Research publications
Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre.
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Journal articlepatel NP, seneci CS, yang GZY, et al.,
Flexible platforms for natural orifice transluminal and endoluminal surgery. Endoscopy International Open, 2(02), E117-E123.
, Endoscopy International Open -
Journal articleVrielink TJCO, Pang YW, Zhao M, et al.,
Surgical task-space optimisation of the CYCLOPS robotic system
The CYCLOPS is a cable-driven parallel mechanism used for minimally invasiveapplications, with the ability to be customised to different surgical needs;allowing it to be made procedure- and patient-specific. For adequateoptimisation, however, appropriate data on clinical constraints and task-spaceis required. Whereas the former can be provided through preoperative planningand imaging, the latter remains a problem, primarily for highly dexterous MISsystems. The current work focuses on the development of a task-spaceoptimisation method for the CYCLOPS system and the development of a datacollection method in a simulation environment for minimally invasivetask-spaces. The same data collection method can be used for the development ofother minimally invasive platforms. A case-study is used to illustrate thedeveloped method for Endoscopic Submucosal Dissection (ESD). This paper showsthat using this method, the system can be succesfully optimised for thisapplication.
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Journal articleEspinosa-Gonzalez A, Prociuk D, Fiorentino F, et al.,
Remote Covid Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Accurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Prospective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>Data were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of ‘low risk’ 98.8%. RECAP-O2 included age, de
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Journal articleNurek M, Rayner C, Freyer A, et al.,
Recommendations for the Recognition, Diagnosis, and Management of Patients with Post COVID-19 Condition ('Long COVID'): A Delphi Study
, SSRN Electronic Journal -
Journal articleEspinosa-Gonzalez AB, Neves AL, Fiorentino F, et al.,
Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool (Preprint)
<sec> <title>BACKGROUND</title> <p>During the pandemic, remote consultations have become the norm for assessing patients with signs and symptoms of COVID-19 to decrease the risk of transmission. This has intensified the clinical uncertainty already experienced by primary care clinicians when assessing patients with suspected COVID-19 and has prompted the use of risk prediction scores, such as the National Early Warning Score (NEWS2), to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and are not designed to capture the idiosyncrasies of COVID-19 infection.</p> </sec> <sec> <title>OBJECTIVE</title> <p>The objective of this study is to produce a multivariate risk prediction tool, RECAP-V1 (Remote COVID-19 Assessment in Primary Care), to support primary care clinicians in the identification of those patients with COVID-19 that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes.</p> </sec> <sec> <title>METHODS</title> <p>The study follows a prospective cohort observational design, whereby patients presenting in primary care with signs and symptoms suggestive of COVID-19 will be followed and their data linked to hospital outcomes (hospital admission and death). Data collection will be carried out by primary care clinicians in four arms: North West London Clinical Commissioning Groups (NWL CCGs), Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), Covid Clinical Assessment Service (CCAS), and South East London CCGs (Doctaly platform). The study involves the use o
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