A Pilot Study using the Compensatory Reserve Index to evaluate individuals with Postural Orthostatic Tachycardia syndrome

Purpose:The diagnosis of Postural Orthostatic Tachycardia syndrome traditionally involves orthostatic vitals evaluation. The Compensatory Reserve Index is a non-invasive, FDA-cleared algorithm that analyses photoplethysmogram waveforms in real time to trend subtle waveform features associated with varying degrees of central volume loss, from normovolemia to decompensation. We hypothesised that patients who met physiologic criteria for Postural Orthostatic Tachycardia syndrome would have greater changes in Compensatory Reserve Index with orthostatic vitals.Methods:Orthostatic vitals and Compensatory Reserve Index values were assessed in individuals previously diagnosed with Postural Orthostatic Tachycardia syndrome and healthy controls aged 12–21 years. Adolescents were grouped for comparison based on whether they met heart rate criteria for Postural Orthostatic Tachycardia syndrome (physiologic Postural Orthostatic Tachycardia syndrome).Results:Sixty-one patients were included. Eighteen percent of patients with an existing Postural Orthostatic Tachycardia syndrome diagnosis met heart rate criteria, and these patients had significantly greater supine to standing change in Compensatory Reserve Index (0.67 vs. 0.51; p<0.001). The optimal change in Compensatory Reserve Index for physiologic Postural Orthostatic Tachycardia syndrome was 0.60. Patients with physiologic Postural Orthostatic Tachycardia syndrome were more likely to report previous diagnoses of anxiety or depression (p = 0.054, 0.042).Conclusion:An accurate diagnosis of Postural Orthostatic Tachycardia syndrome may be confounded by related comorbidities. Only 18% (8/44) of previously diagnosed Postural Orthostatic Tachycardia syndrome patients met heart rate criteria. Findings support the utility of objective physiologic measures, such as the Compensatory Reserve Index, to more accurately identify patients with true autonomic dysfunction.

Modeling and Forecasting of Covid-19 Growth Curve in India

In this article, we analyze the growth pattern of Covid-19 pandemic in India from March 4th to May 15th using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt-Winters models. We found that the growth of Covid-19 cases follows a power regime of (t2, t,..) after the exponential growth. We found the optimal change points from where the Covid-19 cases shift their course of growth from exponential to quadratic and then from quadratic to linear. We have also found the best fitted regression models using the various criteria such as significant p-values, coefficients of determination and ANOVA etc. Further, we search the best fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and CAIC (Consistent Akaike Information Criterion) and provide the forecast of Covid-19 cases for future days. We also use usual exponential smoothing and Holt-Winters models for forecasting purpose. We further found that the ARIMA (2,2,0) model is the best-fitting model for Covid-19 cases in India.

A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits

We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT log T), where N is the number of piecewise-stationary segments, K is the number of base arms, and T is the number of time steps. As a complement, we also derive a nearly matching regret lower bound on the order of Ω(√NKT), for both piecewise-stationary multi-armed bandits and combinatorial semi-bandits, using information-theoretic techniques and judiciously constructed piecewise-stationary bandit instances. Our lower bound is tighter than the best available regret lower bound, which is Ω(√T). Numerical experiments on both synthetic and real-world datasets demonstrate the superiority of GLR-CUCB compared to other state-of-the-art algorithms.

Task Shifting, Midwifery Empowerment and the Nascence of Clinical Pharmacy

This chapter addresses the role that the intervention has played in shaping professional engagement within the multi-disciplinary team. The existence of laboratory results has triggered the emergence of clinical pharmacy roles. The chapter traces the impact of this on prescribing behaviour and on procurement planning and hospital policies. Whilst celebrating the progress made and viability of the model, it describes the structural impact that access to antibiotics and IPC supplies has on the realisation of optimal change.

Alternative Algorithm for Automatically Driving Best-Fit Building Energy Baseline Models Using a Data—Driven Grid Search

Change-point regression models are often used to develop building energy baselines that can be used to predict energy use and determine energy savings during a given performance period. However, the reliability of building energy baselines can depend on how well the change-point model fits the data measured during the baseline period. This research proposes the use of segmented linear regression models with one or two change points for automatically driving best-fit building energy baseline models, along with an algorithm using a data-driven grid search to find the optimal change point(s) within a given data boundary for the proposed models. The algorithm was programmed and tested with actual measured data (e.g., daily gas and electricity use) for case-study buildings. Graphical and statistical analysis was also performed to validate its reliability within acceptable deviations of an overall coefficient of variation of the root mean squared error (i.e., CV(RMSE)) of 1%, as compared to the results derived from the ASHRAE Inverse Model Toolkit (IMT) that was developed as a public domain program to manually derive the change-point model with user specified parameters. Consequently, it is expected that the algorithm can be applied for automatically deriving best-fit building energy baseline models with optimal change point(s) from measured data.