East Midlands Evidence Repository (EMER)

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Welcome to the East Midlands Evidence Repository.

The East Midlands Evidence Repository (EMER) is the official institutional research repository for; Derbyshire Community Health Services, Leicester Partnership Trust, NHS Nottingham and Nottinghamshire CCG, Nottinghamshire Healthcare, Sherwood Forest Hospitals, University Hospitals of Derby and Burton and the University Hospitals Of Leicester

EMER is intended to make NHS research more visible and discoverable by capturing, storing and preserving the East Midlands research output and making it available to the research community through open access protocols.

Wherever possible, full-text content is provided for all research publications in the repository. Content grows daily as new collections are added.

 

 

  • Authors' response to correspondence re ethnic differences in radiotherapy outcomes in a majority south asian Leicester community

    Ahmad, S; Chauhan, M; Freeman, N; Mair, M; Nazareth, J; Patil, N; Reynolds, C; Sim, A; Walter, H (2024-05-08)
  • Oral cavity cancer and its pre-treatment radiological evaluation: a pictorial overview

    Lam, Vincent (2024-05-04)
    Purpose: Oral cavity cancer, primarily squamous cell carcinoma (SCC), is a prevalent malignancy globally, necessitating accurate clinical assessment and staging to enable effective treatment planning. Diagnosis requires biopsy and is followed by surgical resection and reconstruction as the primary therapeutic modality. Imaging plays a pivotal role during this process, aiding in the evaluation of tumour extent, nodal involvement and distant metastases. However, despite its value, both radiologists and clinicians must recognise its inherent limitations. Methods: This pictorial review article aims to illustrate the application of various imaging modalities in the pre-treatment evaluation of oral cavity SCC and highlights potential pitfalls. It underscores the importance of understanding the anatomical subsites of the oral cavity, the diverse patterns of spread tumours exhibit at each site, alongside the role of imaging in facilitating informed management strategies, while also acknowledging its limitations. Results: The review delves into fundamentals of current staging including nodal involvement, while, emphasising imaging strategies and potential limitations. Finally, it touches on the potential of novel radiomic techniques in characterising tumours and predicting treatment response. Conclusions: Pre-treatment oral cavity cancer staging reflects an ongoing quest for enhanced diagnostic accuracy and prognostic prediction. Recognising the value of imaging alongside its limitations fosters a multidisciplinary approach to treatment planning, ultimately improving patient outcomes.
  • Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms

    Antoun, Ibrahim; Barker, Joseph; Chin, Shui Hao; Dhutia, Harshil; Koev, Ivelin; Kotb, Ahmed; Mavilakandy, Akash; Ng, Andre; Thaitirarot, Chokanan (2024-01-30)
    Aims: European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs. Methods and results: A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81). Conclusion: Ambulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid sampling from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model. Question: Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)? Findings: In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults. Meaning: In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.
  • ERS International Congress 2023: highlights from the Respiratory Clinical Care and Physiology Assembly

    Latimer, Lorna (2024-05-20)
    It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, clinical trials and qualitative research that were presented during the 2023 European Respiratory Society International Congress within the sessions from the five groups of Assembly 1 (Respiratory Clinical Care and Physiology). Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.

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