Surgical stress results in immune dysfunction, predisposing patients to infections in the postoperative period and potentially increasing the risk of cancer recurrence. Perioperative immunonutrition with arginine-enhanced diets has been found to potentially improve short-term and cancer outcomes. This study seeks to measure the impact of perioperative immunomodulation on biomarkers of the immune response and perioperative outcomes following hepatopancreaticobiliary surgery.

This is a 1:1:1 randomised, controlled and blinded superiority trial of 45 patients. Baseline and perioperative variables were collected to evaluate immune function, clinical outcomes and feasibility outcomes. The primary outcome is a reduction in natural killer cell killing as measured on postoperative day 1 compared with baseline between the control and experimental cohorts.

This trial has been approved by the research ethics boards at participating sites and Health Canada (parent control number: 223646). Results will be distributed widely through local and international meetings, presentation, publication and ClinicalTrials.gov (identifier: NCT04549662). Any modifications to the protocol will be communicated via publications and ClinicalTrials.gov.

ClinicalTrials.gov identifier: NCT04549662.

An increasing number of tracheotomised and/or ventilated patients with high-cost out-of-hospital intensive care needs and, at the same time, a decreasing number of healthcare professionals inevitably lead to challenges in the care of this patient population. In addition, little is known about this population, their health restrictions, needs, patient journeys, care structures and processes. The project ‘Needs, requirements and cross-sectoral care pathways of out-of-hospital ventilated intensive care patients’ (ATME) aims to analyse these aspects and explore current care structures to inform further development of care in line with patients’ needs and requirements.

Qualitative and quantitative methods will be used. In preparation of a semistandardised survey, exploratory interviews will be conducted with tracheotomised and/or ventilated patients with out-of-hospital intensive care needs (TVPOI) (n=15), nursing care providers (n=30), outpatient medical centres, as well as outpatient medical, medical technology and therapeutic care providers (n=35). Three semistandardised survey questionnaires for TVPOI (n=2,000) will be developed and conducted with nursing care facilities (n=250) and outpatient medical centres for mechanical ventilation (n=25). Content analyses will be conducted for qualitative data; survey data will be analysed descriptively. In addition, healthcare claims data will be analysed descriptively to provide information on patient journeys. Three result workshops and one consensus conference will be carried out with representatives of the relevant target groups to analyse the suitability of care structures and to develop recommendations for action to improve TVPOI.

The ATME study received a positive vote from the Ethics Committee of the Osnabrück University of Applied Sciences and is registered in ‘Deutsches Register Klinischer Studien (DRKS)’ (registration number: DRKS00030891). The study results will be presented at national conferences and in relevant peer-reviewed journals. Additionally, study results will be published by the funding institution (the Innovation Committee of the Federal Joint Committee) on their website.

A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.

A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.

The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.

NCT06018545.

Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.

We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.

We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.

5241 patients were included in the analysis. For ICU days 2–7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4–7 for all states.

We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a

Rises in average life expectancy, increased comorbidities and frailty among older patients lead to higher admission rates to intensive care units (ICU). During an ICU stay, loss of physical and cognitive functions may occur, causing prolonged rehabilitation. Some functions may be lost permanently, affecting quality of life (QoL). There is a lack of understanding regarding how many variables are relevant to health-related outcomes and which outcomes are significant for the QoL of frail, elderly patients following discharge from the ICU. Therefore, this scoping review aims to identify reported variables for health-related outcomes and explore perspectives regarding QoL for this patient group.

The Joanna Briggs Institute guidelines for scoping reviews will be employed and original, peer-reviewed studies in English and Scandinavian languages published from 2013 to 2023 will be included. The search will be conducted from July 2023 to December 2023, according to the inclusion criteria in Embase, MEDLINE, PsycINFO and CINAHL. References to identified studies will be hand-searched, along with backward and forward citation searching for systematic reviews. A librarian will support and qualify the search strategy. Two reviewers will independently screen eligible studies and perform data extraction according to predefined headings. In the event of disagreements, a third reviewer will adjudicate until consensus is achieved. Results will be presented narratively and in table form and discussed in relation to relevant literature.

Ethical approval is unnecessary, as the review synthesises existing research. The results will be disseminated through a peer-reviewed publication in a scientific journal.

Quantify the risk of mental health (MH)-related emergency department visits (EDVs) due to heat, in the city of Curitiba, Brazil.

Daily time series analysis, using quasi-Poisson combined with distributed lag non-linear model on EDV for MH disorders, from 2017 to 2021.

All nine emergency centres from the public health system, in Curitiba.

101 452 EDVs for MH disorders and suicide attempts over 5 years, from patients residing inside the territory of Curitiba.

Relative risk of EDV (RR_EDV) due to extreme mean temperature (24.5°C, 99th percentile) relative to the median (18.02°C), controlling for long-term trends, air pollution and humidity, and measuring effects delayed up to 10 days.

Extreme heat was associated with higher single-lag EDV risk of RR_EDV 1.03(95% CI 1.01 to 1.05—single-lag 2), and cumulatively of RR_EDV 1.15 (95% CI 1.05 to 1.26—lag-cumulative 0–6). Strong risk was observed for patients with suicide attempts (RR_EDV 1.85, 95% CI 1.08 to 3.16) and neurotic disorders (RR_EDV 1.18, 95% CI 1.06 to 1.31). As to demographic subgroups, females (RR_EDV 1.20, 95% CI 1.08 to 1.34) and patients aged 18–64 (RR_EDV 1.18, 95% CI 1.07 to 1.30) were significantly endangered. Extreme heat resulted in lower risks of EDV for patients with organic disorders (RR_EDV 0.60, 95% CI 0.40 to 0.89), personality disorders (RR_EDV 0.48, 95% CI 0.26 to 0.91) and MH in general in the elderly ≥65 (RR_EDV 0.77, 95% CI 0.60 to 0.98). We found no significant RR_EDV among males and patients aged 0–17.

The risk of MH-related EDV due to heat is elevated for the entire study population, but very differentiated by subgroups. This opens avenue for adaptation policies in healthcare: such as monitoring populations at risk and establishing an early warning systems to prevent exacerbation of MH episodes and to reduce suicide attempts. Further studies are welcome, why the reported risk differences occur and what, if any, role healthcare seeking barriers might play.

Women with type 2 diabetes (T2DM) are more likely to experience adverse reproductive outcomes, yet preconception care can significantly reduce these risks. For women with T2DM, preconception care includes reproductive planning and patient education on: (1) the importance of achieving glycaemic control before pregnancy, (2) using effective contraception until pregnancy is desired, (3) discontinuing teratogenic medications if pregnancy could occur, (4) taking folic acid, and (5) managing cardiovascular and other risks. Despite its importance, few women with T2DM receive recommended preconception care.

We are conducting a two-arm, clinic-randomised trial at 51 primary care practices in Chicago, Illinois to evaluate a technology-based strategy to ‘hardwire’ preconception care for women of reproductive age with T2DM (the PREPARED (Promoting REproductive Planning And REadiness in Diabetes) strategy) versus usual care. PREPARED leverages electronic health record (EHR) technology before and during primary care visits to: (1) promote medication safety, (2) prompt preconception counselling and reproductive planning, and (3) deliver patient-friendly educational tools to reinforce counselling. Post-visit, text messaging is used to: (4) encourage healthy lifestyle behaviours. English and Spanish-speaking women, aged 18–44 years, with T2DM will be enrolled (N=840; n=420 per arm) and will receive either PREPARED or usual care based on their clinic’s assignment. Data will be collected from patient interviews and the EHR. Outcomes include haemoglobin A1c (primary), reproductive knowledge and self-management behaviours. We will use generalised linear mixed-effects models (GLMMs) to evaluate the impact of PREPARED on these outcomes. GLMMs will include a fixed effect for treatment assignment (PREPARED vs usual care) and random clinic effects.

This study was approved by the Northwestern University Institutional Review Board (STU00214604). Study results will be published in journals with summaries shared online and with participants upon request.

ClinicalTrials.gov Registry (NCT04976881).