Efficacy and safety of antiarrhythmic therapy in dogs with naturally acquired tachyarrhythmias treated with amiodarone or sotalol: a retrospective analysis of 64 cases.

INTRODUCTION/OBJECTIVE: Studies on the use of amiodarone or sotalol are limited in dogs. Therefore, this study aimed to provide data on the efficacy and safety of these drugs in dogs with ventricular tachyarrhythmia (VT) and/or supraventricular tachyarrhythmia (SvT). ANIMALS, MATERIALS, AND METHODS: Dogs with VT and/or SvT treated with amiodarone or sotalol as a first-line therapy were retrospectively evaluated. Signalment, clinical, diagnostic, therapeutic, and outcome data were retrieved. For VT, efficacy was demonstrated through a decrease of the Lown-Wolf grade to less than five or a reduction of at least 85% in the number of ventricular premature complexes observed on Holter monitoring. For SvT, efficacy was represented by cardioversion or a reduction in the mean heart rate on Holter monitoring ≤140 beats/min. Treatment-related side effects (TRSEs) were classified as clinically relevant and irrelevant. Statistical analysis was performed to compare data before and after antiarrhythmic prescription. RESULTS: Sixty-four dogs were included. Amiodarone and sotalol were efficacious in treating both VT (85.7% and 90.0% of cases, respectively) and SvT (75% and 71.4% of cases, respectively). No significant differences were found when comparing their efficacy rates in dogs with VT and SvT (P=0.531 and 0.483, respectively). Clinically relevant TRSEs were rare with both amiodarone and sotalol (8.3% and 5% of cases, respectively), while clinically irrelevant TRSEs occurred more frequently with amiodarone (29.2%) than with sotalol (10%). DISCUSSION: In dogs with tachyarrhythmias, amiodarone and sotalol are generally efficacious and safe, as clinically relevant TRSEs seem rare. CONCLUSIONS: This study provides novel data on the effects of amiodarone and sotalol in dogs with tachyarrhythmias.

Learning, not adaptation, characterizes stroke motor recovery: evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace.

Both the American Heart Association and the VA/DoD endorse upper-extremity robot-mediated rehabilitation therapy for stroke care. However, we do not know yet how to optimize therapy for a particular patient's needs. Here, we explore whether we must train patients for each functional task that they must perform during their activities of daily living or alternatively capacitate patients to perform a class of tasks and have therapists assist them later in translating the observed gains into activities of daily living. The former implies that motor adaptation is a better model for motor recovery. The latter implies that motor learning (which allows for generalization) is a better model for motor recovery. We quantified trained and untrained movements performed by 158 recovering stroke patients via 13 metrics, including movement smoothness and submovements. Improvements were observed both in trained and untrained movements suggesting that generalization occurred. Our findings suggest that, as motor recovery progresses, an internal representation of the task is rebuilt by the brain in a process that better resembles motor learning than motor adaptation. Our findings highlight possible improvements for therapeutic algorithms design, suggesting sparse-activity-set training should suffice over exhaustive sets of task specific training.

Changing motor synergies in chronic stroke.

Synergies are thought to be the building blocks of vertebrate movements. The inability to execute synergies in properly timed and graded fashion precludes adequate functional motor performance. In humans with stroke, abnormal synergies are a sign of persistent neurological deficit and result in loss of independent joint control, which disrupts the kinematics of voluntary movements. This study aimed at characterizing training-related changes in synergies apparent from movement kinematics and, specifically, at assessing: 1) the extent to which they characterize recovery and 2) whether they follow a pattern of augmentation of existing abnormal synergies or, conversely, are characterized by a process of extinction of the abnormal synergies. We used a robotic therapy device to train and analyze paretic arm movements of 117 persons with chronic stroke. In a task for which they received no training, subjects were better able to draw circles by discharge. Comparison with performance at admission on kinematic robot-derived metrics showed that subjects were able to execute shoulder and elbow joint movements with significantly greater independence or, using the clinical description, with more isolated control. We argue that the changes we observed in the proposed metrics reflect changes in synergies. We show that they capture a significant portion of the recovery process, as measured by the clinical Fugl-Meyer scale. A process of "tuning" or augmentation of existing abnormal synergies, not extinction of the abnormal synergies, appears to underlie recovery.

Robot-aided neurorehabilitation: from evidence-based to science-based rehabilitation.

There is no "magic bullet" in rehabilitation. In the absence of direct neural transplants, neurological rehabilitation is an arduous process. We have pioneered the clinical application of robotics in stroke rehabilitation and have shown evidence of the positive impact of targeted exercise on stroke recovery. In this article, we will review results obtained in the initial clinical trials with 96 stroke patients at the Burke Rehabilitation Hospital. We will provide evidence that robot-aided training enhances recovery, that this enhanced recovery is sustained in the long term, and that this recovery is not due to a general physiological improvement--in fact, it appears to be limb and muscle group specific. An evidence-based approach must now segue into a more scientific approach to stroke rehabilitation. Given the length of the required protocols and patients' variability and limited census, the practical limitations of the evidence-based approach are self-evident and extend trials for years. Each patient and lesion is unique in stroke rehabilitation, so there is no reason to believe that a "one-size-fits-all" optimal treatment exists. To optimize therapy for individual patients, we need science-based models. In this article, we will summarize the scientific tools and models that we are investigating and present some of the results to date.