Impact of pathologic body composition assessed by CT-based anthropometric measurements in adult patients with multiple trauma: a retrospective analysis.

PURPOSE: In recent years, there has been mounting evidence on the clinical importance of body composition, particularly obesity and sarcopenia, in various patient populations. However, the relevance of these pathologic conditions remains controversial, especially in the field of traumatology. Computed tomography-based measurements allow clinicians to gain a prompt and thorough assessment of fat and muscle compartments in trauma patients. Our aim was to investigate whether CT-based anthropometric parameters of fat and muscle tissues show correlations with key elements of pre-hospital and clinical care in an adult population with multiple trauma. METHODS: In this retrospective analysis we searched our institutional records of the German Trauma Registry (TraumaRegister DGU®) from January 2008 to May 2014. Included were 297 adult trauma patients with multiple trauma who underwent a whole-body CT-scan on admission and were treated in an ICU. We measured anthropometric determinants of abdominal core muscle and adipose tissue using the digital imaging software OsiriX™. Multivariate linear and logistic regression analyses were conducted to unveil potential correlations. RESULTS: None of the obesity-linked anthropometric parameters were associated with longer pre-hospital or initial ED treatment times. Obese patients were less frequently intubated at the site of the accident. Patients with increased abdominal fat tissue received on average lower volumes during fluid resuscitation in the pre-hospital phase but were not more often in shock on admission. During ED treatment, fluid resuscitation and transfusion volumes were not affected by abdominal fat tissue, although transfusion rates were higher in the obese. Furthermore, damage control surgeries took place less frequently in patients with increased abdominal fat tissue markers. Obesity parameters did not affect the prevalence of sepsis, although increased abdominal fat was associated with higher white blood cell counts on admission. Finally, there was no statistically significant correlation between sarcopenia or obesity markers and duration of mechanical ventilation, ICU length of stay or neurologic outcome. CONCLUSION: CT-based assessment of abdominal fat and muscle mass is a simple method in revealing pathologic body composition in trauma patients. Our study suggests that obesity influences pre-hospital and ED treatment and early immune response in multiple trauma. Nevertheless, we could not demonstrate any significant effect of abdominal fat and muscle tissue parameters on the course of treatment, in particular the duration of mechanical ventilation, ICU length of stay and neurologic outcome.

On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study.

BACKGROUND: The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. METHODS: Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. RESULTS: The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. CONCLUSIONS: The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

A plea for taking all available clinical information into account when assessing the predictive value of omics data.

BACKGROUND: Omics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on integrating omics and clinical data, yet has often ignored the need for appropriate model building for clinical variables. Medical literature on classical prognostic scores, as well as biostatistical literature on appropriate model selection strategies for low dimensional (clinical) data, are often ignored in the context of omics research. The goal of this paper is to fill this methodological gap by investigating the added predictive value of gene expression data for models using varying amounts of clinical information. METHODS: We analyze two data sets from the field of survival prognosis of breast cancer patients. First, we construct several proportional hazards prediction models using varying amounts of clinical information based on established medical knowledge. These models are then used as a starting point (i.e. included as a clinical offset) for identifying informative gene expression variables using resampling procedures and penalized regression approaches (model based boosting and the LASSO). In order to assess the added predictive value of the gene signatures, measures of prediction accuracy and separation are examined on a validation data set for the clinical models and the models that combine the two sources of information. RESULTS: For one data set, we do not find any substantial added predictive value of the omics data when compared to clinical models. On the second data set, we identify a noticeable added predictive value, however only for scenarios where little or no clinical information is included in the modeling process. We find that including more clinical information can lead to a smaller number of selected omics predictors. CONCLUSIONS: New research using omics data should include all available established medical knowledge in order to allow an adequate evaluation of the added predictive value of omics data. Including all relevant clinical information in the analysis might also lead to more parsimonious models. The developed procedure to assess the predictive value of the omics data can be readily applied to other scenarios.