Management of acute hypercortisolism

An occasional patient with Cushing's syndrome may require urgent management primarily because the chronic ravages of hypercortisolism have caused the patient to be in a precarious metabolic condition. The side effects of prolonged excess corticosteroids increase the risk of operations in such patients and must be considered in overall management. Among the many effects of hypercortisolism to be considered are hypertension, diabetes, ocular hypertension, myopathies, dermatologic changes including skin infection, pancreatitis, osteoporosis, pathological fractures, peptic ulcers, renal calculi, coagulopathies, hypokalemia, poor wound healing, and increased susceptibility to infection. The most effective way to avert these complications is by earlier diagnosis and definitive treatment of Cushing's syndrome. The present report includes a review of the etiology and diagnosis of Cushing's syndrome and the management of problems associated with hypercortisolism . Il est possible qu'un malade atteint de maladie de Cushing ait besoin d'être traité sans attente en raisons de troubles métaboliques sévères dus aux effets nocifs de l'hypercortisolisme chronique qui augmentent les risques opératoires et doivent être pris en considération avant tout traitement. Il en est ainsi de l'hypertension, du diabète, de l'hypertension intra-oculaire, des lésions dermiques comprenant l'infection cutanée, la pancréatite, l'ostéoporose, les fractures pathologiques, l'ulcère peptique, les calculs rénaux, les coagulopathies, l'hypokaliémie, la lenteur du processus de cicatrisation et l'augmentation de la suceptibilité à l'infection.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41309/1/268_2005_Article_BF01655367.pd

Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies