Exposure to prescription opioids following traumatic injury can increase the risk of developing tolerance, persistent opioid use and opioid use disorder. The mechanisms underlying opioid tolerance or dependence are not well understood, and no biomarkers predict risk. Opioid exposure causes epigenetic modifications, including alterations in microRNA (miRNA) expression. Several miRNAs, which regulate synaptic plasticity, are hypothesised to underlie substance use disorders and influence µ-opioid receptor levels, modulating opioid tolerance. This project aims to develop a bio-behavioural signature to predict persistent opioid use and chronic pain up to 6 months post-discharge.

The study will use a prospective cohort design, enrolling 180 adult patients at a Level I Trauma Center who are prescribed opioids at discharge. Prospective data will be collected in the hospital and at 7 days and 1, 3 and 6 months post-discharge. Biological data (genotyping and miRNA levels) and clinical measures of opioid use, pain, pain sensitivity (EEG) and psychosocial functioning will be collected at each time point. Bayesian regression methods will be used to identify baseline clinical, genetic, epigenetic and psychosocial predictors of opioid use and pain outcomes at 6 months post-discharge. Growth mixture modelling will identify distinct subgroups with varying trajectories, followed by Bayesian hierarchical modelling to predict trajectory classification based on predictor variables.

Ethics approval for this study was obtained from the University of Texas Health Science Center at Houston Committee for the Protection of Human Subjects (HSC-MS-24–0314). Findings will be disseminated in peer-reviewed scientific journals and at national and international conferences.

Specialised outpatient palliative care (SOPC) is an important element of the palliative care concept in Germany. The aim of this study is to compare patient characteristics, care processes and outcomes of patients with heart failure (HF) and oncological diseases, using the latter as a reference group to identify disease-specific needs and support the adaptation of SOPC to non-oncological conditions such as HF.

In this cross-sectional study (22 SOPC providers), clinical data of all palliative care patients who were treated between 2017 and 2021 were retrospectively analysed.

Survival was estimated by Kaplan-Meier analysis. To further examine the relationship between patient survival time and various variables, a Cox proportional hazards model was used. Differences in symptom burden were tested for statistical significance using the McNemar test.

Data from 48 882 patients were analysed, with 5387 (11.0%) identified as having a primary HF diagnosis. This cohort was compared against a large oncological group consisting of 34 287 (70.1%) patients.

For HF patients, the mean number of days spent in SOPC was 30.5±67.7 days and for oncological patients 44.1±72.0 days. A significantly higher proportion of oncological patients died in hospices (14.0%) and hospitals (6.9%) compared with HF (2.9% and 2.2%). Age-adjusted Charlson Comorbidity Index at admission into SOPC was 9.4±3.1 in oncological patients compared with 6.7±1.7 in HF (p

HF patients in SOPC exhibit a different clinical profile compared with oncological patients, characterised by significant symptom burden and shorter survival times. These results emphasise the necessity for tailored palliative interventions to address the specific needs of HF patients.