Late Reopening of Adequately Coiled Intracranial Aneurysms Frequency and Risk Factors in 400 Patients With 440 Aneurysms

Background and Purpose-In aneurysms that are adequately occluded 6 months after coiling, the risk of late reopening is largely unknown. We assessed the occurrence of late aneurysm reopening and possible risk factors. Methods-From January 1995 to June 2005, 1808 intracranial aneurysms were coiled in 1675 patients at 7 medical centers. At 6 months, 1066 aneurysms in 971 patients were adequately occluded. At mean 6.0 years after coiling, of the 971 patients, 400 patients with 440 aneurysms underwent 3 Tesla magnetic resonance angiography to assess occlusion status of the aneurysms. Proportions and corresponding 95% CI of aneurysm reopening and retreatment were calculated. Risk factors for late reopening were assessed by univariate and multivariate logistic regression analysis, and included patient sex, rupture status of aneurysms, aneurysm size >= 10 mm, and aneurysm location. Results-In 11 of 400 patients (2.8%; 95% CI, 1.4-4.9%) with 440 aneurysms (2.5%; 95% CI, 1.0-4.0%), late reopening had occurred; 3 reopened aneurysms were retreated (0.7%; 95% CI, 0.2-1.5%). Independent predictors for late reopening were aneurysm size >= 10 mm (OR 4.7; 95% CI, 1.3-16.3) and location on basilar tip (OR 3.9; 95% CI, 1.1-14.6). There were no late reopenings in the 143 anterior cerebral artery aneurysms. Conclusions-For the vast majority of adequately occluded intracranial aneurysms 6 months after coiling (those <10 mm and not located on basilar tip), prolonged imaging follow-up within the first 5 to 10 years after coiling does not seem beneficial in terms of detecting reopened aneurysms that need retreatment. Whether patients might benefit from screening beyond the 5- to 10-year interval is not yet clear. (Stroke. 2011;42:1331-1337.)Neuro Imaging Researc

Probabilistic model checking of labelled Markov processes via finite approximate bisimulations

This paper concerns labelled Markov processes (LMPs), probabilistic models over uncountable state spaces originally introduced by Prakash Panangaden and colleagues. Motivated by the practical application of the LMP framework, we study its formal semantics and the relationship to similar models formulated in control theory. We consider notions of (exact and approximate) probabilistic bisimulation over LMPs and, drawing on methods from both formal verification and control theory, propose a simple technique to compute an approximate probabilistic bisimulation of a given LMP, where the resulting abstraction is characterised as a finite-state labelled Markov chain (LMC). This construction enables the application of automated quantitative verification and policy synthesis techniques over the obtained abstract model, which can be used to perform approximate analysis of the concrete LMP. We illustrate this process through a case study of a multi-room heating system that employs the probabilistic model checker PRISM