Minimal Effective Dose of Ropivacaine Under Spinal Anesthesia for Cesarean Section After Failed Vaginal Delivery by Epidural Labor Analgesia

Background: The studies associated with EC50 of ropivacaine for spinal anesthesia are few worldwide during failed vaginal trial by epidural labor analgesia transfer to cesarean section. We preliminarily explore it to determine the minimum local analgesic dose (MLAD) of ropivacaine for spinal anesthesia during failed vaginal trial by epidural labor analgesia transfer to cesarean section(CS) and survey its adverse effect. Trial design: a sequential experimentMethods: The analgesia quality was defined as effective if VAS ( Visual Analogue Scale) score was less than 3 from 15 min after spinal anesthesia to the end of CS. The Brownlee up-and-down sequential allocation was used to estimate the MLAD of subarachnoid ropivacaine and its 95% confidence intervals during failed vaginal trial by epidural labor analgesia transfer to CS. Results: There were significant changes for the time to reach maximum sensory block, the time to reach maximum motor block and the duration from spinal anesthesia to starting operation and hypotension occurence (p＜0.05, p＜0.0001, respectively) between the effective group and ineffective group. Bradyarrhythmia, nausea, vomiting and chills were no significant changes between these two groups. The EC50 dose of f subarachnoid ropivacaine for failed vaginal trial conversion to CS by epidural labor analgesia was 8.2985 mg , and 95% CI( Confidence Interval) was 8.07947mg~8.52348mg.Conclusion: The MLAD of ropivacaine was 8.2985 mg ( 95% CI: 8.0795mg~ 8.5235mg) for spinal anesthesia for failed vaginal trial by epidural labor analgesia conversion to CS. It was indicated that 8.2985 mg ropivacaine by subarachnoid block for failed vaginal trial transfer to CS can provide satisfactory and safe analgesia to parturients with low incidence rate of side effects.Clinical Trial Registration: This clinical study has been registered at www.chictr.org.cn(ChiCTR1900027527).

A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements

Use of probabilistic techniques has been demonstrated to learn air data parameters from surface pressure measurements. Integration of numerical models with wind tunnel data and sequential experiment design of wind tunnel runs has been demonstrated in the calibration of a flush air data sensing anemometer system. Development and implementation of a metamodeling method, Sequential Function Approximation (SFA), are presented which lies at the core of the discussed probabilistic framework. SFA is presented as a tool capable of nonlinear statistical inference, uncertainty reduction by fusion of data with physical models of variable fidelity, and sequential experiment design. This work presents the development and application of these tools in the calibration of FADS for a Runway Assisted Landing Site (RALS) control tower. However, the multidisciplinary nature of this work is general in nature and is potentially applicable to a variety of mechanical and aerospace engineering problems.