The relationship between obesity and patient-reported outcome measures in people with polymyalgia rheumatica.

Objective: To examine the association between obesity and patient-reported outcome measures (PROMs) in a primary care-based cohort of people with PMR. Methods: The PMR Cohort Study recruited people with incident PMR from 382 general practices. Self-completed questionnaires (0, 12, 24 months) captured a range of PROMs for pain, stiffness, anxiety, depression, fatigue, function and quality of life, alongside data on BMI. People were categorized as underweight/normal weight (BMI < 25kg/m2), overweight (25-29.99 kg/m2) or obese (≥30 kg/m2). Piecewise, multilevel, linear mixed-effects regression models examined relationships between BMI categories and PROMs over time, adjusting for confounding variables. Chi-squared tests examined the relationship between obesity and glucocorticoid persistence. Results: 644 people with PMR were included. At baseline, 33.9% were normal/underweight, 40.6% overweight and 25.5% obese. Compared with normal/underweight people, those with obesity had significantly worse scores for the following: pain and stiffness at 12 months; fatigue at 12 and 24 months; depression at baseline; physical function at all time points; and quality of life at baseline and 12 months. They also had significantly smaller improvements in stiffness (1.13 units on an 11-point numeric rating scale; P = 0.001) and physical function (0.14 units measured using the modified Health Assessment Questionnaire; P = 0.025) between 0 and 12 months. BMI categories did not relate to persistent glucocorticoid use at 12 months (P = 0.110) or 24 months (P = 0.166). Conclusion: Obesity associates with poorer outcomes for a range of PROMs in people with PMR. Consideration should be given to providing weight management support to people with PMR and obesity.

Management of refractory disease and persistent symptoms in inflammatory arthritis: qualitative framework analysis of interviews with patients and healthcare professionals.

Objectives: This study aims to explore patients' and clinicians' experiences in managing and living with refractory disease (RD) and persistent physical and emotional symptoms (PPES) in patients with RA or polyarticular JIA from their perspectives through interviews and/or focus groups. Methods: A qualitative exploration with 25 patients and 32 multidisciplinary rheumatology healthcare professionals (HCPs) was conducted to obtain participants respective understanding and experiences of managing RD/PPES and its impact on the patient-professional relationship. A pragmatic epistemology approach with framework analysis was employed. Results: Four key themes were identified from both patients and professionals in the management of RD/PPES: risk/perpetuating factors/triggers; need for a patient-centred holistic approach to care, diagnosis and treatment; discordance and impact on the patient-practitioner relationship and current problems in managing RD/PPES. These themes covered 22 subthemes, with none being patient specific and seven being HCP specific. Suggestions for potential management strategies were highlighted throughout, such as involving other specialties or a multidisciplinary team, assessing/treating patient-reported outcome measures and psychosocial factors, patient (re)education, need for adjustments/aids or adaptations, checking the diagnosis and further investigations/imaging and optimizing medications. Conclusion: Management strategies need to be developed that enable appropriate treatment plans for those with RD/PPES that account for wider biopsychosocial factors beyond inflammation and reduce discordance in the patient-practitioner relationship.

Laying train tracks en route: How institutional education leaders navigate complexity during mandated curriculum change.

INTRODUCTION: Institutional education leaders serve key roles in leading major curricular change within residency education, yet little is known about how they accomplish these goals on the ground. Change management principles have predominantly been developed and described in the hierarchical context of management science and corporate settings. However, the non-hierarchical, complex and adaptive features of health professions education may render these traditional change management models inadequate. We explored how institutional educational leaders navigate the complex residency education system in implementing a major curricular change. METHODS: Using constructivist grounded theory, we conducted and iteratively analysed semi-structured interviews with 11 institutional education leaders from across Canada who were responsible for leading the nationally mandated curricular change to competency-based residency education. Thematic analysis was performed iteratively using constant comparison. RESULTS: Leaders managing the change process focused on two priorities: steering the direction of the change process as it evolved and maintaining the momentum amongst stakeholders to move forward steadily. Four common threats and opportunities impacted the focus on direction and momentum: multiplicity of contexts, innovation, resistance and distractions. In response, leaders utilised various tactics and harnessed diverse leadership styles to manage these challenges accordingly. CONCLUSIONS: We identified a change framework that offers a more contextually nuanced understanding of curricular change in residency education that has not been described in the change management literature generated by the management sector. Institutional education leaders focused on maintaining the direction and momentum, while constantly assessing and adapting to evolving, uncertain and complex conditions. Our findings provide a simple and practical foundation to support leadership education in curricular change as well as researchers in developing further change theories in complex adaptive health professions education systems.

Google Internet searches related to inflammatory arthritis: An observational study using Google Trends data.

OBJECTIVE: The Internet has transformed how patients access health information. We examined Google search engine data to understand which aspects of health are most often searched for in combination with inflammatory arthritis (IA). METHODS: Using Google Trends data (2011-2022) we determined the relative popularity of searches for 'patient symptoms' (pain, fatigue, stiffness, mood, work) and 'treat-to-target' (disease-modifying drugs, steroids, swelling, inflammation) health domains made with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (AxSpA) in the UK/USA. Google Trends normalises searches by popularity over time and region, generating 0-100 scale relative search volumes (RSV; 100 represents the time-point with most searches). Up to five search term combinations can be compared. RESULTS: In all IA forms, pain was the most popular patient symptom domain. UK/USA searches for pain gave mean RSVs of 58/79, 34/51, and 39/63 with RA, PsA, and AxSpA; mean UK/USA RSVs for other patient symptom domains ranged 2-7/2-8. Methotrexate was the most popular treat-to-target search term with RA/PsA in the UK (mean 28/21) and USA (mean 63/33). For AxSpA, inflammation was most popular (mean UK/USA 9/34). Searches for pain were substantially more popular than searches for methotrexate in RA and PsA, and inflammation in AxSpA. Searches increased over time. CONCLUSIONS: Pain is the most popular search term used with IA in Google searches in the UK/USA, supporting surveys/qualitative studies highlighting the importance of improving pain to patients with IA. Routine pain assessments should be embedded within treat-to-target strategies to ensure patient perspectives are considered.

Evaluating automated machine learning platforms for use in healthcare.

Objective: To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models. Materials and Methods: Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes. Results: The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case. Discussion: A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example. Conclusion: An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.

EPidemiology Of Cardiogenic sHock in Scotland (EPOCHS): A multicentre, prospective observational study of the prevalence, management and outcomes of cardiogenic shock in Scotland.

Background: Despite high rates of cardiovascular disease in Scotland, the prevalence and outcomes of patients with cardiogenic shock are unknown. Methods: We undertook a prospective observational cohort study of consecutive patients with cardiogenic shock admitted to the intensive care unit (ICU) or coronary care unit at 13 hospitals in Scotland for a 6-month period. Denominator data from the Scottish Intensive Care Society Audit Group were used to estimate ICU prevalence; data for coronary care units were unavailable. We undertook multivariable logistic regression to identify factors associated with in-hospital mortality. Results: In total, 247 patients with cardiogenic shock were included. After exclusion of coronary care unit admissions, this comprised 3.0% of all ICU admissions during the study period (95% confidence interval [CI] 2.6%-3.5%). Aetiology was acute myocardial infarction (AMI) in 48%. The commonest vasoactive treatment was noradrenaline (56%) followed by adrenaline (46%) and dobutamine (40%). Mechanical circulatory support was used in 30%. Overall in-hospital mortality was 55%. After multivariable logistic regression, age (odds ratio [OR] 1.04, 95% CI 1.02-1.06), admission lactate (OR 1.10, 95% CI 1.05-1.19), Society for Cardiovascular Angiographic Intervention stage D or E at presentation (OR 2.16, 95% CI 1.10-4.29) and use of adrenaline (OR 2.73, 95% CI 1.40-5.40) were associated with mortality. Conclusions: In Scotland the prevalence of cardiogenic shock was 3% of all ICU admissions; more than half died prior to discharge. There was significant variation in treatment approaches, particularly with respect to vasoactive support strategy.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model.

BACKGROUND: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. AIM: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. METHODS: A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). RESULTS: A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. CONCLUSION: The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Achieving large-scale clinician adoption of AI-enabled decision support.

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

Developing an Adolescent and Young Adult Oncology Program in a Medium-Sized Canadian Centre: Lessons Learned.

The Adolescent and Young Adult (AYA) Program at CancerCare Manitoba (CCMB) has experienced tremendous growth since its inception. This report provides an overview of how the AYA program at CCMB was established and the crucial factors that led to its early accomplishments and continued expansion. These factors included actions and decisions made at the individual and organizational level that helped lay a strong foundation for the program's sustained success. We hope that some of these lessons learned can be adapted and implemented by other oncology agencies to improve the care outcomes and experiences of AYAs living with cancer.

Role of CYP9E2 and a long non-coding RNA gene in resistance to a spinosad insecticide in the Colorado potato beetle, Leptinotarsa decemlineata.

Spinosads are insecticides used to control insect pests, especially in organic farming where limited tools for pest management exist. However, resistance has developed to spinosads in economically important pests, including Colorado potato beetle (CPB), Leptinotarsa decemlineata. In this study, we used bioassays to determine spinosad sensitivity of two field populations of CPB, one from an organic farm exposed exclusively to spinosad and one from a conventional farm exposed to a variety of insecticides, and a reference insecticide naïve population. We found the field populations exhibited significant levels of resistance compared with the sensitive population. Then, we compared transcriptome profiles between the two field populations to identify genes associated primarily with spinosad resistance and found a cytochrome P450, CYP9E2, and a long non-coding RNA gene, lncRNA-2, were upregulated in the exclusively spinosad-exposed population. Knock-down of these two genes simultaneously in beetles of the spinosad-exposed population using RNA interference (RNAi) resulted in a significant increase in mortality when gene knock-down was followed by spinosad exposure, whereas single knock-downs of each gene produced smaller effects. In addition, knock-down of the lncRNA-2 gene individually resulted in significant reduction in CYP9E2 transcripts. Finally, in silico analysis using an RNA-RNA interaction tool revealed that CYP9E2 mRNA contains multiple binding sites for the lncRNA-2 transcript. Our results imply that CYP9E2 and lncRNA-2 jointly contribute to spinosad resistance in CPB, and lncRNA-2 is involved in regulation of CYP9E2 expression. These results provide evidence that metabolic resistance, driven by overexpression of CYP and lncRNA genes, contributes to spinosad resistance in CPB.

Reducing potentially inappropriate polypharmacy at a national and international level: the impact of deprescribing networks.

INTRODUCTION: Over the past decade, polypharmacy has increased dramatically. Measurable harms include falls, fractures, cognitive impairment, and death. The associated costs are massive and contribute substantially to low-value health care. Deprescribing is a promising solution, but there are barriers. Establishing a network to address polypharmacy can help overcome barriers by connecting individuals with an interest and expertise in deprescribing and can act as an important source of motivation and resources. AREAS COVERED: Over the past decade, several deprescribing networks were launched to help tackle polypharmacy, with evidence of individual and collective impact. A network approach has several advantages; it can spark interest, ideas and enthusiasm through information sharing, meetings and conversations with the public, providers, and other key stakeholders. In this special report, the details of how four deprescribing networks were established across the globe are detailed. EXPERT OPINION: Networks create links between people who lead existing and/or budding deprescribing practices and policy initiatives, can influence people with a shared passion for deprescribing, and facilitate sharing of intellectual capital and tools to take initiatives further and strengthen impact.This report should inspire others to establish their own deprescribing networks, a critical step in accelerating a global deprescribing movement.

The new paradigm in machine learning - foundation models, large language models and beyond: a primer for physicians.

Foundation machine learning models are deep learning models capable of performing many different tasks using different data modalities such as text, audio, images and video. They represent a major shift from traditional task-specific machine learning prediction models. Large language models (LLM), brought to wide public prominence in the form of ChatGPT, are text-based foundational models that have the potential to transform medicine by enabling automation of a range of tasks, including writing discharge summaries, answering patients questions and assisting in clinical decision-making. However, such models are not without risk and can potentially cause harm if their development, evaluation and use are devoid of proper scrutiny. This narrative review describes the different types of LLM, their emerging applications and potential limitations and bias and likely future translation into clinical practice.

How should artificial intelligence be used in Australian health care? Recommendations from a citizens' jury.

OBJECTIVE: To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care. STUDY DESIGN: Citizens' jury, deliberating the question: "Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?" SETTING, PARTICIPANTS: Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March - 2 April 2023): fifteen days online and three days face-to-face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face-to-face meeting. MAIN OUTCOME MEASURES: Jury recommendations, with reasons. RESULTS: The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients' rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication. CONCLUSIONS: The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.

Through a Tainted Lens: A Qualitatve Study of Medical Learners' Thinking About Patient 'Deservingness' of Health Advocacy.

Introduction: While health advocacy is a key component of many competency frameworks, mounting evidence suggests that learners do not see it as core to their learning and future practice. When learners do advocate for their patients, they characterize this work as 'going above and beyond' for a select few patients. When they think about advocacy in this way, learners choose who deserves their efforts. For educators and policymakers to support learners in making these decisions thoughtfully and ethically, we must first understand how they are currently thinking about patient deservingness. Methods: We conducted qualitative interviews with 29 undergraduate and postgraduate medical learners, across multiple sites and disciplines, to discuss their experiences of and decision-making about health advocacy. We then carried out a thematic analysis to understand how learners decided when and for whom to advocate. Stemming from initial inductive coding, we then developed a deductive coding framework, based in existing theory conceptualizing 'deservingness.' Results: Learners saw their patients as deserving of advocacy if they believed that the patient: was not responsible for their condition, was more in need of support than others, had a positive attitude, was working to improve their health, and shared similarities to the learner. Learners noted the tensions inherent in, and discomfort with, their own thinking about patient deservingness. Discussion: Learners' decisions about advocacy deservingness are rooted in their preconceptions about the patient. Explicit curriculum and conversations about advocacy decisions are needed to support learners in making advocacy decisions equitably.

Monoclonal antibodies for treating early Alzheimer disease-a commentary on recent 'positive' trials.

Recent phase 3 randomised controlled trials of amyloid-targeting monoclonal antibodies in people with pre-clinical or early Alzheimer disease have reported positive results, raising hope of finally having disease-modifying drugs. Given their far-reaching implications for clinical practice, the methods and findings of these trials, and the disease causation theory underpinning the mechanism of drug action, need to be critically appraised. Key considerations are the representativeness of trial populations; balance of prognostic factors at baseline; psychometric properties and minimal clinically important differences of the primary efficacy outcome measures; level of study fidelity; consistency of subgroup analyses; replication of findings in similar trials; sponsor role and potential conflicts of interest; consistency of results with disease causation theory; cost and resource estimates; and alternative prevention and treatment strategies. In this commentary, we show shortcomings in each of these areas and conclude that monoclonal antibody treatment for early Alzheimer disease is lacking high-quality evidence of clinically meaningful impacts at an affordable cost.

Designer Small-Molecule Control System Based on Minocycline-Induced Disruption of Protein-Protein Interaction.

A versatile, safe, and effective small-molecule control system is highly desirable for clinical cell therapy applications. Therefore, we developed a two-component small-molecule control system based on the disruption of protein-protein interactions using minocycline, an FDA-approved antibiotic with wide availability, excellent biodistribution, and low toxicity. The system comprises an anti-minocycline single-domain antibody (sdAb) and a minocycline-displaceable cyclic peptide. Here, we show how this versatile system can be applied to OFF-switch split CAR systems (MinoCAR) and universal CAR adaptors (MinoUniCAR) with reversible, transient, and dose-dependent suppression; to a tunable T cell activation module based on MyD88/CD40 signaling; to a controllable cellular payload secretion system based on IL12 KDEL retention; and as a cell/cell inducible junction. This work represents an important step forward in the development of a remote-controlled system to precisely control the timing, intensity, and safety of therapeutic interventions.

The relationship between pain and depression and anxiety in patients with inflammatory arthritis: a systematic review protocol.

Pain is a major challenge for patients with inflammatory arthritis (IA). Depression and anxiety are common comorbidities in IA, associating with worse outcomes. How they relate to pain is uncertain, with existing systematic reviews (a) mainly considering cross-sectional studies, (b) focusing on the relationship between pain and mental health in the context of disease activity/quality of life, and (c) not specifically considering the impact of treating depression/anxiety on pain. This PROSPERO-registered (CRD42023411823) systematic review will address this knowledge-gap by synthesizing evidence to summarise the associations (and potential mediators) between pain and depression/anxiety and evaluate the impact of treating co-morbid depression/anxiety on pain in IA. Relevant databases will be searched, articles screened and their quality appraised (using Joanna Briggs Institute critical appraisal tools) by two reviewers. Eligible studies will include adults with rheumatoid arthritis or spondyloarthritis, be a clinical trial or observational study, and either (a) report the relationship between pain and depression/anxiety (observational studies/baseline trials), or (b) randomise participants to a pharmacological or psychological treatment to manage depression/anxiety with a pain outcome as an endpoint (trials). To synthesise data on the association between pain and depression/anxiety, where available adjusted coefficients from regression models will be pooled in a random-effects meta-analysis. A synthesis without meta-analysis will summarise mediators. To evaluate the impact of treating depression/anxiety on pain, endpoint mean differences between treatment arms will be combined in a random-effects meta-analysis. Through understanding how depression/anxiety contribute to pain in IA, our review has the potential to help optimise approaches to IA pain.

Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain.

OBJECTIVE: To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. MATERIALS AND METHODS: A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. RESULTS: Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. DISCUSSION: Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. CONCLUSIONS: : There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers.