We designed a randomized, double-blinded study to determine the efficacy and safety of 0.5 mg/kg intravenous ephedrine for the prevention of hypotension during spinal anesthesia for cesarean delivery. Patients were randomly allocated into two groups: ephedrine group (n=21) and control group (n=21). Intravenous preload of 15 mL/kg lactated Ringer's solution was given. Shortly after the spinal injection, ephedrine 0.5 mg/kg or saline was injected intravenous for 60 sec. The mean of highest and lowest heart rate in the ephedrine group was higher than those of control group (P<0.05). There were significant lower incidences of hypotension and nausea and vomiting in the ephedrine group compared with the control group (8 [38.1%] vs. 18 [85.7%]); (4 [19%] vs. 12 [57.1%], respectively) (P<0.05). The first rescue ephedrine time in the ephedrine group was significantly longer (14.9±7.1 min vs. 7.9±5.4 min) than that of the control group (P<0.05). Neonatal outcome were similar between the study groups. These findings suggest, the prophylactic bolus dose of 0.5 mg/kg intravenous ephedrine given at the time of intrathecal block after a crystalloid fluid preload, plus rescue boluses reduce the incidence of hypotension.

The Valsalva maneuver yields similar results to the EMLA(®) in terms of pain reduction during venipuncture.

We study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a specified false alarm rate while maximizing the detection power. Regardless of whether the source is stationary or nonstationary, the proposed algorithm sequentially receives a time series and learns the nominal attributes-in the online setting-under possibly varying Markov statistics. Then, an anomaly is declared at a time instance, if the observations are statistically sufficiently deviant. Moreover, the proposed algorithm is remarkably versatile since it does not require parameter tuning to match the desired rates even in the case of strong nonstationarity. The presented study is the first to provide the online implementation of Neyman-Pearson (NP) characterization for the problem such that the NP optimality, i.e., maximum detection power at a specified false alarm rate, is nearly achieved in a truly online manner. In this regard, the proposed algorithm is highly novel and appropriate especially for the applications requiring sequential data processing at large scales/high rates due to its parameter-tuning free computational efficient design with the practical NP constraints under stationary or non-stationary source statistics.

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