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Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis.

Background Chronic Obstructive Pulmonary Disease (COPD) poses significant public health and economic challenges and performant prognostic models may be useful to direct treatment. The purpose of this study was to develop predictive models, validate the DOSE and updated ADO predictive models, and identify predictors of future hospitalisation and mortality in contemporary Australian COPD patients. Methods Data from 8,578 inpatients and outpatients diagnosed with COPD (via post-bronchodilator spirometry) between 2006 and 2021 at a large South Australian tertiary public hospital were analysed. Multivariate logistic models and Cox regression, utilising penalised regularisation in multiply imputed data, were used to investigate predictors of hospitalisation due to COPD exacerbation at 1-, 3-, and 5-years post-diagnosis, and COPD-specific mortality at 3- and 5-years. Haemoglobin-corrected DLCO (DLCOc) was used to extend the DOSE and updated ADO models. Results Locally developed models could predict COPD-specific 1-year hospitalisation risk with AUCs 0.80 (95% CI = [0.76, 0.83]) in males and 0.82 (95% CI = [0.78, 0.86]) in females on a temporally distinct hold-out set, with 3- and 5-year AUCs falling within this range. COPD-specific mortality was predicted with AUCs of 0.90 (95% CI = [0.85, 0.94]) and 0.89 (95% CI = [0.84, 0.92]) at 3 and 5 years in females, and 0.90 (95% CI = [0.86, 0.93]) and 0.88 (95% CI = [0.84, 0.92]) in males. Cox regression models predicted survival well in the test set for both females (C-index = 0.88, 95% CI = [0.85, 0.90]) and males (C-index = 0.86, 95% CI = [0.82, 0.88]). Local model performance was superior to that of the DOSE and updated ADO models for all outcomes, although not always significantly. Among the selected predictors, reduced DLCOc was strongly predictive of all outcomes. and acts as a short-term survival risk for follow-up durations less than 10 years. There was no significant difference in performance between sex, and there were differences in selected features and feature strength between sexes. Extending the extant clinical models with DLCOc significantly improved updated ADO and DOSE model fit and improved discriminatory performance, with the extended ADO index achieving AUC of 0.77 (95% CI = [0.75, 0.79]) and 0.87 (95% CI = [0.84, 0.89]) for predicting 5-year hospitalisation and mortality respectively across the full cohort. The extended DOSE index performed similarly with AUCs 0.77 (95% CI = [0.75, 0.79]) and 0.87 (95% CI = 0.83, 0.89)) for 5-year hospitalisation and mortality. Conclusions Ours is the only large clinical cohort and prognostic study of Australian COPD patients to date. Locally developed models achieved greater discriminative performance than the original updated ADO and DOSE models within our cohort. Extending the ADO and DOSE models with DLCOc significantly improved model fit in our cohort. We recommend further research into the use of DLCOc as a prognostic index for COPD, and its inclusion in future modelling attempts. Trial registration Retrospectively registered. Clinical trial number: Not Applicable.

BackgroundAccurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical outcomes in patients with successful reperfusion following EVT in an Australian population.MethodsThe study included all patients who had ischaemic stroke with occlusion in the proximal anterior cerebral circulation and successful reperfusion post-EVT over a 7-year period. Multivariable logistic regression and Cox regression models, incorporating bootstrap and multiple imputation techniques, were used to identify predictors and develop models for key clinical outcomes: 3-month poor functional status; 30-day, 1-year and 3-year mortality; survival time.ResultsA total of 978 patients were included in the analyses. Predictors associated with one or more poor outcomes include: older age (ORs for every 5-year increase: 1.22–1.40), higher premorbid functional modified Rankin Scale (ORs: 1.31–1.75), higher baseline National Institutes of Health Stroke Scale (ORs: 1.05–1.07) score, higher blood glucose (ORs: 1.08–1.19), larger core volume (ORs for every 10 mL increase: 1.10–1.22), pre-EVT thrombolytic therapy (ORs: 0.44–0.56), history of heart failure (outcome: 30-day mortality, OR=1.87), interhospital transfer (ORs: 1.42 to 1.53), non-rural/regional stroke onset (outcome: functional dependency, OR=0.64), longer onset-to-groin puncture time (outcome: 3-year mortality, OR=1.08) and atherosclerosis-caused stroke (outcome: functional dependency, OR=1.68). The models using these predictors demonstrated moderate predictive abilities (area under the receiver operating characteristic curve range: 0.752–0.796).ConclusionOur models using real-world predictors assessed at hospital admission showed satisfactory performance in predicting poor functional outcomes and short-term and long-term mortality for patients with successful reperfusion following EVT. These can be used to inform EVT treatment provision and consent.

ObjectiveIn this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years.MethodsA cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.ResultsThe discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.ConclusionIn our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment.Key Points• Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data.• Patient reported outcomes were more consistent predictors of function than traditional prognostic markers.• Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.

Barcode medication administration (BCMA) technology can improve patient safety by using scanning technology to ensure the right drug and dose are given to the right patient. Implementation can be challenging, requiring adoption of different workflows by nursing staff. In one London National Health Service trust scanning rates were lower than desired at around 0–20% of doses per ward. Our objective was to encourage patient safety behaviours in the form of medication scanning through implementation of a feedback intervention. This was informed by behavioural science, codesigned with nurses and informed by known barriers to use. Five wards were selected to trial the intervention over an 18-week period beginning August 2021. The remaining 14 hospital wards acted as controls. Intervention wards had varying uptake of BCMA at baseline and represented a range of specialties. A bespoke feedback intervention comprising three behavioural science constructs (gamification, the messenger effect and framing) was delivered to each intervention ward each week. A linear difference-in-difference analysis was used to evaluate the impact of our intervention on scan rates, both for the overall 18-week period and at two weekly intervals within this timeframe. We identified a 23.1 percentage point increase in medication scan rates (from an average baseline of 15.0% to 38.1%) on the intervention wards compared with control (p<0.001) following implementation of the intervention. Feedback had most impact in the first 6 weeks, with an initial percentage point increase of 26.3 (p<0.001), which subsequently plateaued. Neither clinical specialty nor number of beds on each ward were significant factors in our models. Our study demonstrated that a feedback intervention, codesigned with end users and incorporating behavioural science constructs, can lead to a significant increase in the adoption of BCMA scanning.

This chapter proposes a method for estimating risk to honey bees (Apis mellifera) and non-Apis bees from pesticides that are applied through sprays (acting on contact) and through seed or soil treatments and tree trunk injections (acting systemically). It describes the risk assessment process for honey bees and non-Apis bees. Problem formulation articulates the intent of the risk assessment and is predicated on particular protection goals for which the regulatory authority is responsible. The chapter illustrates the proposed risk assessment process identified by the participants of the 2011 SETAC Workshop on Pesticide Risk Assessment for Pollinators. The potential risk to adult honey bees from spray applications is assessed through calculation of an HQ. The screening-level and refined risk assessment processes for soil or seed treatment-applied pesticides incorporate different degrees of ecological realism. Screening-level assessments are typically based on conservative assumptions regarding both exposure and effects.