NIHRDataInsights/HRCSData huggingface dataset:
Title: Minimising and managing missing data in palliative care clinical trials.
Funder: Department of Health and Social Care

Abstract: Participants in palliative care trials may be too unwell to provide all the information required. Such missing data are not completely avoidable and reduce the precision of results. There are standard approaches to managing missing data, but the optimal methods for palliative care studies are unknown. I will explore the views of individuals involved in research, on how to minimise data loss. Subsequently different approaches to managing missing data will be tested using trial datasets. I aim to provide frameworks for minimising and analysing missing data that will inform trial design, leading to more accurate results and better patient care.

Lay abstract: Participants in palliative care trials may be too unwell to provide all the information required. Such missing data are not completely avoidable and reduce the precision of results. There are standard approaches to managing missing data, but the optimal methods for palliative care studies are unknown. I will explore the views of individuals involved in research, on how to minimise data loss. Subsequently different approaches to managing missing data will be tested using trial datasets. I aim to provide frameworks for minimising and analysing missing data that will inform trial design, leading to more accurate results and better patient care.

Research activity codes: 7.2 END OF LIFE CARE;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Data Science
Funder: Department of Health and Social Care

Abstract: Our research will drive innovation across six work streams: 1) Ethical framework for using clinical data for learning and research 2) Standards, data safety and interoperability 3) Hardware/software 4) Data analytics to stratify NHS patients 5) Clinical reporting, feedback and evaluation 6) Education and dissemination We will deliver: Short-term  An N3 compliant integrated BRC data repository  Categorised, cleaned and catalogued theme-related NHS and research data for transfer into the BRC data warehouse  BRC data warehouse population with complex and linkable NHS and research data, within an ethical framework Medium-term  An NHS Learning Health System to identify insights using novel data analysis methods, translating these into improved medical care for exemplar conditions  Trans-disciplinary research methods to link clinical with laboratory data, and develop theme-specific phenomarkers  Processes to iteratively interrogate patient data, refining phenomarkers with potential to influence clinical decisions  Cross-comparison of phenomarker signatures across research themes, defining common etiologies/co-morbidities Long-term  Implementation across other therapeutic areas/BRCs/Trusts  Routine integration of high quality, high dimension data (e.g. imaging/genomics) into the N3 environment, for robust incorporation into personalised clinical practice  Assessment of the clinical benefits and resource savings facilitated by a UK Learning Health System  Educational materials raising awareness among health professionals of the centrality of high quality data collection, analysis and interpretation for personalised medicine.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY);4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Clinical Research Facility
Funder: Department of Health and Social Care

Abstract: Short term:  Continue to maintain the highest standards of research quality/governance within our CRF through a successful accreditation as a Phase 1 Unit by the Medicines and Healthcare Regulatory Authority to allow the CRF to become a recognised Centre of Excellence for early phase trials. An application was submitted in May 2016; the MHRA have confirmed an accreditation inspection in Quarter 3 2016;  CRF activity, complexity of studies undertaken and our capacity to undertake imaging and interventional cardiology studies has increased significantly since 2012. The CRF will develop the capacity to deliver an expanding research portfolio; this will be undertaken through continuing to support externally funded research and income generation from industry studies. Medium term:  A strategy for business development and marketing the CRF, aligned to that for our Research Platforms, will be developed to promote and maximise income to support state of the art equipment and technical expertise; this may include opportunities for income generation through collaboration with spin out companies based within the BRC Commercialisation & Innovation Hub;  Create a culture where every patient participates in research to increase the quality of clinical care and accelerate health care benefits; maximize the patient experience; and engage the wider community promoting research for prevention as well as treatment. Long term:  The CRF will aim to become largely self sustaining through the generation of income via grant-funding and the provision of service to academic partners and commercial organisations.


Research activity codes: 6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Hearing Health
Funder: Department of Health and Social Care

Abstract: We will bring about a step change in hearing health within GM using experimental medicine and technological innovation, and by engaging with newly integrated health and social care partners and industrial collaborators. Judicious project selection will benefit the whole population through the following three objectives:  Develop approaches for prevention and treatment of hearing problems in addition to traditional emphasis on diagnosis;  Serve the whole population specifically children, adolescents, adults and older persons;  Pioneer technology by exploitation of our leading hearing science and cross-cutting translation infrastructure. The Hearing Health team will become an internationally acclaimed translational research powerhouse, delivering a strategy based on: (i) prevention and prediction, (ii) precision and personalisation, (iii) patient-public participation, and (iv) partnerships. Each project has its own specific time-bound objectives with measurable outcomes summarised and aggregated as follows: Short-term (1-2 years)  Build teams and infrastructure; initiate projects;  Develop enabling technologies for prevention, diagnosis, and treatment;  Launch unique Hearing Device Research Centre;  Develop strategy for funding sustainability;  Establish ‘Hear Today and Tomorrow’ campaign, website and volunteer database;  Increase social media presence based on Altmetrics. Medium-term (3-4 years)  Secure external funding to sustain each programme;  Completion of 15 Phase I/II trials;  Annual review of new discoveries and areas for disinvestment. Long-term (5+ years)  Translate 6 projects into industrial products;  Initiate 6 phase III trials building on each research programme’s success;  Working with professional bodies and national commissioning framework to implement ≥3 new technologies;  Ensuring that ≥50% higher training dissertations build on implementation science from MBRC's developments.


Research activity codes: 3.5 RESOURCES AND INFRASTRUCTURE (PREVENTION);5.9 RESOURCES AND INFRASTRUCTURE (TREATMENT DEVELOPMENT);6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Clinical Informatics & Big Data
Funder: Department of Health and Social Care

Abstract: This Cross-Cutting Theme will provide core expertise in secure and controlled acquisition, integration, and analysis of large, heterogeneous biomedical and clinical data for translational research. The work will synergise across multiple other BRC Themes, and will achieve additional impact and value-for-money through collaborations locally, nationally, and internationally. Aim 1: Clinical Informatics Develop and deploy secure, interoperable methods, tools, and systems for acquisition, integration, and sharing of health data (including EHR, patient-reports, sensors, imaging, ‘omics, and other biological data), and for two-way information exchange between academic and NHS environments. Aim 2: Information Governance Develop, deliver and evaluate an ethically robust governance framework (with appropriate informatics tools and services) for capture, application and management of consents; information sharing; and transparency within and across organizations. Aim 3: Big Data analytics Develop methods for analysis of large, heterogeneous biomedical datasets, including scalable methods for evaluating phenotype and joint analysis of high-dimensional, longitudinal biological and clinical data. Objectives Short-term: establish clinical informatics systems, information governance framework, computational infrastructure, and data discovery tools to enable analytical researchers to develop and apply algorithms to selected BRC datasets (e.g. Cancer, Gastroenterology & Mucosal Immunity). Medium-term: roll-out clinical informatics and information governance tools across the AHSN and other BRCs; develop a framework for benchmarking and validating digital phenotyping algorithms; extend analytic approaches with other themes. Long-term: demonstrate and evaluate large-scale translational research activities across networked partners; evaluate methods for describing the latent structure of underlying health trajectories and the influence of factors (genetic, environmental) on disease progression.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Technology & Digital Health
Funder: Department of Health and Social Care

Abstract: The aim of our research is to improve patient outcomes in the hospital and in the community through technology and algorithms. The combination of machinelearning algorithms with emerging digital technologies such as wearable or non-contact sensors, smartphones and computer tablets, gives us an unprecedented opportunity to provide individualised cost-efficient patient care. We aim to move to personalised monitoring and alerting, regardless of where care is being provided. Devices and algorithms will be advanced from initial feasibility studies (in multiple themes) to clinical trials, adoption and commercialisation. • Short-term: - Development and evaluation of novel wearable and non-contact sensing, initially in the controlled environment of a hospital ward, prior to community deployment (medium-term). Retrospective analysis of existing database of vital-sign observations to develop population-specific early warning scores to improve specificity and enable earlier identification of deterioration in hospital patients. Development of machine-learning algorithms to predict the short-term risk of acute exacerbation for people with chronic disease, with the addition of plug-and-play wearable sensors when escalation of care is required. • Medium-term: -Development of a virtual High-Dependency Unit based on our integrated monitoring and alerting systems. Evaluation of “virtual wards” with ambulatory acute care to support early discharge to the community. Effectiveness trial of digital health platforms to support individualised self-management and pro-active intervention strategies, with the objective of reducing hospital admissions. • Long-term: - Randomised Controlled Trials where appropriate, and integration into clinical pathways; collection of health economic data. Large-scale adoption of patient management agnostic to their location.


Research activity codes: 7.4 RESOURCES AND INFRASTRUCTURE (DISEASE MANAGEMENT)
Health categories: GENERIC HEALTH RELEVANCE
Title: Surgical Innovation
Funder: Department of Health and Social Care

Abstract: Our aim is to transform early phase study design and practice in surgery. New methods for safe and transparent translation of innovation will expedite rejection of ineffective techniques and inform efficient trial design and evidence-based surgical practice. Short term S1. Determine meaningful and practical definitions for stages of innovation of surgical interventions (Workstream 1 (WS1)). S2. Understand surgeon practice of information provision when offering patients novel and evolving procedures (WS2). S3. Identify, interrogate and categorise benefit and harm outcomes in early phase surgical studies (WS3). S4. Adapt and modify evidence synthesis methods using surgical case studies to inform network meta-analyses (WS4). Medium term M1. Pilot innovation definitions in case studies in interventions at different phases of early development (WS1). M2. Develop ‘Core Information Sets’ for use in consultations where surgical innovation is discussed, and test their feasibility and acceptability (WS2). M3. Develop ‘Core Outcome Sets’: minimal mandated sets of outcomes to measure within each type of early phase surgical study. Identify and test measures to assess outcomes (WS3). M4. Use network meta-analysis to map novel interventions onto target outcomes and identify active interventions (WS4). Long term L1. Produce and disseminate guidance for transition between early and later phase study designs (WS1). L2. Design early phase studies with nested methodological research to evaluate the Core Information Sets (WS2). L3. Implement ‘Core Outcome Sets’ in early phase studies and develop outcome reporting guidelines (WS3). L4. Design early phase studies evaluating active interventions identified in the network meta-analyses (WS4).


Research activity codes: 5.9 RESOURCES AND INFRASTRUCTURE (TREATMENT DEVELOPMENT);6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Translational population science
Funder: Department of Health and Social Care

Abstract: We aim to collaborate with the Research Themes to incorporate novel biomarker measurements and causal analyses into their translational activities, support data interpretation using bioinformatics tools, and provide expertise on sample handling, storage and management. Short term S1. Establish a Translational Population Science team combining infrastructural, methodological and applied expertise. S2. Support Research Themes on all aspects of biosample collection and management (workstream 1 [WS1]). S3. Advise on and execute biomarker assays including DNA methylation quantification, metabolomic profiling and genomic profiling (WS2). S4. Provide training in use of our Mendelian randomization software (MR-Base) to predict on-target and off-target effects of therapeutic interventions (WS3). S5. Provide support in application and interpretation of bioinformatics tools to enhance exploratory work on novel targets (WS3). Medium term M1. Maintain support to Research Themes in biosample collection, governance and management (WS1). M2. Identify causal modifiable (especially metabolomic) factors related to disease progression (WS2&3). M3. Combine evidence from MR studies and pilot RCTs to provide interim evidence regarding specific therapeutics (WS3). M4. Use MR and other causal analysis methods on major international studies to estimate causal effects of modifiable risk factors on a range of health outcomes (WS3). Long term L1. Develop individual or panels of biomarkers for clinical application in a range of contexts (WS2). L2. Establish methods for combining MR studies and RCT data to provide robust preliminary data regarding pharmacotherapeutic, nutritional or lifestyle modification that can be used before definitive RCT data are available (WS3).


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Surgery and Technology
Funder: Department of Health and Social Care

Abstract: Our aim is to improve the quality and safety of the surgical care pathway through technological innovations using our strong track-record of interdisciplinary research. We will translate innovations, in a safe and cost-effective manner and with significant health and economic impact, through three inter-related programmes:  Surgical Robotics and Allied Technologies  Diagnostics and Sensing  Translation and Service Delivery Short-term objectives  Develop and validate novel concepts for cost-effective robotic systems and smart devices, and customisable imaging and navigation tools (biophotonics/confocal microscopy/mass spectrometry), to further reduce the trauma of access and the invasiveness of surgery: o First-in-human studies of Cyclops o First-in-human validation of Micro-IGES  Proof-of-concept for a patient-specific robot-assisted navigation and imaging platform for catheter–based vascular robotics: o Conduct in vivo testing of Rapid Evaporative Ionisation Mass (REIMS/i-Knife) to detect metabonomic differences between tissue types for real-time intraoperative diagnosis o Proof-of-concept for integrating digital technology, design-led innovation and analytics (machine learning/artificial intelligence) to support decisionmaking Medium-term objectives  Intraoperative validation of (REIMS/i-Knife) towards a multicentre RCT comparing REIMS-guided versus conventional breast conserving surgery  Develop an advanced digital technology platform, with industrial partners (Google Deepmind), integrating system learning and artificial intelligence as a clinical and patient decision support tool  Develop longitudinal metabolic phenotyping of patients undergoing surgery for better therapeutic targeting and outcome prediction (with Molecular Phenomics) Long-term objectives  Develop wearable and implantable multimodal bio-sensing technologies for precision monitoring of surgical recovery to improve patient outcomes  Develop evidence-based innovation policy frameworks to guide safe translation, evaluation, and diffusion for sustainability and impact for patient benefit


Research activity codes: 5.9 RESOURCES AND INFRASTRUCTURE (TREATMENT DEVELOPMENT);6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Healthcare Informatics, Genomics/Omics, Data Science
Funder: Department of Health and Social Care

Abstract: Aim: Deliver patient centred analytics across a wide-range of clinical, omics, imaging and wearable data, thereby rapidly improving the precision, quality, outcomes, and efficiency of healthcare, increasing patient autonomy, and accelerating drug discovery. Objectives: (Sub-themes)  a patient-centered UCLH Informatics Platform and Analytics Hub as a sustainable, scalable research platform in experimental/translational medicine.  an efficient, sustainable pipeline for data-driven (omic) drug-target identification/validation and approaches to precision medicine.  a sustainable infrastructure for in-hospital EHR-driven trials with optimized design in a precision medicine framework (e.g. outcomes, recruitment) and point-of-care randomization.  an informatics capability for deploying and evaluating actionable analytics for decision support systems (DSS) for patient benefit at the individual and the systems level. Phased deliverables across objectives: Short – Establish NHS Clinical Research Informatics Department; establish consent models; build capacity for EHR (security, linkage and NLP), omics (bioinformatics pipelines, pathways, machine learning; integrate EHR-omic data on 10k), wearables (embedded analytics, anomaly detection); establish framework for deploying hundreds of clinically-driven rule-based safety alarms; Medium – Accelerate omics-EHR recruitment to 50k patients; deliver 3 actionable precision medicine research findings (genetics of ADRs, blood-based treatment response biomarkers); genomic validation of repurposing opportunities (52 targets) identified by DTAdb platform; deploy >10 rule-based DSS across clinical themes; integrate wearables into emerging observational/interventional studies (sleep monitoring in dementia, GPS tracking for viral transmission pattern); Long – Enrich/scale omics-EHR and DSS by integrating diverse data (genomic variants, imaging, text, wearables) and algorithms (knowledge-representation, reasoning); deploy >5 research-driven DSS across acute themes; undertake >5 informatics-driven trials informed by themes.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY);4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: GENERIC HEALTH RELEVANCE
Title: NIHR Sheffield Clinical Research Facility
Funder: Department of Health and Social Care

Abstract: Aims: 1. Further improve the quality and efficiency of study delivery. 2. Extend the range support services provide for experimental medicine (EM). 3. Progress the central role of the CRF in research capacity building, education and training. Objectives: Short term (1-2 years): 1. Conduct review of clinical and research skills required of CRF staff to deliver BRC EM projects (beginning Autumn 2016 in readiness for BRC initiation). 2. Develop and deliver a BRC/CRF training programme for existing and new CRF clinical staff to address any identified skills gaps in relation to BRC research programme. 3. Execute consultancy partnership agreement with Boyds (http://www.boydconsultants.com) to access advice related to early phase therapeutic development and device testing (see below). 4. Support delivery of at least one BRC EM protocol per neuroscience sub-theme. Medium term (2-4 years): 1. Develop protocols with BRC to establish and extend existing patient cohorts with neurological conditions, including detailed clinical phenotyping, sample collection and storage (eg blood/skin biopsies/cerebrospinal fluid), and database management. 2. Support BRC investigators in the design, set up and delivery of externally funded EM/early phase studies arising from BRC initiated pilot projects. 3. Play central role in education and training programme for clinical, administrative and allied health professionals in neurosciences clinical research. Long term (5+ years): 1. Progress BRC EM/early phase studies to later phases with external programme funding. 2. Capture clinical observations from BRC EM trials that may inform new hypothesis driven BRC research (bench-to-bedside-to-bench). 3. Robust pipeline, skills and capabilities established to support BRC renewal.


Research activity codes: 6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE