Line-scan diffusion tensor imaging of the posttraumatic brain stem: changes with neuropathologic correlation

Following trauma, imaging of brain stem lesions is often inconclusive. In a man who suffered a lethal accident, postmortem MR diffusion tensor (DT) imaging of the brain and neuropathologic examination were performed. DT imaging showed a disorganization of fibers in the brain stem that was not found in 2 controls and corresponded to changes on neuropathologic correlation. Diffusion tensor imaging provides an insight into the organization of myelinated structures of the CNS, potentially allowing diagnosis of traumatic fiber tract rupture.

Myocardial bridging: depiction rate and morphology at CT coronary angiography--comparison with conventional coronary angiography

PURPOSE: To prospectively assess the depiction rate and morphologic features of myocardial bridging (MB) of coronary arteries with 64-section computed tomographic (CT) coronary angiography in comparison to conventional coronary angiography. MATERIALS AND METHODS: Patients were simultaneously enrolled in a prospective study comparing CT and conventional coronary angiography, for which ethics committee approval and informed consent were obtained. One hundred patients (38 women, 62 men; mean age, 63.8 years +/- 11.6 [standard deviation]) underwent 64-section CT and conventional coronary angiography. Fifty additional patients (19 women, 31 men; mean age, 59.2 years +/- 13.2) who underwent CT only were also included. CT images were analyzed for the direct signs length, depth, and degree of systolic compression, while conventional angiograms were analyzed for the indirect signs step down-step up phenomenon, milking effect, and systolic compression of the tunneled segment. Statistical analysis was performed with Pearson correlation analysis, the Wilcoxon two-sample test, and Fisher exact tests. RESULTS: MB was detected with CT in 26 (26%) of 100 patients and with conventional angiography in 12 patients (12%). Mean tunneled segment length and depth at CT (n = 150) were 24.3 mm +/- 10.0 and 2.6 mm +/- 0.8, respectively. Systolic compression in the 12 patients was 31.3% +/- 11.0 at CT and 28.2% +/- 10.5 at conventional angiography (r = 0.72, P < .001). With CT, a significant correlation was not found between systolic compression and length (r = 0.16, P = .25, n = 150) but was found with depth (r = 0.65, P < .01, n = 150) of the tunneled segment. In 14 patients in whom MB was found at CT but not at conventional angiography, length, depth, and systolic compression were significantly lower than in patients in whom both modalities depicted the anomaly (P < .001, P < .01, and P < .001, respectively). CONCLUSION: The depiction rate of MB is greater with 64-section CT coronary angiography than with conventional coronary angiography. The degree of systolic compression of MB significantly correlates with tunneled segment depth but not length.

An empirical comparison of methods for meta-analysis of diagnostic accuracy showed hierarchical models are necessary

OBJECTIVE: Meta-analysis of studies of the accuracy of diagnostic tests currently uses a variety of methods. Statistically rigorous hierarchical models require expertise and sophisticated software. We assessed whether any of the simpler methods can in practice give adequately accurate and reliable results. STUDY DESIGN AND SETTING: We reviewed six methods for meta-analysis of diagnostic accuracy: four simple commonly used methods (simple pooling, separate random-effects meta-analyses of sensitivity and specificity, separate meta-analyses of positive and negative likelihood ratios, and the Littenberg-Moses summary receiver operating characteristic [ROC] curve) and two more statistically rigorous approaches using hierarchical models (bivariate random-effects meta-analysis and hierarchical summary ROC curve analysis). We applied the methods to data from a sample of eight systematic reviews chosen to illustrate a variety of patterns of results. RESULTS: In each meta-analysis, there was substantial heterogeneity between the results of different studies. Simple pooling of results gave misleading summary estimates of sensitivity and specificity in some meta-analyses, and the Littenberg-Moses method produced summary ROC curves that diverged from those produced by more rigorous methods in some situations. CONCLUSION: The closely related hierarchical summary ROC curve or bivariate models should be used as the standard method for meta-analysis of diagnostic accuracy.

Erythropoietin or Darbepoetin for patients with cancer--meta-analysis based on individual patient data

BACKGROUND: Erythropoiesis-stimulating agents (ESAs) reduce anemia in cancer patients and may improve quality of life, but there are concerns that ESAs might increase mortality. OBJECTIVES: Our objectives were to examine the effect of ESAs and identify factors that modify the effects of ESAs on overall survival, progression free survival, thromboembolic and cardiovascular events as well as need for transfusions and other important safety and efficacy outcomes in cancer patients. SEARCH STRATEGY: We searched the Cochrane Library, Medline, Embase and conference proceedings for eligible trials. Manufacturers of ESAs were contacted to identify additional trials. SELECTION CRITERIA: We included randomized controlled trials comparing epoetin or darbepoetin plus red blood cell transfusions (as necessary) versus red blood cell transfusions (as necessary) alone, to prevent or treat anemia in adult or pediatric cancer patients with or without concurrent antineoplastic therapy. DATA COLLECTION AND ANALYSIS: We performed a meta-analysis of randomized controlled trials comparing epoetin alpha, epoetin beta or darbepoetin alpha plus red blood cell transfusions versus transfusion alone, for prophylaxis or therapy of anemia while or after receiving anti-cancer treatment. Patient-level data were obtained and analyzed by independent statisticians at two academic departments, using fixed-effects and random-effects meta-analysis. Analyses were according to the intention-to-treat principle. Primary endpoints were on study mortality and overall survival during the longest available follow-up, regardless of anticancer treatment, and in patients receiving chemotherapy. Tests for interactions were used to identify differences in effects of ESAs on mortality across pre-specified subgroups. The present review reports only the results for the primary endpoint. MAIN RESULTS: A total of 13933 cancer patients from 53 trials were analyzed, 1530 patients died on-study and 4993 overall. ESAs increased on study mortality (combined hazard ratio [cHR] 1.17; 95% CI 1.06-1.30) and worsened overall survival (cHR 1.06; 95% CI 1.00-1.12), with little heterogeneity between trials (I(2) 0%, p=0.87 and I(2) 7.1%, p=0.33, respectively). Thirty-eight trials enrolled 10441 patients receiving chemotherapy. The cHR for on study mortality was 1.10 (95% CI 0.98-1.24) and 1.04; 95% CI 0.97-1.11) for overall survival. There was little evidence for a difference between trials of patients receiving different cancer treatments (P for interaction=0.42). AUTHORS' CONCLUSIONS: ESA treatment in cancer patients increased on study mortality and worsened overall survival. For patients undergoing chemotherapy the increase was less pronounced, but an adverse effect could not be excluded.

Taking an Object-Centric View on Dynamic Information with Object Flow Analysis

A large body of research analyzes the runtime execution of a system to extract abstract behavioral views. Those approaches primarily analyze control flow by tracing method execution events or they analyze object graphs of heap snapshots. However, they do not capture how objects are passed through the system at runtime. We refer to the exchange of objects as the object flow, and we claim that object flow is necessary to analyze if we are to understand the runtime of an object-oriented application. We propose and detail Object Flow Analysis, a novel dynamic analysis technique that takes this new information into account. To evaluate its usefulness, we present a visual approach that allows a developer to study classes and components in terms of how they exchange objects at runtime. We illustrate our approach on three case studies.

Stakeholder analysis combined with social network analysis provides fine-grained insights into water infrastructure planning processes

Environmental policy and decision-making are characterized by complex interactions between different actors and sectors. As a rule, a stakeholder analysis is performed to understand those involved, but it has been criticized for lacking quality and consistency. This lack is remedied here by a formal social network analysis that investigates collaborative and multi-level governance settings in a rigorous way. We examine the added value of combining both elements. Our case study examines infrastructure planning in the Swiss water sector. Water supply and wastewater infrastructures are planned far into the future, usually on the basis of projections of past boundary conditions. They affect many actors, including the population, and are expensive. In view of increasing future dynamics and climate change, a more participatory and long-term planning approach is required. Our specific aims are to investigate fragmentation in water infrastructure planning, to understand how actors from different decision levels and sectors are represented, and which interests they follow. We conducted 27 semi-structured interviews with local stakeholders, but also cantonal and national actors. The network analysis confirmed our hypothesis of strong fragmentation: we found little collaboration between the water supply and wastewater sector (confirming horizontal fragmentation), and few ties between local, cantonal, and national actors (confirming vertical fragmentation). Infrastructure planning is clearly dominated by engineers and local authorities. Little importance is placed on longer-term strategic objectives and integrated catchment planning, but this was perceived as more important in a second analysis going beyond typical questions of stakeholder analysis. We conclude that linking a stakeholder analysis, comprising rarely asked questions, with a rigorous social network analysis is very fruitful and generates complementary results. This combination gave us deeper insight into the socio-political-engineering world of water infrastructure planning that is of vital importance to our well-being.

Association between trends in clinical variables and outcome in intensive care patients with faecal peritonitis: analysis of the GenOSept cohort.

INTRODUCTION Patients admitted to intensive care following surgery for faecal peritonitis present particular challenges in terms of clinical management and risk assessment. Collaborating surgical and intensive care teams need shared perspectives on prognosis. We aimed to determine the relationship between dynamic assessment of trends in selected variables and outcomes. METHODS We analysed trends in physiological and laboratory variables during the first week of intensive care unit (ICU) stay in 977 patients at 102 centres across 16 European countries. The primary outcome was 6-month mortality. Secondary endpoints were ICU, hospital and 28-day mortality. For each trend, Cox proportional hazards (PH) regression analyses, adjusted for age and sex, were performed for each endpoint. RESULTS Trends over the first 7 days of the ICU stay independently associated with 6-month mortality were worsening thrombocytopaenia (mortality: hazard ratio (HR) = 1.02; 95% confidence interval (CI), 1.01 to 1.03; P <0.001) and renal function (total daily urine output: HR =1.02; 95% CI, 1.01 to 1.03; P <0.001; Sequential Organ Failure Assessment (SOFA) renal subscore: HR = 0.87; 95% CI, 0.75 to 0.99; P = 0.047), maximum bilirubin level (HR = 0.99; 95% CI, 0.99 to 0.99; P = 0.02) and Glasgow Coma Scale (GCS) SOFA subscore (HR = 0.81; 95% CI, 0.68 to 0.98; P = 0.028). Changes in renal function (total daily urine output and renal component of the SOFA score), GCS component of the SOFA score, total SOFA score and worsening thrombocytopaenia were also independently associated with secondary outcomes (ICU, hospital and 28-day mortality). We detected the same pattern when we analysed trends on days 2, 3 and 5. Dynamic trends in all other measured laboratory and physiological variables, and in radiological findings, changes inrespiratory support, renal replacement therapy and inotrope and/or vasopressor requirements failed to be retained as independently associated with outcome in multivariate analysis. CONCLUSIONS Only deterioration in renal function, thrombocytopaenia and SOFA score over the first 2, 3, 5 and 7 days of the ICU stay were consistently associated with mortality at all endpoints. These findings may help to inform clinical decision making in patients with this common cause of critical illness.

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.