Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Asymptotic output tracked artificial immunity controller for eco-maximum power point tracking of wind turbine driven by doubly fed induction generator

This paper aims to design a controller for a Doubly Fed Induction Generator (DFIG) targeting the Eco-Maximum Power Point Tracking (EMPPT) for environmental aspects. The proposed controller consists of two clusters, which are the novel Artificial Immunity sensorless Eco-Maximum Power Point Tracking (AI EMPPT) and the asymptotic non-linear control techniques. The main target of the AI EMPPT is to reduce the carbon dioxide emission by generating the maximum possible power from the renewable electrical energy resource, which is wind electrical power generation to replace the fossil-fuel conventional generation. To build the AI EMPPT, an Artificial Immunity System Estimator (AISE) based on artificial immunity technique and a Model Reference Adaptive System (MRAS) are used to estimate the DFIG rotor speed. Then, the AI EMPPT is applied to provide the reference electromagnetic torque signal. Subsequently, the reference electromagnetic torque interacts with the estimated generator speed, determined by the wind mechanical power, to supply the wind electrical power. The second cluster is the asymptotic non-linear control technique which proposes the reference signal tracking of the rotor direct and quadratic current, respectively. Thus, assigning specific zeros through feedback ensures the reproduction of an output that converges asymptotically to a required reference rotor current. For online operation, the Artificial Immunity Technique (AIT) is utilized to deal with the generated control reference signal. A proposal hardware implementation on Field Programmed Gate Array (FPGA) is also presented. The introduced approach was applied to a wind turbine generator driving a 3.7 kW load. MATLAB program was used to simulate and test the performance of the proposed control methods. The results to show the effectiveness of the proposed technique. The reduction in CO2 emission was calculated

Immunity technique in determine micro grid studies due to fault occurrence

In this paper, Artificial Immunity System (AIS) is used to study transient fault occurrence. The concept of micro grids is used to protect system against transient fault through AIS technique. Transient fault occurrence is analyzed and studied using the concept of wide area measurement protection and control (WAMPAC). WAMPAC gives the opportunity of having a wide information system and sending selected local information to remote locations. The existence of phasor measurement unit (PMU) found to be addressed all problems that have surfed in the most major blackout, as it overcome problem of real time monitoring data. It help to put a defiance strategy which is designed to answers the following inquiries: a-detecting abnormal condition, b-taking Special Protection Schemes (SPS) action, c-initiating SPS action, and d-taking SPS action in certain place. The proposed technique is based on a technology called Artificial Immunity system (AIS). It is used as a predictor to decide if the system is stable or not, and determines the main generating groups which can construct proper operating islands. The proposed system is applied on New England IEEE 39-Bus system. The proposed defensive approach shows good results in mitigating transient instabilities power system. © 2013 American Scientific Publishers All rights reserved

Micro grid studies due to fault occurrence using immunity technique

In this paper, micro grids resulted due to transient fault occurrence are studied utilizing Artificial Immunity System (AIS). Transient fault detection is analyzed, studied and protected using the concept of wide area measuring, protection and control (WAMPAC). WAMPAC gives the opportunity of having a wide information system and sending selected local information to remote locations. The existence of Phasor Measurement Unit (PMU) overcomes the problem of real time monitoring problem and it help to put a defiance strategy which is designed to answers the following inquiries: a-detecting abnormal condition, b-taking Special Protection Schemes (SPS) action, c-initiating SPS action, and d-taking SPS action in certain place. The technique is based on a technology called Artificial Immunity system (AIS). It is used as a predictor to decide if the system is stable or not, and determines the main generating groups which can construct proper operating islands. The proposed system is applied on New England IEEE 39 - Bus system. The proposed defensive approach shows accepted results in mitigating the power system transient instabilities