Prescribing long-term opioid therapy is a nuanced clinical decision requiring careful consideration of risks versus benefits. Our goal is to understand patient, provider and context factors that impact the decision to prescribe opioids in patients with cancer.

We conducted a secondary analysis of the raw semistructured interview data gathered from 42 prescribers who participated in one of two aligned concurrent qualitative studies in the USA and Australia. We conducted a two-part analysis of the interview: first identifying all factors influencing long-term prescribing and second open coding-related content for themes.

Factors that influence long-term opioid prescribing for cancer-related pain clustered under three key domains (patient-related, provider-related and practice-related factors) each with several themes. Domain 1: Patient factors related to provider–patient continuity, patient personality, the patient’s social context and patient characteristics including racial/ethnic identity, housing and socioeconomic status. Domain 2: Provider-related factors centred around provider ‘personal experience and expertise’, training and time availability. Domain 3: Practice-related factors included healthcare interventions to promote safer opioid practices and accessibility of quality alternative pain therapies.

Despite the differences in the contexts of the two countries, providers consider similar patient, provider and practice-related factors when long-term prescribing opioids for patients with cancer. Some of these factors may be categorised as cognitive biases that may intersect in an already disadvantaged patient and exacerbate disparities in the treatment of their pain. A more systematic understanding of these factors and how they impact the quality of care can inform appropriate interventions.

Treatment for abdominal pain in patients with chronic pancreatitis (CP) remains challenging in the setting of central nervous system sensitisation, a phenomenon of remodelling and neuronal hyperexcitability resulting from persistent pain stimuli. This is suspected to render affected individuals less likely to respond to conventional therapies. Endotherapy or surgical decompression is offered to patients with pancreatic duct obstruction. However, the response to treatment is unpredictable. Pancreatic quantitative sensory testing (P-QST), an investigative technique of standardised stimulations to test the pain system in CP, has been used for phenotyping patients into three mutually exclusive groups: no central sensitisation, segmental sensitisation (pancreatic viscerotome) and widespread hyperalgesia suggestive of supraspinal central sensitisation. We will test the predictive capability of the pretreatment P-QST phenotype to predict the likelihood of pain improvement following invasive treatment for painful CP.

This observational clinical trial will enrol 150 patients from the University of Pittsburgh, Johns Hopkins and Indiana University. Participants will undergo pretreatment phenotyping with P-QST. Treatment will be pancreatic endotherapy or surgery for clearance of painful pancreatic duct obstruction. Primary outcome: average pain score over the preceding 7 days measured by Numeric Rating Scale at 6 months postintervention. Secondary outcomes will include changes in opioid use during follow-up, and patient-reported outcomes in pain and quality of life at 3, 6 and 12 months after the intervention. Exploratory outcomes will include creation of a model for individualised prediction of response to invasive treatment.

The trial will evaluate the ability of P-QST to predict response to invasive treatment for painful CP and develop a predictive model for individualised prediction of treatment response for widespread use. This trial was approved by the University of Pittsburgh Institutional Review Board. Data and results will be reported and disseminated in conjunction with National Institutes of Health policies.

NCT04996628.

Chronic inflammation plays a key role in knee osteoarthritis pathophysiology and increases risk of comorbidities, yet most interventions do not typically target inflammation. Our study will investigate if an anti-inflammatory dietary programme is superior to a standard care low-fat dietary programme for improving knee pain, function and quality-of-life in people with knee osteoarthritis.

The eFEct of an Anti-inflammatory diet for knee oSTeoarthritis study is a parallel-group, assessor-blinded, superiority randomised controlled trial. Following baseline assessment, 144 participants aged 45–85 years with symptomatic knee osteoarthritis will be randomly allocated to one of two treatment groups (1:1 ratio). Participants randomised to the anti-inflammatory dietary programme will receive six dietary consultations over 12 weeks (two in-person and four phone/videoconference) and additional educational and behaviour change resources. The consultations and resources emphasise nutrient-dense minimally processed anti-inflammatory foods and discourage proinflammatory processed foods. Participants randomised to the standard care low-fat dietary programme will receive three dietary consultations over 12 weeks (two in-person and one phone/videoconference) consisting of healthy eating advice and education based on the Australian Dietary Guidelines, reflecting usual care in Australia. Adherence will be assessed with 3-day food diaries. Outcomes are assessed at 12 weeks and 6 months. The primary outcome will be change from baseline to 12 weeks in the mean score on four Knee injury and Osteoarthritis Outcome Score (KOOS₄) subscales: knee pain, symptoms, function in daily activities and knee-related quality of life. Secondary outcomes include change in individual KOOS subscale scores, patient-perceived improvement, health-related quality of life, body mass and composition using dual-energy X-ray absorptiometry, inflammatory (high-sensitivity C reactive protein, interleukins, tumour necrosis factor-α) and metabolic blood biomarkers (glucose, glycated haemoglobin (HbA1c), insulin, liver function, lipids), lower-limb function and physical activity.

The study has received ethics approval from La Trobe University Human Ethics Committee. Results will be presented in peer-reviewed journals and at international conferences.

ACTRN12622000440729.

Dementia is a complex medical condition that poses significant challenges to healthcare systems and support services. People living with dementia (PLWD) and their carers experience complex needs often exacerbated by social isolation and challenges in accessing support. Social prescribing (SP) seeks to enable PLWD and their carers to access community and voluntary sector resources to support them address such needs. Existing research, however, does not describe what SP interventions are currently in place in dementia care. Little is known about the needs these interventions are designed to address, the reasons that lead PLWD and their carers to participate in them, their effectiveness and the extent to which they could increase positive health outcomes if adopted and how.

A complex intervention systematic review of SP for PLWD and/or their carers will be conducted using an iterative logic model approach. Six electronic (MEDLINE, EMBASE, PsycINFO, CINAHL, Scopus and Cochrane/CENTRAL) and two grey literature databases (EThOS and CORE) were searched for publications between 1 January 2003 and June 2023, supplemented by handsearching of reference lists of included studies. Study selection, data extraction and risk of bias assessment, using Gough’s Weight of Evidence Framework, will be independently performed by two reviewers. A narrative approach will be employed to synthesise and report quantitative and qualitative data. Reporting will be informed by the Preferred Reporting Items for Systematic Review and Meta-Analysis Complex Interventions extension statement and checklist.

No ethical approval is required due to this systematic review operating only with secondary sources. Findings will be disseminated through peer-reviewed publications, conference presentations and meetings with key stakeholders including healthcare professionals, patient and carer groups, community organisations (eg, the Social Prescribing Network and the Evidence Collaborative at the National Academy for Social Prescribing), policymakers and funding bodies.

CRD42023428625.

Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration.

We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU.

A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay.

Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model.

A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort.

Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653).

We showed that a scoring system incorporating data from a patient’s stay in the ICU has better performance than commonly used EWS systems based on vital signs alone.

ISRCTN32008295.