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

Insider Threat Detection Using Machine Learning Approach

Insider threats pose a critical challenge for securing computer networks and systems. They are malicious activities by authorised users that can cause extensive damage, such as intellectual property theft, sabotage, sensitive data exposure, and web application attacks. Organisations are tasked with the duty of keeping their layers of network safe and preventing intrusions at any level. Recent advances in modern machine learning algorithms, such as deep learning and ensemble models, facilitate solving many challenging problems by learning latent patterns and modelling data. We used the Deep Feature Synthesis algorithm to derive behavioural features based on historical data. We generated 69,738 features for each user, then used PCA as a dimensionality reduction method and utilised advanced machine learning algorithms, both anomaly detection and classification models, to detect insider threats, achieving an accuracy of 91% for the anomaly detection model. The experimentation utilised a publicly available insider threat dataset called the CERT insider threats dataset. We tested the effect of the SMOTE balancing technique to reduce the effect of the imbalanced dataset, and the results show that it increases recall and accuracy at the expense of precision. The feature extraction process and the SVM model yield outstanding results among all other ML models, achieving an accuracy of 100% for the classification model