Post-mortem virtual estimation of free abdominal blood volume

PURPOSE: The purpose of this retrospective study was to examine the reliability of virtually estimated abdominal blood volume using segmentation from postmortem computed tomography (PMCT) data. MATERIALS AND METHODS: Twenty-one cases with free abdominal blood were investigated by PMCT and autopsy. The volume of the blood was estimated using a manual segmentation technique (Amira, Visage Imaging, Germany) and the results were compared to autopsy data. Six of 21 cases had undergone additional post-mortem computed tomographic angiography (PMCTA). RESULTS: The virtually estimated abdominal blood volumes did not differ significantly from those measured at autopsy. Additional PMCTA did not bias data significantly. CONCLUSION: Virtual estimation of abdominal blood volume is a reliable technique. The virtual blood volume estimation is a useful tool to deliver additional information in cases where autopsy is not performed or in cases where a postmortem angiography is performed.

Survey estimation and marginal effects in Stata

Stata is a general purpose software package that has become popular among various disciplines such as epidemiology, economics, or social sciences. Users like Stata for its scientific approach, its robustness and reliability, and the ease with which its functionality can be extended by user written programs. In this talk I will first give a brief overview of the functionality of Stata and then discuss two specific features: survey estimation and predictive margins/marginal effects. Most surveys are based on complex samples that contain multiple sampling stages, are stratified or clustered, and feature unequal selection probabilities. Standard estimators can produce misleading results in such samples unless the peculiarities of the sampling plan are taken into account. Stata offers survey statistics for complex samples for a wide variety of estimators and supports several variance estimation procedures such as linearization, jackknife, and balanced repeated replication (see Kreuter and Valliant, 2007, Stata Journal 7: 1-21). In the talk I will illustrate these features using applied examples and I will also show how user written commands can be adapted to support complex samples. Complex can also be the models we fit to our data, making it difficult to interpret them, especially in case of nonlinear or non-additive models (Mood, 2010, European Sociological Review 26: 67-82). Stata provides a number of highly useful commands to make results of such models accessible by computing and displaying predictive margins and marginal effects. In my talk I will discuss these commands provide various examples demonstrating their use.

Estimation of heart weight by post-mortem cardiac magnetic resonance imaging

Introduction The aim of this study was to determine which single measurement on post-mortem cardiac MR reflects actual heart weight as measured at autopsy, assess the intra- and inter-observer reliability of MR measurements, derive a formula to predict heart weight from MR measurements and test the accuracy of the formula to prospectively predict heart weight. Materials and methods 53 human cadavers underwent post-mortem cardiac MR and forensic autopsy. In Phase 1, left ventricular area and wall thickness were measured on short axis and four chamber view images of 29 cases. All measurements were correlated to heart weight at autopsy using linear regression analysis. In Phase 2, single left ventricular area measurements on four chamber view images (LVA_4C) from 24 cases were used to predict heart weight at autopsy based on equations derived during Phase 1. Intra-class correlation coefficient (ICC) was used to determine inter- and intra-reader agreement. Results Heart weight strongly correlates with LVA_4C (r=0.78 M; p<0.001). Intra-reader and inter-reader reliability was excellent for LVA_4C (ICC=0.81–0.91; p<0.001 and ICC=0.90; p<0.001 respectively). A simplified formula for heart weight ([g]≈LVA_4C [mm2]×0.11) was derived based on linear regression analysis. Conclusions This study shows that single circumferential area measurements of the left ventricle in the four chamber view on post-mortem cardiac MR reflect actual heart weight as measured at autopsy. These measurements yield an excellent intra- and inter-reader reliability and can be used to predict heart weight prior to autopsy or to give a reasonable estimate of heart weight in cases where autopsy is not performed.

Reliability of the cervical vertebrae maturation (CVM) method

OBJECTIVE To assess the reliability of the cervical vertebrae maturation method (CVM). BACKGROUND Skeletal maturity estimation can influence the manner and time of orthodontic treatment. The CVM method evaluates skeletal growth on the basis of the changes in the morphology of cervical vertebrae C2, C3, C4 during growth. These vertebrae are visible on a lateral cephalogram, so the method does not require an additional radiograph. METHODS In this website based study, 10 orthodontists with a long clinical practice (3 routinely using the method - "Routine user - RU" and 7 with less experience in the CVM method - "Non-Routine user - nonRU") rated twice cervical vertebrae maturation with the CVM method on 50 cropped scans of lateral cephalograms of children in circumpubertal age (for boys: 11.5 to 15.5 years; for girls: 10 to 14 years). Kappa statistics (with lower limits of 95% confidence intervals (CI)) and proportion of complete agreement on staging was used to evaluate intra- and inter-assessor agreement. RESULTS The mean weighted kappa for intra-assessor agreement was 0.44 (range: 0.30-0.64; range of lower limits of 95% CI: 0.12-0.48) and for inter-assessor agreement was 0.28 (range: -0.01-0.58; range of lower limits of 95% CI: -0.14-0.42). The mean proportion of identical scores assigned by the same assessor was 55.2 %(range: 44-74 %) and for different pairs of assessors was 42 % (range: 16-68 %). CONCLUSIONS The reliability of the CVM method is questionable and if orthodontic treatment should be initiated relative to the maximum growth, the use of additional biologic indicators should be considered (Tab. 4, Fig. 1, Ref. 24).