The Leeds Teaching Hospitals Repository contains research, organisational learning and information generated by LTH staff and colleagues and departments.
Recent Submissions
Item Data usage to inform and improve patient and staff experience(2025 LTHT Research & Innovation Conference, 2025-07-10)Item Classic: An interactive, collaborative variant classification portal for genetic analysis.(2025 LTHT Research & Innovation Conference, 2025-07-10)Diagnostic genetic analysis is essential for identifying the causes of genetic disorders, enabling more precise and personalised treatments for patients. However, classifying genetic variants—determining which impact health and which are harmless—is a complex and time-consuming task. To address this challenge, bioinformatics specialists and genomic scientists developed ClassIC (Classification Information Commons), a user-friendly software tool that facilitates the recording, sharing, and curating of variant classifications. This tool aligns with national guidelines, supporting standardised analysis in NHS labs. Aims The primary goals of the software are to improve diagnostic accuracy, speed, and collaboration in genetic analysis through: More Accurate Diagnoses: The standardised classification system reduces misinterpretation, leading to more accurate diagnoses. Improved Collaboration: The platform enhances knowledge sharing among scientists, improving patient care. Faster Diagnoses: Streamlining the process reduces waiting times, ensuring timely care delivery. Methods ClassIC integrates into existing analysis workflows, allowing scientists to classify variants consistently. Deployed in Leeds, Sheffield, and Newcastle, the platform has been used for the classification of thousands of variants. The variant database has been combined with a user-friendly interface to reduce the time taken to analyse variants and allowing teams to share variant analysis data. Results ClassIC has been successfully implemented in Leeds and the wider region, with very positive feedback. The platform has enhanced collaboration, helping scientists to compare and refine analysis methods, and staff continue to contribute to the database’s ongoing development to maintain its effectiveness. Conclusion ClassIC improves genetic analysis by offering a collaborative platform that enhances diagnosis speed and accuracy. This approach enhances the overall quality of genetic analysis, ensuring that patients receive faster, more accurate diagnoses and better overall care. ClassIC is an example of innovation that provides an effective, user-friendly variant analysis portal, making Leeds one of the very few NHS labs with such an asset.Item Cancer Screening Data Integration in the NHS: Challenges and Solutions(2025 LTHT Research & Innovation Conference, 2025-07-10)Background: Cancer screening plays a critical role in early detection and reducing mortality rates, yet fragmented NHS data systems create challenges in tracking screening participation and patient outcomes. Data silos between primary care (GPs) and secondary care (hospitals) limit effective monitoring, making it difficult to optimize screening programs and reduce health disparities. Strengthening data integration, interoperability, and governance is essential for improving screening participation and enhancing cancer prevention efforts. Aims: This study aims to assess the feasibility of integrating cancer screening data within NHS systems, identify barriers to data-sharing, and propose policy-driven and technological solutions to improve data interoperability, research capabilities, and patient outcomes. Methods: A qualitative and policy analysis approach was used, including: • Stakeholder engagement with NHS data teams (Leeds Teaching Hospitals NHS Trust, West Yorkshire Integrated Care Board). • Assessment of NHS IT infrastructure, focusing on interoperability challenges between SystmOne (GPs) and PPM+ (hospitals). • Review of data governance policies, including IRAS approvals, GDPR compliance, and Caldicott Guardian regulations. • Comparative analysis of international data integration models (e.g., Germany’s DIFUTURE, Sweden’s eHealth, U.S. Cancer Moonshot Program). Results: • Cancer screening data in NHS is highly fragmented, limiting patient tracking and screening program evaluation. • Data governance restrictions (e.g., lack of cross-institutional data-sharing agreements) create barriers to research access. • Interoperability challenges due to the absence of standardized frameworks (HL7 FHIR, SNOMED-CT, DICOM) hinder seamless data exchange. • AI and digital health solutions (e.g., wearable technologies, cloud-based repositories) present opportunities for improving screening participation and early detection. Conclusion: The study highlights urgent need for NHS-wide data integration strategies to enhance cancer screening effectiveness, research accessibility, and patient care. Adopting standardized interoperability frameworks, strengthening data-sharing agreements, and leveraging AI-driven analytics could significantly improve screening participation and reduce health disparities. These findings have important implications for public health planning, digital transformation within NHS, and future cancer prevention strategies.Item Can 129Xe ventilation MRI guide personalisation of airway clearance regimens in children with Primary ciliary dyskinesia?(2025 LTHT Research & Innovation Conference, 2025-07-10)Background Children with Primary ciliary dyskinesia (PCD) use airway clearance techniques (ACT) to clear airway secretions, but physiotherapists lack sensitive outcome methods to guide ACT regimen personalisation. 129Xe Ventilation MRI (129Xe-MRI) is an imaging method that provides a 3D image of lung ventilation distribution. Aim We aimed to establish if providing data from 129Xe-MRI and structural MRI influenced physiotherapists’ decision making. Methods Children with a confirmed diagnosis of PCD were assessed with structural and 129Xe-MRI pre-, post- and 4-hours post their usual ACT regimen. During cognitive task analysis interviews, physiotherapists were asked to “think aloud” their clinical decisions whilst reviewing the data of children with PCD under their care: routine clinical information; baseline structural MRI; 129Xe-MRI data. Results Five experienced physiotherapists from four NHS trusts reviewed data from a total of 19 children with PCD (age 5-17years). Minimal ACT changes pertaining to positioning during airway clearance were proposed when physiotherapists reviewed the structural MRI scans. 129Xe-MRI data aligned with the physiotherapists’ existing clinical impression for nine cases. In these reviews, the 129Xe-MRI either confirmed the physiotherapists’ ACT regimen decision or led them to propose modifications. In ten cases 129Xe-MRI challenged the current clinical impression formed from prior knowledge of the patient, routine clinical information, and structural MRI findings. For these cases, the physiotherapist either: re-evaluated their clinical decisions and proposed ACT regimen modifications or; felt unsure what regimen changes to propose during the review, so planned to reassess the patient in light of the 129Xe-MRI findings. Conclusion Clinical review of 129Xe-MRI data led physiotherapists to propose ACT regimen changes in most children with PCD. In some cases, physiotherapists reported clinical re-assessment in light of the 129Xe-MRI findings was warranted. 129Xe-MRI provides an intuitive 3D lung ventilation image, which is sensitive to airways obstruction and can inform clinical ACT regimen personalisation.Item British Axial Spondyloarthritis Inception Cohort (BAxSIC): Driving Innovation and Empowering Research Participation in the NHS across the UK(2025 LTHT Research & Innovation Conference, 2025-07-10)Background: Axial spondyloarthritis (axSpA) is a chronic, disabling arthritis affecting young people. AxSpA remains underdiagnosed and undertreated, with diagnostic delays of 8.5 years affecting long-term outcomes. BAxSIC was set up in collaboration with BRITSpA and (NASS) (1) to address this unmet need. Aims: To provide real-world data on the impact of diagnostic delay in disease progression, work participation, and functional outcomes in axSpA. Methods: Multi-centre, observational, prospective inception cohort study of people with axSpA within 6 months of a confirmed diagnosis. Participants undergo in-person assessments at baseline and 24 months, with remote data collection at 6, 12, 18, 30, and 36 months using an innovative, low-burden virtual follow-up system integrating electronic health records and patient-reported outcomes via a dedicated REDCap platform. Results: Since June 2023, BAxSIC has expanded to 27 sites, enrolling 260 participants towards its 500 target. First results are expected in 2026, with no interim analysis planned. Showing remarkable flexibility, BAxSIC engages all size research teams across the UK despite limited funding. Participating patients and sites praise the ease of use of the online platform, convenience of remote consent and short 10-15 min online visits, reducing hospital appointments, whilst providing research teams real time access to clinically relevant data. Challenges include a 5 year set up delay with loss of funding and slow uptake of innovative and remote methods for clinical research after the COVID‐19 pandemic. At LTHT, BAxSIC led to the adoption of REDCap and creation of a dedicated research cloud. Conclusion: Data from BAxSIC will enhance early detection and optimise long-term management of axSpA. BAxSIC exemplifies LTHT priorities by driving innovation, expanding research participation, and fostering collaboration across NHS Trusts, academic institutions, and patient organisations to address key challenges in axSpA. Its patient-centred approach and digital innovation model leverages NHS infrastructure addressing inequities in research delivery capabilities and setting a precedent for future large-scale NHS research initiatives, ultimately improving patient outcomes and healthcare efficiency.
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