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Abstract: Prescription opioids for noncancer pain in the United Kingdom have increased over the past 2 decades, alongside associated harms. Policies addressing opioid prescribing must be tailored to individual patient needs with specific disease systems. The aim of this study was to evaluate clinical conditions associated with new opioid initiation in noncancer pain using nationally representative UK data. Primary care electronic health records from January 1, 2006, to September 31, 2021, were used from the Clinical Research Practice Datalink to identify incident opioid prescriptions. Patient histories were reviewed using code lists for opioid-related conditions with a 5-year look-back for chronic conditions and a 1-year look-back for surgical indications before opioid initiation. In total, 3,030,077 new opioid use episodes in 2,027,402 patients were identified, with 61% being women, 77% aged 45 years and older, and 48% from the highest deprivation quintile. Ten systems associated with opioid initiation were identified, which were not mutually exclusive, as patients could have opioids prescribed for multiple indications. The most common were musculoskeletal (80.8%), respiratory (57.6%), infections (30.4%), trauma/injury (20.4%), neurology (19.9%), and postsurgical indications (5.5%). Osteoarthritis (60.7%) and low back pain (41.0%) were the most frequent musculoskeletal conditions. Orthopedic surgeries accounted for 41.2% of all postsurgical indications. This is the first study in the United Kingdom evaluating large-scale national data to assess indications associated with opioid initiation. Nearly 3 quarters of new opioid prescriptions for noncancer pain were in patients with musculoskeletal conditions, often for conditions with limited evidence for opioid efficacy. These findings could inform targeted interventions and future policies to support nonpharmacological interventions in the most common conditions where opioid harms outweigh benefits., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain.)

Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions. Most studies (96%) originated from North America, with only 7% reporting external validation. Model performance was moderate to strong, but calibration was often missing (41%). Transparent reporting of model development was often incomplete, with key aspects such as calibration, imbalance correction, and handling of missing data absent. Infrequent external validation limited the generalizability of current models. Addressing these aspects is critical for transparency, interpretability, and future implementation of the results., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective: Our study aimed to assess the heart failure/cardiomyopathy-related population-level mortality trends among patients with obesity in the United States and disparities across demographics., Methods: We queried the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research database among adults aged ≥25 from 1999 to 2019. Heart failure/cardiomyopathy were listed as the main causes of death, with obesity as a contributing cause. We calculated age-adjusted mortality rates (AAMR) per 100,000 individuals and estimated the average annual percent change (AAPC). We also evaluated the social vulnerability of United States counties (2014-2018)., Results: There were 29,334 deaths related to heart failure/cardiomyopathy among patients with comorbid obesity. The overall AAMR increased from 0.41 in 1999 to 0.94 in 2019, with an AAPC of 3.78 (95 % CI, 3.41-4.14). The crude mortality rate increase for heart failure/cardiomyopathy was greater in individuals with comorbid obesity than in those without. Males had a higher AAMR than females (0.78 vs 0.55). African Americans also had higher AAMR than Whites (1.35 vs 0.62). The AAMR was higher in rural areas than in urban regions (0.76 vs 0.66). The overall AAMR was higher in counties with social vulnerability index-Quartile 4 (SVI-Q4) (most vulnerable) (1.08) compared to SVI-Q1 (least vulnerable) (0.63) with a risk ratio of 1.71 (95 % CI: 1.61-1.83)., Conclusion: Heart failure/cardiomyopathy mortality in individuals with comorbid obesity was rising. Males, African Americans, and individuals from rural regions had higher AAMR than their counterparts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)