Management of hyperglycemia in hospitalized patients has a significant bearing on outcome, in terms of both morbidity and mortality. However, there are few national assessments of diabetes care during hospitalization which could serve as a baseline for change. This analysis of a large clinical database (74 million unique encounters corresponding to 17 million unique patients) was undertaken to provide such an assessment and to find future directions which might lead to improvements in patient safety. Almost 70,000 inpatient diabetes encounters were identified with sufficient detail for analysis. Multivariable logistic regression was used to fit the relationship between the measurement of HbA1c and early readmission while controlling for covariates such as demographics, severity and type of the disease, and type of admission. Results show that the measurement of HbA1c was performed infrequently (18.4%) in the inpatient setting. The statistical model suggests that the relationship between the probability of readmission and the HbA1c measurement depends on the primary diagnosis. The data suggest further that the greater attention to diabetes reflected in HbA1c determination may improve patient outcomes and lower cost of inpatient care.
The paper introduces a multi-layer multi-column model of the cortex that uses four different neuron types and short-term plasticity dynamics. It was designed with details of neuronal connectivity available in the literature and meets these conditions: (1) biologically accurate laminar and columnar flows of activity, (2) normal function of low-threshold spiking and fast spiking neurons, and (3) ability to generate different stages of epileptiform activity. With these characteristics the model allows for modeling lesioned or malformed cortex, i.e. examine properties of developmentally malformed cortex in which the balance between inhibitory neuron subtypes is disturbed.
Izhikevich model of a neuron allows for simulation of spiking pattern that mimics known biological subtypes. When a current within a range typical for biological experiments is injected into the cell the firing pattern produced in the simulation is close to that observed biologically. However, once these neurons are embedded into a network, the level of depolarization is controlled only by the synaptic depolarization received by the simulated connections. Under these conditions there is no limit on the maximum firing rate produced by any of the neurons. Here we introduce a modification of the Izhikevich model to restrict the firing rate. We demonstrate how this modification affects the overall network activity using a simple artificial neural network. The proposed restraint on the Izhikevich model is particularly important for larger scale simulations or when the frequency dependent short-term plasticity is used in the network. Although maximum firing rates are most likely exceeded in simulations of seizure-like activity we show that restriction of neuronal firing frequencies impacts even small networks with moderate levels of activity.
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