Use of antipsychotics and benzodiazepines in patients with psychiatric emergencies: Results of an observational trial

<p>Abstract</p> <p>Background</p> <p>Conventional antipsychotics augmented with benzodiazepines have been the standard acute treatment for psychiatric emergencies for more than 50 years. The inability of patients to give informed consent limits randomised, controlled studies. This observational study on immediate therapy for aggression and impulse control in acutely agitated patients (IMPULSE) evaluated the short-term effectiveness and tolerability of atypical and typical antipsychotic medications (AP) in a non-interventional setting.</p> <p>Methods</p> <p>This was a comparative, non-randomised, prospective, open-label, observational study. Treatment over the first 5 days was classified according to whether any olanzapine, risperidone, or haloperidol was included or not. Documentations (PANSS-excited component, CGI-aggression, CGI-suicidality, tranquilisation score) were at baseline (day 1) and days 2–6 after start of AP.</p> <p>Results</p> <p>During the short treatment-period, PANSS-EC and CGI-aggression scores improved in all cohorts. 68.7% of patients treated with olanzapine, 72.2% of patients treated with risperidone, and 83.3% of patients treated with haloperidol received concomitant benzodiazepines (haloperidol vs. non-haloperidol: p < 0.001). More patients treated with olanzapine (73.8%) were fully alert according to a tranquilisation score and active at day 2 than patients treated with risperidone (57.1%) or haloperidol (58.0%).</p> <p>Conclusion</p> <p>Current medication practices for immediate aggression control are effective with positive results present within a few days. In this study, concomitant benzodiazepine use was significantly more frequent in patients receiving haloperidol.</p

Neuro-Genetic Adaptive Attitude Control

It has previously been demonstrated that for smooth dynamic systems, using relatively few sample points from a single trajectory, a neural network can be trained to perform very accurate short-term prediction over a large part of the phase space. In this paper, we exploit the capability of a Locally Predictive Network (LPN) to derive an adaptive control architecture for a satellite equipped with controllable, bidirectional thrusters on each of the three principal axes. It is assumed that a hardware implementation of the neural network is available. The inputs for the network are a small history of system states up to the present time and a set of current control inputs, the outputs are the next system state. Once the LPN has been trained successfully, at each time step a genetic algorithm searches the space of hypothetical control inputs. Given a set of control signals, the LPN is used to predict the state of the system at the next sample point. This enables the ‘fitness’ of each set of hypothetical control torques to be evaluated very rapidly. In effect, the genetic algorithm determines a satisfactory solution to the inverse kinematic problem in time to apply the solution (set of control torques) at the next control point. With the exception of the neuromodelling (which is repeated only when the system dynamics change), the whole process is then repeated. The results presented indicate that such an architecture is easily able to master the attitude control problem for arbitrary slew angles, with arbitrary a priori unknowndynamics and noise in the sensor system