation using quiet-stance data, regardless of age. The existence of this FDT-based linear relationship with respect to the human postural control system suggests that, for a given individual, the postural control system may use the same control mechanisms during quiet stance and mild-perturbation conditions, regardless of age.     

A sensitive and selective liquid chromatography/tandem mass spectrometry method for determination of MLN8237 in human plasma

We describe a selective and a highly sensitive high-performance liquid chromatography–electron spray ionization-collision induced dissociation-tandem mass spectrometry (HPLC–ESI-CID-MS/MS) assay for the Aurora A kinase inhibitor MLN8237 in human plasma. The intra-day precision based on the standard deviation of replicates of quality control samples ranged from 0.2 to 4% and with accuracy ranging from 96 to 102%. The inter-day precision ranged from 0.5 to 7% and the accuracy ranged from 93 to 105%. Stability studies showed that MLN8237 was stable both during the expected conditions for sample preparation and storage. The lower limit of quantification for MLN8237 was 5 ng/mL. The analytical method showed excellent sensitivity, precision, and accuracy. This method is robust and is being successfully employed in a Children's Oncology Group Phase 1 Consortium study of MLN8237 in children with cancer.     

Association Models for Clustered Data with Binary and Continuous Responses

Summary .  We consider analysis of clustered data with mixed bivariate responses, i.e., where each member of the cluster has a binary and a continuous outcome. We propose a new bivariate random effects model that induces associations among the binary outcomes within a cluster, among the continuous outcomes within a cluster, between a binary outcome and a continuous outcome from different subjects within a cluster, as well as the direct association between the binary and continuous outcomes within the same subject. For the ease of interpretations of the regression effects, the marginal model of the binary response probability integrated over the random effects preserves the logistic form and the marginal expectation of the continuous response preserves the linear form. We implement maximum likelihood estimation of our model parameters using standard software such as PROC NLMIXED of SAS . Our simulation study demonstrates the robustness of our method with respect to the misspecification of the regression model as well as the random effects model. We illustrate our methodology by analyzing a developmental toxicity study of ethylene glycol in mice.     

A bivariate pseudolikelihood for incomplete longitudinal binary data with nonignorable nonmonotone missingness

For analyzing longitudinal binary data with nonignorable and nonmonotone missing responses, a full likelihood method is complicated algebraically, and often requires intensive computation, especially when there are many follow-up times. As an alternative, a pseudolikelihood approach has been proposed in the literature under minimal parametric assumptions. This formulation only requires specification of the marginal distributions of the responses and missing data mechanism, and uses an independence working assumption. However, this estimator can be inefficient for estimating both time-varying and time-stationary effects under moderate to strong within-subject associations among repeated responses. In this article, we propose an alternative estimator, based on a bivariate pseudolikelihood, and demonstrate in simulations that the proposed method can be much more efficient than the previous pseudolikelihood obtained under the assumption of independence. We illustrate the method using longitudinal data on CD4 counts from two clinical trials of HIV-infected patients.     

A global goodness-of-fit statistic for Cox regression models

In this paper, a global goodness-of-fit test statistic for a Cox regression model, which has an approximate chi-squared distribution when the model has been correctly specified, is proposed. Our goodness-of-fit statistic is global and has power to detect if interactions or higher order powers of covariates in the model are needed. The proposed statistic is similar to the Hosmer and Lemeshow (1980, Communications in Statistics A10, 1043-1069) goodness-of-fit statistic for binary data as well as Schoenfeld's (1980, Biometrika 67, 145-153) statistic for the Cox model. The methods are illustrated using data from a Mayo Clinic trial in primary billiary cirrhosis of the liver (Fleming and Harrington, 1991, Counting Processes and Survival Analysis), in which the outcome is the time until liver transplantation or death. The are 17 possible covariates. Two Cox proportional hazards models are fit to the data, and the proposed goodness-of-fit statistic is applied to the fitted models.