Accurate arterial pressure monitoring is critical in cardiac surgery to guide haemodynamic management and vasopressor therapy. Radial arterial pressure monitoring may systematically underestimate central aortic pressure compared with femoral monitoring, potentially leading to inappropriate vasopressor escalation and associated complications. Recent evidence demonstrates that excessive norepinephrine exposure is associated with acute kidney injury and increased mortality in cardiac surgery patients.

To determine whether femoral arterial pressure monitoring reduces norepinephrine use compared with radial monitoring in cardiac surgery patients.

This is a prospective, randomised, controlled, single-blind, superiority trial conducted at two French university hospitals (CHU Besancon and CHU Dijon). Adult patients undergoing cardiac surgery with cardiopulmonary bypass will be randomised 1:1 to receive either femoral or radial arterial pressure monitoring. The primary endpoint is the proportion of patients treated with norepinephrine from anaesthetic induction to postoperative day 7. Secondary endpoints include acute kidney injury according to KDIGO criteria, cardiac complications, vasoactive-inotropic scores, duration of vasopressor therapy, vascular complications, and 7-day and 30-day mortality. Sample size calculation indicates 340 patients (170 per group) are needed to detect a 15% absolute reduction in norepinephrine use with 90% power and α=0.05, and an anticipated loss to follow-up rate of 5%.

The study has been approved by the French Ethics Committee (Comité de Protection des Personnes Nord-Ouest II, no. 2024/897) and will be conducted according to the Declaration of Helsinki and Good Clinical Practice guidelines. Results will be submitted for publication in peer-reviewed journals and presented at international conferences.

NCT06952907.

Cognitive impairments, such as dementia and Alzheimer’s disease, are considered a significant public health challenge as they affect mental functions like memory, attention, language and decision-making. With the growing number of older individuals, the prevalence of these diseases is also increasing and is projected to reach 152 million worldwide by 2050. These disorders result in difficulties with judgement, communication and daily activities, leading to more hospitalisations and risks such as disorientation in the environment and a higher likelihood of falls. Current hospital safety assessment tools mainly focus on physical aspects and overlook other crucial factors. Therefore, this study aims to clarify the concept of safety challenges for patients with cognitive impairment and to develop and psychometrically validate a multidimensional instrument for use in hospital settings.

A sequential exploratory study with mixed methods will be conducted. In the first qualitative phase, based on a content analysis approach, the aim is to explain and clarify safety issues in patients with cognitive impairment. Participants will be purposively selected until saturation is reached in educational-therapeutic centres affiliated with the University of Medical Sciences, and unstructured in-depth interviews will be conducted. The data will be analysed simultaneously using MAXQDA V.20 software. Additionally, a literature review on safety issues in patients with cognitive impairment will be conducted to supplement aspects that may not have emerged in the interviews.

In the second phase, an instrument based on the inductive–deductive method will be developed. The items will be created based on the participants’ experiences and the literature review. Face, content and construct validity, as well as reliability, will be assessed. Data synthesis will involve a linkage strategy, where the qualitative data will be linked to the quantitative data immediately after the completion of the qualitative phase, once the protocols are developed.

This study is part of a postdoctoral project approved by the Tehran University of Medical Sciences Research Ethics Board (IR.TUMS.FNM.REC.1403.006). Findings will be disseminated at the local, national and international levels.

Intermittent physiological monitoring and early warning scores (EWS) are limited in their ability to detect deteriorating patients in a timely manner. Wearable physiological sensors allow continuous remote monitoring and may be more timely and accurate in the identification of those at risk, compared with manual collection. This study aims to determine if wearable physiological sensors can be used for the early detection of postoperative deterioration, while being acceptable to patients and healthcare staff.

This is a prospective observational cohort study that will recruit adults undergoing major surgery in Benin, India, Ghana, Guatemala, Mexico, Nigeria, Rwanda and the UK. Participants will wear wearable physiological chest and limb sensors before, during and after surgery for up to 10 days or until discharge. In this ‘shadow-mode’ study, continuous physiological observations collected using the devices will not be made available to clinical teams. No changes in participant care will result. Standard of care clinical data will be collected contemporaneously. Continuous sensor data will be used to design algorithms to predict deterioration and specific complications in this population. Usability and feasibility testing, through focus groups, interviews and questionnaires, will be undertaken with healthcare professionals and people undergoing surgery.

Our stakeholder panel are directly involved in all aspects of this study, which will be conducted in accordance with the principles of the International Conference on Harmonisation Tripartite Guideline for Good Clinical Practice (ICH GCP) in addition to the principles of the ethics committee(s)/Institutional Review Boards (IRBs) who have reviewed and approved this study. Artificial intelligence (AI) prediction models will be reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis+Artificial Intelligence (TRIPOD+AI) and Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) reporting guidelines frameworks.

NCT06565559.