Surgical management of foramen magnum meningiomas

The objective of the study is to describe our experience in the surgical management of foramen magnum meningiomas with regard to the clinical-radiological findings, the surgical approach and the outcomes after mid-term follow up. Over a 5-year period, 15 patients presenting with meningiomas of the foramen magnum underwent surgical treatment. The medical records were reviewed in order to analyze the clinical-radiological aspects, as well as the surgical approach and the outcomes. Based on the preoperative magnetic resonance imaging exams, the tumors were classified as anterior or anterolateral in the axial slices and clivospinal or spinoclival in the sagittal slices. The lateral approach was used in all cases. However, the extent of bone removal and the management of the vertebral artery were tailored to each patient. Fourteen patients were females, and one was male, ranging in age from 42 to 74 years (mean 55,9 years). The occipital condyle was partially removed in eight patients, and in seven patients, removal was not necessary. Total removal of the tumor was achieved in 12 patients, subtotal in two, and partial resection in one patient. Postoperative complications occurred in two patients. Follow-up ranged from 6 to 56 months (mean 23.6 months).There was no surgical mortality in this series. The extent of the surgical approach to foramen magnum meningiomas must be based on the main point of dural attachment and tailored individually case-by-case. The differentiation between the clivospinal and spinoclival types, as well as anterior and anterolateral types, is crucial for the neurosurgical planning of foramen magnum meningiomas.

Management of acute psychostimulant toxicity

Psychostimulants produce a broad range of effects. Adverse effects can exist on a spectrum of severity from minor symptoms to life threatening toxicity. Although regular use or use of high doses increases risk of adverse events, many adverse events requiring emergency intervention may occur even in the naïve user.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Heart failure disease management program experience in 4,545 heart failure admissions to a community hospital

Background Disease management programs (DMPs) are developed to address the high morbi-mortality and costs of congestive heart failure (CHF). Most studies have focused on intensive programs in academic centers. Washington County Hospital (WCH) in Hagerstown, MD, the primary reference to a semirural county, established a CHF DMP in 2001 with standardized documentation of screening and participation. Linkage to electronic records and state vital statistics enabled examination of the CHF population including individuals participating and those ineligible for the program. Methods All WCH inpatients with CHF International Classification of Diseases, Ninth Revision code in any position of the hospital list discharged alive. Results Of 4,545 consecutive CHF admissions, only 10% enrolled and of those only 52.2% made a call. Enrollment in the program was related to: age (OR 0.64 per decade older, 95% CI 0.58-0.70), CHF as the main reason for admission (OR 3.58, 95% CI 2.4-4.8), previous admission for CHF (OR 1.14, 95% CI 1.09-1.2), and shorter hospital stay (OR 0.94 per day longer, 95% CI 0.87-0.99). Among DMP participants mortality rates were lowest in the first month (80/1000 person-years) and increased subsequently. The opposite mortality trend occurred in nonenrolled groups with mortality in the first month of 814 per 1000 person-years in refusers and even higher in ineligible (1569/1000 person-years). This difference remained significant after adjustment. Re-admission rates were lower among participants who called consistently (adjusted incidence rate ratio 0.62, 95% CI 0.52-0.77). Conclusion Only a small and highly select group participated in a low-intensity DMP for CHF in a community-based hospital. Design of DMPs should incorporate these strong selective factors to maximize program impact. (Am Heart J 2009; 15 8:459-66.)

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.