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

Adaptive image processing using computational intelligence techniques

In this thesis, we illustrate the essential aspects of the adaptive image processing problem in terms of two applications: the adaptive assignment of the regularization parameters in image restoration, and the adaptive characterization of edges in feature detection applications. These two problems are representative of the general adaptive image processing paradigm in that the three requirements for its successive implementation: namely the segmentation of an image into its main feature types, the characterization of each of these features, and the optimization of the image model parameters corresponding to the individual features, are present. In view of these requirements, we have adopted the three main approaches within the class of computational intelligence algorithms, namely neu— ral network techniques, fuzzy set theory, and evolutionary computation, for solving the adaptive image processing problem. This is in view of the direct correspondence between some of the above requirements with the particular capabilities of specific computational intelligence approaches. We first applied neural network techniques to the adaptive regularization problem in image restoration. Instead of the usual approach of selecting the regularization parameter values by trial and error, we adopt a learning approach by treating the parameters in various local image regions as network weights of a model—based neural network with hierarchical architecture (HMBNN), such that they are adjustable through the supply of training examples specifying the desired image quality. In addition, we also applied the HMBNN to the proble