Estimation of the postmortem interval by means of ¹H MRS of decomposing brain tissue: influence of ambient temperature

Standard methods for the estimation of the postmortem interval (PMI, time since death), based on the cooling of the corpse, are limited to about 48 h after death. As an alternative, noninvasive postmortem observation of alterations of brain metabolites by means of (1)H MRS has been suggested for an estimation of the PMI at room temperature, so far without including the effect of other ambient temperatures. In order to study the temperature effect, localized (1)H MRS was used to follow brain decomposition in a sheep brain model at four different temperatures between 4 and 26°C with repeated measurements up to 2100 h postmortem. The simultaneous determination of 25 different biochemical compounds at each measurement allowed the time courses of concentration changes to be followed. A sudden and almost simultaneous change of the concentrations of seven compounds was observed after a time span that decreased exponentially from 700 h at 4°C to 30 h at 26°C ambient temperature. As this represents, most probably, the onset of highly variable bacterial decomposition, and thus defines the upper limit for a reliable PMI estimation, data were analyzed only up to this start of bacterial decomposition. As 13 compounds showed unequivocal, reproducible concentration changes during this period while eight showed a linear increase with a slope that was unambiguously related to ambient temperature. Therefore, a single analytical function with PMI and temperature as variables can describe the time courses of metabolite concentrations. Using the inverse of this function, metabolite concentrations determined from a single MR spectrum can be used, together with known ambient temperatures, to calculate the PMI of a corpse. It is concluded that the effect of ambient temperature can be reliably included in the PMI determination by (1)H MRS.

QTc interval and resting heart rate as long-term predictors of mortality in type 1 and type 2 diabetes mellitus: a 23-year follow-up

We evaluated the association of QT interval corrected for heart rate (QT(c)) and resting heart rate (rHR) with mortality (all-causes, cardiovascular, cardiac, and ischaemic heart disease) in subjects with type 1 and type 2 diabetes.

Delayed response and lack of habituation in plasma interleukin-6 to acute mental stress in men

Acute mental stress induces a significant increase in plasma interleukin (IL)-6 levels as a possible mechanism for how psychological stress might contribute to atherosclerosis. We investigated whether the IL-6 response would habituate in response to a repetitively applied mental stressor and whether cortisol reactivity would show a relationship with IL-6 reactivity. Study participants were 21 reasonably healthy men (mean age 46+/-7 years) who underwent the Trier Social Stress Test (combination of a 3-min preparation, 5-min speech, and 5-min mental arithmetic) three times with an interval of 1 week. Plasma IL-6 and free salivary cortisol were measured immediately before and after stress, and at 45 and 105 min of recovery from stress. Cortisol samples were also obtained 15 and 30 min after stress. Compared to non-stressed controls, IL-6 significantly increased between rest and 45 min post-stress (p=.022) and between rest and 105 min post-stress (p=.001). Peak cortisol (p=.034) and systolic blood pressure (p=.009) responses to stress both habituated between weeks one and three. No adaptation occurred in diastolic blood pressure, heart rate, and IL-6 responses to stress. The areas under the curve integrating the stress-induced changes in cortisol and IL-6 reactivity were negatively correlated at visit three (r=-.54, p=.011), but not at visit one. The IL-6 response to acute mental stress occurs delayed and shows no adaptation to repeated moderate mental stress. The hypothalamus-pituitary-adrenal axis may attenuate stress reactivity of IL-6. The lack of habituation in IL-6 responses to daily stress could subject at-risk individuals to higher atherosclerotic morbidity and mortality.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Smooth Hazard Function Estimation for Interval Censored Data with Time Varying Covariates

In this paper we propose methods for smooth hazard estimation of a time variable where that variable is interval censored. These methods allow one to model the transformed hazard in terms of either smooth (smoothing splines) or linear functions of time and other relevant time varying predictor variables. We illustrate the use of this method on a dataset of hemophiliacs where the outcome, time to seroconversion for HIV, is interval censored and left-truncated.

The Two-Interval Line-Segment Problem

In this paper, the NPMLE in the one-dimensional line segment problem is defined and studied, where line segments on the real line through two non-overlapping intervals are observed. The self-consistency equations for the NPMLE are defined and a quick algorithm for solving them is provided. Supnorm weak convergence to a Gaussian process and efficiency of the NPMLE is proved. The problem has a strong geological application in the study of the lifespan of species.

Intraobserver and interobserver variability of 64-row computed tomography abdominal aortic aneurysm neck measurements

BACKGROUND: Integrity of the abdominal aortic aneurysm (AAA) neck is crucial for the long-term success of endovascular AAA repair (EVAR). However, suitable tools for reliable assessment of changes in small aortic volumes are lacking. The purpose of this study was to assess the intraobserver and interobserver variability of software-enhanced 64-row computed tomographic angiography (CTA) AAA neck volume measurements in patients after EVAR. METHODS: A total of 25 consecutive patients successfully treated by EVAR underwent 64-row follow-up CTA in 1.5-mm collimation. Manual CTA measurements were performed twice by three blinded and independent readers in random order with at least a 4-week interval between readings. Maximum and minimum transverse aortic neck diameters were measured twice on two different levels within the proximal neck. Volumetry of the proximal aortic neck was performed by using dedicated software. Variability was calculated as 1.96 SD of the mean arithmetic difference according to Bland and Altman. Two-sided and paired t tests were used to compare measurements. P values <.05 were considered to indicate statistical significance. RESULTS: Intraobserver agreement was excellent for dedicated aneurysmal neck volumetry, with mean differences of less than 1 mL (P > .05), whereas it was poor for transverse aortic neck diameter measurements (P < .05). However, interobserver variability was statistically significant for both neck volumetry (P < .005) and neck diameter measurements (P < .015). CONCLUSIONS: The reliability of dedicated AAA neck volumetry by using 64-row CTA is excellent for serial measurements by individual readers, but not between different readers. Therefore, studies should be performed with aortic neck volumetry by a single experienced reader.

On the Accelerated Failure Time Model for Current Status and Interval Censored Data

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time (AFT) model for current status and interval censored data. The estimator is constructed by inverting a Wald type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo (MCMC) based resampling method is proposed to simultaneously obtain the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored data sets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite sample performance of the new estimators.