Effects of a nurse-led, clinic and home-based intervention on recurrent hospital use in chronic heart failure

Background: Few studies have examined the potential benefits of specialist nurse-led programs of care involving home and clinic-based follow-up to optimise the post-discharge management of chronic heart failure (CHF). Objective: To determine the effectiveness of a hybrid program of clinic plus home-based intervention (C+HBI) in reducing recurrent hospitalisation in CHF patients. Methods: CHF patients with evidence of left ventricular systolic dysfunction admitted to two hospitals in Northern England were assigned to a C+HBI lasting 6 months post-discharge (n=58) or to usual, post-discharge care (UC: n=48) via a cluster randomization protocol. The co-primary endpoints were death or unplanned readmission (event-free survival) and rate of recurrent, all-cause readmission within 6 months of hospital discharge. Results: During study follow-up, more UC patients had an unplanned readmission for any cause (44% vs. 22%: P=0.0191 OR 1.95 95% CI 1.10-3.48) whilst 7 (15%) versus 5 (9%) UC and C+HBI patients, respectively, died (P=NS). Overall, 15 (26%) C+HBI versus 21 (44%) UC patients experienced a primary endpoint. C+HBI was associated with a non-significant, 45% reduction in the risk of death or readmission when adjusting for potential confounders (RR 0.55, 95% CI 0.28-1.08: P=0.08). Overall, C+HBI patients accumulated significantly fewer unplanned readmissions (15 vs. 45: P

Reoperative parathyroid surgery: The importance of ectopic location and multigland disease

Background: Despite the availability of expert surgeons and preoperative imaging investigations, some patients require reoperation for persistent or recurrent hyperparathyroidisms. Method: Fifty consecutive patients were reviewed. Results: There were 28 persistent cases (24 primary, 4 secondary) and 22 recurrent cases (15 primary, 7 secondary) and 98% had successful surgical treatment. Multigland disease was present in 24 of 39 (62%) of primary cases, 11 of 24 persistent and 13 of 15 recurrent (P < 0.02). Four patients in the recurrent primary group had multiple endocrine neoplasia type 1, whereas the other 20 primary patients had sporadic multigland disease. Multigland disease was present in all secondary cases and was a very important factor in this entire series of patients (70%). Regrowth of a remnant of a gland biopsied or partially resected at an earlier operation was the cause of recurrence in 12 of 15 primary and 2 of 7 secondary cases (P < 0.05). The site of missed glands in persistent disease was ectopic in 60%. Ectopic glands were found in the following sites: intrathyroidal 10 (8 inferior and 2 superior), intrathymic 9, posterior mediastinum 4, base of skull 2, carotid sheath 1 and supernumerary 5. Investigations to locate missing glands were positive in 28 of 43 sestamibi scans (65%), 14 of 34 ultrasound scans (41%), 10 of 24 computed tomography scans (42%) and 11 of 13 selective venous sampling tests (85%). Conclusion: Some persistent cases are unavoidable because of ectopic locations and some recurrences are inevitable because of multigland disease.

A New Approach for Eliminating the Spurious States in Recurrent Neural Networks

As is well known, the Convergence Theorem for the Recurrent Neural Networks, is based in Lyapunov ́s second method, which states that associated to any one given net state, there always exist a real number, in other words an element of the one dimensional Euclidean Space R, in such a way that when the state of the net changes then its associated real number decreases. In this paper we will introduce the two dimensional Euclidean space R2, as the space associated to the net, and we will define a pair of real numbers ( x, y ) , associated to any one given state of the net. We will prove that when the net change its state, then the product x ⋅ y will decrease. All the states whose projection over the energy field are placed on the same hyperbolic surface, will be considered as points with the same energy level. On the other hand we will prove that if the states are classified attended to their distances to the zero vector, only one pattern in each one of the different classes may be at the same energy level. The retrieving procedure is analyzed trough the projection of the states on that plane. The geometrical properties of the synaptic matrix W may be used for classifying the n-dimensional state- vector space in n classes. A pattern to be recognized is seen as a point belonging to one of these classes, and depending on the class the pattern to be retrieved belongs, different weight parameters are used. The capacity of the net is improved and the spurious states are reduced. In order to clarify and corroborate the theoretical results, together with the formal theory, an application is presented.