The Experts below are selected from a list of 49710 Experts worldwide ranked by ideXlab platform
Brian P. Griffin - One of the best experts on this subject based on the ideXlab platform.
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Radiation-Associated Cardiac Disease: From Molecular Mechanisms to Clinical Management
Current Treatment Options in Cardiovascular Medicine, 2019Co-Authors: Eoin Donnellan, Christine L. Jellis, Brian P. GriffinAbstract:Purpose of review Radiation-associated cardiac disease (RACD) is an increasingly recognized Latent manifestation of chest and mediastinal radiation therapy. The delayed presentation reflects increased survival rates from malignancies successfully treated decades previously. However, individuals are now presenting with multiple coexistent manifestations of RACD and pulmonary disease as a consequence of high-dose radiation administered prior to the routine institution of modern dose-modulating regimens. Increased awareness of RACD is critical for implementation of appropriate screening algorithms and for specific management strategies involving the timing and strategies of intervention in these patients. Recent findings Recent advances in multimodality cardiac imaging have demonstrated pathognomonic findings of RACD, which can predict outcomes including mortality. Accurate diagnosis of these typically concurrent manifestations is critical and should prompt referral to a center experienced in managing RACD as surgical risk is significantly increased for this patient cohort, particularly for those undergoing redo operation. Summary The Latent Effect of RACD and its unique combination of manifestations means that these patients will increasingly present with challenging management issues, resulting in increased rates of morbidity and mortality. Timing of treatment intervention must be carefully considered, although percutaneous options may provide alternative future strategies for this higher risk cohort.
Raphaël Huser - One of the best experts on this subject based on the ideXlab platform.
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Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster
Stochastic Environmental Research and Risk Assessment, 2018Co-Authors: Luigi Lombardo, Thomas Opitz, Raphaël HuserAbstract:We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random Effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial Latent Effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this Latent Effect provides valuable imaging support on the unobserved rainfall pattern.
Eoin Donnellan - One of the best experts on this subject based on the ideXlab platform.
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Radiation-Associated Cardiac Disease: From Molecular Mechanisms to Clinical Management
Current Treatment Options in Cardiovascular Medicine, 2019Co-Authors: Eoin Donnellan, Christine L. Jellis, Brian P. GriffinAbstract:Purpose of review Radiation-associated cardiac disease (RACD) is an increasingly recognized Latent manifestation of chest and mediastinal radiation therapy. The delayed presentation reflects increased survival rates from malignancies successfully treated decades previously. However, individuals are now presenting with multiple coexistent manifestations of RACD and pulmonary disease as a consequence of high-dose radiation administered prior to the routine institution of modern dose-modulating regimens. Increased awareness of RACD is critical for implementation of appropriate screening algorithms and for specific management strategies involving the timing and strategies of intervention in these patients. Recent findings Recent advances in multimodality cardiac imaging have demonstrated pathognomonic findings of RACD, which can predict outcomes including mortality. Accurate diagnosis of these typically concurrent manifestations is critical and should prompt referral to a center experienced in managing RACD as surgical risk is significantly increased for this patient cohort, particularly for those undergoing redo operation. Summary The Latent Effect of RACD and its unique combination of manifestations means that these patients will increasingly present with challenging management issues, resulting in increased rates of morbidity and mortality. Timing of treatment intervention must be carefully considered, although percutaneous options may provide alternative future strategies for this higher risk cohort.
Christine L. Jellis - One of the best experts on this subject based on the ideXlab platform.
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Radiation-Associated Cardiac Disease: From Molecular Mechanisms to Clinical Management
Current Treatment Options in Cardiovascular Medicine, 2019Co-Authors: Eoin Donnellan, Christine L. Jellis, Brian P. GriffinAbstract:Purpose of review Radiation-associated cardiac disease (RACD) is an increasingly recognized Latent manifestation of chest and mediastinal radiation therapy. The delayed presentation reflects increased survival rates from malignancies successfully treated decades previously. However, individuals are now presenting with multiple coexistent manifestations of RACD and pulmonary disease as a consequence of high-dose radiation administered prior to the routine institution of modern dose-modulating regimens. Increased awareness of RACD is critical for implementation of appropriate screening algorithms and for specific management strategies involving the timing and strategies of intervention in these patients. Recent findings Recent advances in multimodality cardiac imaging have demonstrated pathognomonic findings of RACD, which can predict outcomes including mortality. Accurate diagnosis of these typically concurrent manifestations is critical and should prompt referral to a center experienced in managing RACD as surgical risk is significantly increased for this patient cohort, particularly for those undergoing redo operation. Summary The Latent Effect of RACD and its unique combination of manifestations means that these patients will increasingly present with challenging management issues, resulting in increased rates of morbidity and mortality. Timing of treatment intervention must be carefully considered, although percutaneous options may provide alternative future strategies for this higher risk cohort.
Luigi Lombardo - One of the best experts on this subject based on the ideXlab platform.
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Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster
Stochastic Environmental Research and Risk Assessment, 2018Co-Authors: Luigi Lombardo, Thomas Opitz, Raphaël HuserAbstract:We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random Effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial Latent Effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this Latent Effect provides valuable imaging support on the unobserved rainfall pattern.
