Deep Vein Thrombosis of the Left Leg: A Case of May-Thurner Syndrome

A 56-year-old woman presented with gradually worsening shortness of breath associated with dull left leg pain over 5 days. She denied any recent travel, recent surgeries or immobilization. CT pulmonary angiography and CT venography revealed multiple bilateral pulmonary emboli and extensive left pelvic and left lower extremity deep vein thromboses. Contrast-enhanced CT showed that the right common iliac artery crossed the left common iliac vein and compressed it externally, indicative of May–Thurner syndrome. Catheter-directed thrombolysis of the left lower extremity was performed and heparin infusion was started. The patient also underwent left iliac vein balloon angioplasty with stenting and infra-renal inferior vena cava filter placement via the jugular approach to prevent further embolization

Robust Bayesian model averaging for the analysis of presence-absence data

When developing a species distribution model, usually one tests several competing models such as logistic regressions characterized by different sets of covariates. Yet, there is an exponential number of subsets of covariates to choose from. This generates the problem of model uncertainty. Bayesian model averaging (BMA) is a state-of-the-art approach to deal with model uncertainty. BMA weights the inferences of multiple models. However, the results yielded by BMA depend on the prior probability assigned to the models. Credal model averaging (CMA) extends BMA towards robustness. It substitutes the single prior over the models by a set of priors. The CMA inferences (e.g., posterior probability of inclusion of a covariate, coefficient of a covariate, probability of presence) are intervals. The interval shows the sensitivity of the BMA estimate on the prior over the models. CMA detects the prior-dependent instances, namely cases in which the most probable outcome becomes presence or absence depending on the adopted prior over the models. On such prior-dependent instances, BMA behaves almost as a random guesser. The weakness of BMA on the prior-dependent instances is to our knowledge pointed out for the first time in the ecological literature. On the prior-dependent instances CMA avoids random guessing acknowledging undecidability. In this way it stimulates the decision maker to convey further information before taking the decision. We provide thorough experiments on different data sets