Postoperative Monitoring Following Adenotonsillectomy for Severe Obstructive Sleep Apnea.

INTRODUCTION: Patients undergoing adenotonsillectomy (T&A) for severe obstructive sleep apnea (OSA) are usually admitted for observation, and many surgeons use the intensive care unit (ICU) for observation due to the risk of postsurgical airway obstruction. Given the limited resources of the pediatric ICU (PICU), there is a push to better define the patients who require postoperative monitoring in the PICU for monitoring severe OSA. METHODS: Forty-five patients were evaluated. Patients who had cardiac or craniofacial comorbidities were excluded. Patients undergoing T&A for severe OSA were monitored in the postanesthesia care unit (PACU) postoperatively. If patients required supplemental oxygen or developed hypoxia while in the PACU within the 3-hour monitoring period, they were admitted to the PICU. RESULTS: Overall, 16 of 45 patients were admitted to the ICU for monitoring. Patients with an Apnea-Hypopnea Index (AHI) >50 or with an oxygen nadir <80% were significantly more likely to be admitted to the PICU. The mean AHI of patients admitted to the PICU was 40.5, and the mean oxygen nadir was 69.9%. Patients younger than 2 years were significantly more likely to be admitted to the PICU. CONCLUSION: Based on the data presented here and academy recommendations, not all patients with severe OSA require ICU monitoring.

Vaccination Control in a Stochastic SVIR Epidemic Model.

For a stochastic differential equation SVIR epidemic model with vaccination, we prove almost sure exponential stability of the disease-free equilibrium for ℛ(0) < 1, where ℛ(0) denotes the basic reproduction number of the underlying deterministic model. We study an optimal control problem for the stochastic model as well as for the underlying deterministic model. In order to solve the stochastic problem numerically, we use an approximation based on the solution of the deterministic model.

Interactive MRI Segmentation with Controlled Active Vision.

Partitioning Magnetic-Resonance-Imaging (MRI) data into salient anatomic structures is a problem in medical imaging that has continued to elude fully automated solutions. Implicit functions are a common way to model the boundaries between structures and are amenable to control-theoretic methods. In this paper, the goal of enabling a human to obtain accurate segmentations in a short amount of time and with little effort is transformed into a control synthesis problem. Perturbing the state and dynamics of an implicit function's driving partial differential equation via the accumulated user inputs and an observer-like system leads to desirable closed-loop behavior. Using a Lyapunov control design, a balance is established between the influence of a data-driven gradient flow and the human's input over time. Automatic segmentation is thus smoothly coupled with interactivity. An application of the mathematical methods to orthopedic segmentation is shown, demonstrating the expected transient and steady state behavior of the implicit segmentation function and auxiliary observer.