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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Deep learning approaches for electrical vehicular mobility management: invited paper

International audienceElectrical vehicular (EV) energy management is a promising trend. Forecasting vehicular trajectories and delay is crucial for EV energy management. The presented work is devoted to the study and the application of deep learning techniques on specific road trajectories. First, exhaustive deep learning algorithms are considered. Second, road traces are converted to time series. Then, delays and road trajectories are analyzed. In fact, we consider two Recurrent Neural Networks (RNN): LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). Neural Networks are adapted and trained on 60 days of real urban traffic of Rome in Italy. We calculate the Loss function for both machine learning techniques which is defined by mean square error (MSE) and Root mean square error (RMSE). Experimental results demonstrate that both LSTM and GRU are adequate for the context of EV in terms of route trajectory and delay prediction

AngioVac System Used for Vegetation Debulking in a Patient with Tricuspid Valve Endocarditis: A Case Report and Review of the Literature

AngioVac is a vacuum-based device approved in 2014 for percutaneous removal of undesirable materials from the intravascular system. Although numerous reports exist with regard to the use of the AngioVac device in aspiration of iliocaval, pulmonary, upper extremity, and right-sided heart chamber thrombi, very few data are present demonstrating its use in treatment of right-sided endocarditis. In this case report, we describe the novel device used in debulking a large right-sided tricuspid valve vegetation reducing the occurrence of septic embolisation and enhancing the efficacy of antibiotics in clearance of bloodstream infection. Further research is needed in larger RSIE patient populations to confirm the benefits and the potential of improved outcomes associated with the AngioVac device as well as identify its potential complications

Thrombus in the Right Coronary Sinus of Valsalva Originating From the Left Atrial Appendage Causing Embolic Inferior Wall Myocardial Infarction

Acute myocardial infarction (MI) is commonly a result of coronary atherosclerotic plaque rupture and superimposed thrombus formation. Nevertheless, uncommon causes of MI including embolism from aortic root and ascending aorta mural thrombi must be considered when coronary atherosclerotic disease is not evident. We report a case of a 84-year-old woman who presented with an inferior ST-segment elevation MI. Initial attempts to engage the right coronary artery (RCA) were unsuccessful. Aortic angiography revealed evidence of the left coronary artery ostium with absence of the right coronary ostium or RCA. Probing with a coronary wire where the RCA ostium was presumed to be located yielded resolution of the ST-segment elevation. The RCA was then easily engaged using a guide catheter, and angiographic evaluation showed a smooth vessel with no evidence of coronary artery disease except for abrupt termination of the distal PL2 branch. Contrast-enhanced computed tomography revealed an aortic root thrombus extending into the right coronary sinus of Valsalva and a thrombus in the left atrial appendage. The case reveals RCA embolism from an aortic root thrombus likely originating from the left trial appendage. A conservative approach to treatment with anticoagulation was pursued that resulted in full recovery. A review of the literature revealed that the etiology of aortic root thrombi is proposed to be multifactorial. Prospective randomized studies are needed to demonstrate the best treatment approach, although this appears to be impracticable given the rarity of the disease

Fault Location in Distribution Network by Solving the Optimization Problem Based on Power System Status Estimation Using the PMU

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s

A High Speed MPPT Control Utilizing a Hybrid PSO-PID Controller under Partially Shaded Photovoltaic Battery Chargers

Improving photovoltaic systems in terms of temporal responsiveness, lowering steady-state ripples, high efficiency, low complexity, and decreased tracking time under various circumstances is becoming increasingly important. A particle-swarm optimizer (PSO) is frequently used for maximum power-point tracking (MPPT) of photovoltaic (PV) energy systems. However, during partial-shadowing circumstances (PSCs), this technique has three major drawbacks. The first problem is that it slowly converges toward the maximum power point (MPP). The second issue is that the PSO is a time-invariant optimizer; therefore, when there is a time-variable shadow pattern (SP), it adheres to the first global peak instead of following the dynamic global peak (GP). The third problem is the high oscillation around the steady state. Therefore, this article proposes a hybrid PSO-PID algorithm for solving the PSO’s three challenges described above and improving the PV system’s performance under uniform irradiance and PSCs. The PID is designed to work with the PSO algorithm to observe the maximum voltage that is calculated by subtracting from the output voltage of the DC-DC boost converter and sending the variation to a PID controller, which reduces the error percentage obtained by conventional PSO and increases system efficiency by providing the precise converter-duty cycle value. The proposed hybrid PSO-PID approach is compared with a conventional PSO and bat algorithms (BAs) to show its superiority, which has the highest tracking efficiency (99.97%), the lowest power ripples (5.9 W), and the fastest response time (0.002 s). The three aforementioned issues can be successfully solved using the hybrid PSO-PID technique; it also offers good performance with shorter times and faster convergence to the dynamic GP. The results show that the developed PID is useful in enhancing the conventional PSO algorithm and solar-system performance

Impact and Outcomes of Patients with Congestive Heart Failure Complicating Non-ST-Segment Elevation Myocardial Infarction,Results from a Nationally-Representative United States Cohort

Introduction: Congestive heart failure (CHF) is seen in up to 13–25% of patients with NSTEMI. Recent data describing the impact of congestive heart failure (CHF) on in-hospital outcomes in patients with non-ST-segment elevation myocardial infarction (NSTEMI) in the United States is limited. We sought to examine the in-hospital outcomes, and management of CHF in patients admitted to the hospital with NSTEMI. Methods: National Inpatient Sample (NIS) database (2010–2014) was analyzed to identify patients with NSTEMI using ICD-9-CM codes. The primary outcome was in-hospital mortality. Propensity score-matching analysis compared mortality in CHF patients to matched controls without CHF. Results: Of 247,624 patients with NSTEMI, 84,115 (34%) had CHF. Patients with CHF were less likely to receive percutaneous coronary intervention (PCI) [20.48% vs. 40.9%, P \u3c 0.001] or coronary artery bypass grafting (CABG) [8.2% vs 9.6%, P \u3c 0.001] during hospitalization. Also, they had longer lengths of stay and higher risk for in-hospital adverse outcomes. CHF was the strongest predictor of in-hospital death. The increased mortality risk was persistent after propensity matching (RR 1.27; 95% CI 1.22 to 1.33). Conclusion: CHF among patients with NSTEMI is associated with increased risk for in-hospital mortality and adverse outcomes