Citations

Importance High-quality evidence guiding opioid prescribing decisions for acute pain across common diagnoses is lacking. Objective To describe pain trajectories and patterns of opioid and nonopioid treatment use among individuals who were offered opioids for the treatment of acute pain. Design, Setting, and Participants This was a prospective cohort study of patients recruited from 5 US health systems between September 2020 and March 2023 in emergency departments (EDs), primary care clinics, dental practices, or after cesarean delivery or knee replacement. Eligible patients were opioid-naive adults aged 18 years or older at all study sites, as well as adolescents aged 15 to17 years undergoing impacted molar extraction at 1 site, who were offered an opioid prescription for acute pain. Analysis was conducted April 2023 through February 2026. Exposure Offer of a prescription for an opioid analgesic. Main Outcomes and Measures Time to pain resolution (3 consecutive reports of no pain), patterns of opioid and nonopioid treatment use, and opioid-related adverse effects, ascertained from digital questionnaires. Results Among 1708 enrolled patients (median age, 38 years [IQR, 28-52 years]; 615 [36.0%] reporting race or ethnicity underrepresented in studies of acute pain management) followed up for 180 days, 915 (53.6%) were recruited in EDs, 307 (18.0%) in primary or urgent care, 263 (15.4%) in dental settings, and 223 (13.1%) in inpatient settings. Pain sources included dental (302 patients [17.7%]), trauma or injury (302 [17.7%]), obstetric (176 [10.3%]), musculoskeletal (131 [7.7%]), and low back (100 [5.9%]). Among 1502 patients reporting pain level at least once, median time to pain resolution irrespective of the treatment approach was 20 days (IQR, 8-88 days), with longer durations for surgical pain (74 days [IQR, 30 days to not reached]) and low back pain (69 days [IQR, 18 days to not reached]). The median time to opioid discontinuation among 1189 patients (69.6%) who reported any opioid use was 7 days (IQR, 2-31 days); an estimated 10.0% (95% CI, 7.7%-12.7%) of patients used opioids for at least 90 days, with higher rates in people reporting frequent pain before enrollment. Of 1482 patients (86.8%) completing at least 1 survey during the first 2 weeks of follow-up, 1153 (77.8%) reported using any opioids and 1287 (86.8%) reported using acetaminophen or ibuprofen. Among 619 (52.1%) patients with any opioid use who reported the dose of opioids taken in the first 15 days, daily doses were low (median, 10 [IQR, 5-15] morphine milligram equivalents). Most respondents reported leftover opioids (657 of 982 responding [66.9%]). Conclusions and Relevance In this cohort study of opioid-naive patients with acute pain, opioid use was generally low dose and of short duration, although some patients reported prolonged opioid use; most reported achieving pain resolution within 3 weeks, with longer times for surgical and low back pain. The findings suggest current guidelines for multimodal treatment and for short-duration opioid prescriptions if needed will serve many but not all patients, and treatment should be tailored to address individual patients’ needs.

Abstract Objective To determine the effect of a user centered clinical decision support tool versus usual care on rates of initiation of buprenorphine in the routine emergency care of individuals with opioid use disorder. Design Pragmatic cluster randomized controlled trial (EMBED). Setting 18 emergency department clusters across five healthcare systems in five states representing the north east, south east, and western regions of the US, ranging from community hospitals to tertiary care centers, using either the Epic or Cerner electronic health record platform. Participants 599 attending emergency physicians caring for 5047 adult patients presenting with opioid use disorder. Intervention A user centered, physician facing clinical decision support system seamlessly integrated into user workflows in the electronic health record to support initiating buprenorphine in the emergency department by helping clinicians to diagnose opioid use disorder, assess the severity of withdrawal, motivate patients to accept treatment, and complete electronic health record tasks by automating clinical and after visit documentation, order entry, prescribing, and referral. Main outcome measures Rate of initiation of buprenorphine (administration or prescription of buprenorphine) in the emergency department among patients with opioid use disorder. Secondary implementation outcomes were measured with the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework. Results 1 413 693 visits to the emergency department (775 873 in the intervention arm and 637 820 in the usual care arm) from November 2019 to May 2021 were assessed for eligibility, resulting in 5047 patients with opioid use disorder (2787 intervention arm, 2260 usual care arm) under the care of 599 attending physicians (340 intervention arm, 259 usual care arm) for analysis. Buprenorphine was initiated in 347 (12.5%) patients in the intervention arm and in 271 (12.0%) patients in the usual care arm (adjusted generalized estimating equations odds ratio 1.22, 95% confidence interval 0.61 to 2.43, P=0.58). Buprenorphine was initiated at least once by 151 (44.4%) physicians in the intervention arm and by 88 (34.0%) in the usual care arm (1.83, 1.16 to 2.89, P=0.01). Conclusions User centered clinical decision support did not increase patient level rates of initiating buprenorphine in the emergency department. Although streamlining and automating electronic health record workflows can potentially increase adoption of complex, unfamiliar evidence based practices, more interventions are needed to look at other barriers to the treatment of addiction and increase the rate of initiating buprenorphine in the emergency department in patients with opioid use disorder. Trial registration ClinicalTrials.gov NCT03658642 .

Abstract Background: Treatment options for women with ovarian cancer remain very limited and acquired resistance to current therapies is very common. Altered DNA repair capability in epithelial ovarian cancer (EOC) may underlie response to both standard therapy and novel treatments, such as PARP inhibitors. Molecular sub-classification of high-grade serous ovarian cancer (HG-SOC) may uncover potential drug targets and possible mechanisms of drug resistance. Understanding the contribution of DNA repair and other driver mutations to drug response and resistance requires the development of molecularly annotated preclinical models reflective of the clinic. Methods: A patient derived xenograft (PDX) cohort has been generated from consecutive, chemotherapy-naïve human HG-SOC and stratified according to in vivo response to standard chemotherapy, DNA repair capability and molecular characteristics, including next generation sequencing by Foundation Medicine. Resistance to therapy is driven by re-treating relapsed PDX in vivo providing invaluable “paired samples” (pre and post drug treatment), which are difficult to obtain from patients, to allow clonal evolution analysis of mechanisms of drug response and resistance. Results: The xenograft success rate was 83%. Of ten HG-SOC PDX, all exhibited mutations in TP53, five in BRCA1/2 (two of which were germline) and two were methylated for BRCA1. In vivo cisplatin response, determined as platinum sensitive (progression-free interval (PFI) ≥100 d, n=4), platinum resistant (PFI <100 d, n=3) or platinum refractory (n=3), was largely consistent with patient outcome. Three of four platinum sensitive HG-SOC PDX contained DNA repair gene mutations, and the fourth was methylated for BRCA1. In contrast, all three platinum refractory PDX overexpressed dominant oncogenes (CCNE1, LIN28B and/or BCL2). Molecular analysis of this cohort has revealed actionable targets for novel therapeutic strategies. In vivo studies, including with PARP inhibitors, are underway. Conclusion: PDX with histologic, molecular and therapeutic annotation, as well as clinical outcome data allow interrogation of molecular aberrations and drug resistance in vivo. This will inform targeting of novel therapies and the design of clinical trials for women. Citation Format: Monique D. Topp, Lynne Hartley, Michele Cook, Valerie Heong, Emma Boehm, Lauren McShane, Jan Pyman, Orla McNally, Sumi Ananda, Maria I. Harell, Dariush Etemadmoghadam, Laura Galletta, Kathryn Alsop, Gillian Mitchell, Stephen B. Fox, Jeff B. Kerr, Karla J. Hutt, Scott H. Kaufmann, Australian Ovarian Cancer Study (AOCS), Elizabeth M. Swisher, David D. Bowtell, Matthew M. Wakefield, Clare L. Scott. Using molecularly characterized patient-derived models to delineate underlying drivers and vulnerabilities of epithelial ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Susceptibility and Cancer Susceptibility Syndromes; Jan 29-Feb 1, 2014; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(23 Suppl):Abstract nr 38. doi:10.1158/1538-7445.CANSUSC14-38