NIHRDataInsights/HRCSData huggingface dataset:
Award Title: HCD: Synthesis of networks of evidence on test accuracy, with and without a 'gold standard'
Funder: Medical Research Council

Abstract: Overall objective: We will develop general methodology to synthesise networks of evidence on the sensitivity and specificity of tests for any given disease. The approach will be applicable across clinical areas and will simultaneously produce pooled estimates of singular, joint and comparative accuracy, allowing for imperfections in 'reference standard' tests where appropriate. The  framework will accommodate data of varied types, e.g. studies reporting results on (i) Test A vs a Gold Standard (GS) test; (ii) A vs B vs GS; (iii) A vs B without a GS; (iv) A vs GS and B vs GS reported separately; (v) B vs C vs GS etc. We will demonstrate how the assumption that any one of the tests in the network is a GS can be relaxed. Why are these estimates needed? Estimates of sensitivity and specificity, informed by systematic reviews and meta-analyses, are crucial for decision-making about which test and/or combination of tests to use for any given disease. For example, these are key parameters in decision models used by NICE to compare the effectiveness and cost-effectiveness of testing strategies. Why are new methods needed? Standard methods (i) cannot produce estimates of the accuracy of tests used in combination, accounting for likely dependencies between them; (ii) produce unnecessarily imprecise estimates of comparative accuracy, (iii) are critically reliant on the assumption that a GS test exists and has been applied in all studies. Proposed advanced methods are not yet fit for general use: for example, involving estimation of infeasible numbers of parameters. Methods: We will work within a Bayesian multi-parameter evidence synthesis framework, allowing data of varied types to be synthesised together through specification of the relationships between parameters. We will draw on - and extend - existing methods for meta-analysis of comparative test accuracy, and latent class models that have been proposed for estimation of accuracy in the absence of a GS.


Research Activity Codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: GENERIC HEALTH RELEVANCE
Award Title: HCD: TEST - Test Evaluation Using Structured Tools
Funder: Medical Research Council

Abstract: New test technologies improve patient health by changing how, when, where and in who tests are used. Current approaches to test evaluation are dominated by test accuracy studies and often ignore impacts created in other ways. We will develop tools to assist researchers and reviewers identify how a test will impact on patients, the study designs and outcomes to measure, and methods for sample size calculation. WP1 will establish a cohort of test evaluations from reports published by HTA organisations (including NICE and WHO) and summarise the evaluation approaches used. WP2 will assess the ways new tests improve or are detrimental to patient health in a sample of test evaluations from this cohort, considering: patterns in test impact related to technology and condition; how evidence reviews consider the ways tests create impact; and where available, the magnitude of effects observed. We will select a sample representative of test technologies and health conditions, sampling 50 to 100 test evaluations until saturation of types of impact. Each test technology will be researched in depth to understand its potential to impact on patients. WP3 will use a subset of test evaluations from WP2, chosen to cover the ways tests impact on patient health. For each route we will consider alternative study designs and outcomes that measure impact, and consider trade-offs between feasibility, validity and efficiency. WP4 will identify and develop sample size calculations for each study design and outcome considered in WP3 and validate their performance in simulation studies.  We will investigate and develop approaches for time-to-event outcomes (such as time to diagnosis/treatment) and for outcomes that only apply in subgroups conditioned on disease state and test result. Project outputs will include guidance, checklists and software tools to improve the process of evidence review and generation, allowing the most clinically effective and cost effective tests to be identified.


Research Activity Codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: GENERIC HEALTH RELEVANCE
Award Title: HCS: Case study, Context and Complex interventions (TRIPLE C) - development of guidance and publication standards to support case study-based research
Funder: Medical Research Council

Abstract: There is increasing recognition that the conventional scientific quest for certainty, predictability and linear causality (often via RCTs) needs to be augmented by the study of how we can best deal with uncertainty, unpredictability and emergent causality. Case study research offers a vital resource for such research, and for those keen to develop methods that support meaningful evaluation of complex interventions in health care.  The literature on case study methodology is diverse, drawing on varied disciplines and epistemologies. It has yet to be formally evaluated in ways that allow researchers, funders and decision makers to better understand the interaction between context and the implementation of complex system-level interventions over time. This study asks: What are the key steps in producing case study research into the influences of context on complex system-level interventions that advances knowledge and informs policy and practice? How might 'high' and 'low' quality in such case studies be defined and assessed? And, how can such case study research best be reported in order to maximise value? It uses meta-narrative review to collate and summarise diverse literature on case study methods, including the range of theoretical and methodological approaches to studying contextual influences; combined with an online Delphi survey with an international and interdisciplinary panel of up to 30 case study experts from academia, policy and practice. The output will be draft methodological guidance and publication standards for case study in health system and public health research. This will be piloted with 3-5 research teams that have published exemplar (i.e. highly-cited) case studies, and with a panel of 10 external experts, to consider the extent to which the range of case study methods have been adhered to by real-world case studies and refine guidance. Final guidance/standards will be disseminated via social media, journals and a knowledge sharing event.


Research Activity Codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health Categories: GENERIC HEALTH RELEVANCE
Award Title: HDHL: A precision nutri-epigenetic approach to tackle the mother-to-child transmission of impaired glucose metabolism
Funder: Medical Research Council

Abstract: PREcisE will operate the overarching hypothesis that the transgenerational association between maternal gestational diabetes and risk of T2D and glycaemic adversity in the next generation(s) can be partially explained by a causal association mediated by durable epigenetic modification of 'maternal glucose responsive (MGR)' loci. The hypothesis is that: - The risk is causal, dose dependent and interplay with maternal psycho-social factors; - The risk is mediated and/or modulated by the glycaemic load and/or the inflammatory score of the diet; -  The risk is causally associated with the regulation of genes in the major metabolic tissues (i.e. liver, skeletal muscles, adipose tissues); - There are causal pathways linking the alteration of MGR loci and glycaemic health in the offspring, part of which is mediated through early growth. PREcisE project harnesses very large data prospectively collected from Spain, Germany, Finland, France and The Netherlands, including randomised control trial and combines with long lasting collaboration with the pregnancy and childhood (PACE), the early growth (EGG) and recent Global MethQTL consortia, to establish the mother-to-offspring transmission of glycaemic health through robust epigenetic factors. The consortium will reach sufficient power to perform its research through the use of data and biological atlas collected prospectively by its PIs.


Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health Categories: METABOLIC AND ENDOCRINE
Award Title: HOD2: Toward Holistic Approaches to Clinical Prediction of Multi-Morbidity: A Dynamic Synergy of Inter-Connected Risk Models
Funder: Medical Research Council

Abstract: Clinical prediction models (CPMs) are models that are used to predict the risk of clinically relevant outcomes in individuals with a particular disease or health condition. They are increasingly used to support clinical decisions, yet they seldom reflect the interplay between developing multiple comorbidities. Specifically, CPMs operate in silos of disease risk, where different (and independent) models are derived to predict the risk of a single adverse clinical outcome. Current approaches to predicting multi-morbidity through CPMs incorrectly assume that the probability of patients developing multi-morbidity is the product of the risks from individual models. Our proposed solution is to advance multi-state modelling, to develop a "CPM-Network" that can appropriately predict the risk of different (but related) clinical outcomes, with the associated benefits to patient care, healthcare planning and service utilisation. Multi-state survival models present a way of predicting risk of a patient moving between different 'states'. While such multi-state CPMs are appealing since they account for competing risks and can predict risks of different clinical events within a given disease area, they do not scale to model the risks of different clinical events across disease areas, in a cumulative sense. As we move from a single event (i.e. two-state) CPM, to a multi-disease (i.e. multi-state) CPM, we find combinatorial complexities that cannot be overcome in the existing multi-state modelling framework. As such, this proposal will extend competing risk and multi-state techniques, to allow development of CPMs that reflect a realistic and holistic view of a patient's health and care. Our research question is: what is the feasibility and potential benefits (compared with existing approaches to predict multi-morbidity) of developing a CPM-Network? We will address methodological challenges in this space such as computational complexity, model overfitting and model validation.


Research Activity Codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: GENERIC HEALTH RELEVANCE
Award Title: Haematopoietic stem cell fate and bone marrow microenvironment changes in severe infection: cellular and molecular mechanisms
Funder: Wellcome Trust

Abstract: This work has two main goals: to identify the mechanisms leading to the degeneration of haematopoietic stem cell (HSC) niches during severe infections, and to measure and improve stem cell recovery post-infection. I will use a murine model of severe malaria, infection by Plasmodium berghei, which I know leads to loss of HSC function and dramatic changes to the bone marrow microenvironment. I will take advantage of my well-established multidisciplinary research approach, combining advanced microscopy, quantitative analyses, molecular manipulations and validation through human samples, to investigate the cellular and molecular mechanisms leading to HSC niche damage, the extent of regeneration following treatment and the consequences of repeated infection/treatment cycles. Finally, I will test molecular interventions that may preserve the HSC niche and HSC function, uncoupling HSC damage from a still active immune response.

Lay Abstract: The spread of antimicrobial resistance is leading us towards a future where severe infections will not all be easily treatable with traditional medicines, therefore the human population will experience more and more severe infections overall. It is important that we develop new therapies, maximising our immune response but curbing deleterious effects on the rest of the body. Until now, cells of the immune system have been the most studied because they are the front-line soldiers dealing with pathogens directly. However, we now know that our entire blood production system switches gear in response to infection, and the stem cells that maintain it are put under great strain. Here we will use advanced microscopy and other technologies to look inside the bone of mice affected by malaria to understand how blood stem cells are affected by the disease and how we can protect them.

Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health Categories: BLOOD;INFECTION;INFLAMMATORY AND IMMUNE SYSTEM
Award Title: Haemostasis in critically ill patients with advanced chronic liver disease
Funder: Medical Research Council

Abstract: Better tests to evaluate haemostasis in patients with chronic liver disease (CLD) are required to reassure clinicians when blood component support is not required and to guide transfusion in the event of bleeding. I am acutely aware of this both as a clinician specialising in thrombosis and haemostasis at one of the largest regional liver centres in Europe, and also as a working group member for the ISTH on 'Haemostasis in patients with liver disease'.   Aim: to identify novel methods to assess haemostasis in critically ill patients with CLD, and determine their association with bleeding and thrombosis.  Objective 1:To evaluate the clinical utility of thrombin generation (TG) and thromboelastography (TEG) in critically ill patients with CLD in reducing blood component use   Objective 2: To identify clinical and laboratory risk factors (including TG, TEG, clot lysis time [CLT] and neutrophil extracellular traps [NETs]) for bleeding and thrombosis  I will set up a prospective observational multicentre cohort study to recruit 250 patients with CLD admitted to critical care, and 30 healthy controls. Blood samples and clinical data will be collected on admission, day 1 and day 5 with clinical outcomes recorded up to day 28 (or liver transplant, discharge or death, whichever is sooner). Major bleeding will be defined by the HEME tool and thrombosis will require confirmation with objective imaging. Thrombin generation will be measured in both plasma and whole blood, along with TEG, CLT and NETs. We will establish the proportion of patients with normal haemostatic profiles on admission, the stability of profile over the initial 5 days of admission and the effect of blood components transfused on haemostatic profile. Associations between clinical risk factors, admission TG and TEG parameters with later bleeding/thrombosis will be evaluated, as will the relationship between markers of NETs and aetiology of CLD, presence of sepsis on admission and later thrombosis.


Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health Categories: ORAL AND GASTROINTESTINAL;BLOOD;CARDIOVASCULAR
Award Title: Hamlet.rt: multi-centre expansion
Funder: Cancer Research UK

Abstract: Background
Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy (Hamlet.rt) is a technology-enabled prospective clinical study aiming to build predictive radiomic models for individualisation of radiotherapy in Head & Neck, Prostate, Brain and Lung cancers. It was established as a single centre study in Cambridge in October 2019. The study concept arose from a patient focus group, and aims to answer a key question: 
"Can machine learning models that incorporate large, longitudinal imaging, clinical and patient-reported data, predict radiotherapy toxicity much better than current simple clinical review of individual patient’s radiotherapy plans?"
This application relates to a multi-centre expansion of the original prospective clinical study.

Aims
This application aims to extend the original study to 10 additional radiotherapy centres, in order to increase the amount of available patient data to over 2,000 patients, enhance the diversity of patients included in the study, and to establish a robust method for cross-validation of predictive models. In addition, the expansion of the study supports the development of complementary translational sub-studies to evaluate tumour response to radiotherapy.

Methods
The study will utilise image analysis, feature extraction and machine-learning based radiomic analysis of radiotherapy image datasets to establish image features that correlate with treatment outcome. Outcome data is captured through the use of serial electronic patient reported outcome measures completed by patients during radiotherapy and every 3 months for up to 2 years post completion of radiotherapy. For brain tumour patients, diffusion tensor magnetic resonance imaging (DT-MRI) will also be captured in order to build image based models of white matter connectivity, in order perform spatial correlation of radiation effects with assessment of neurocognitive function.

How the results of this research will be used
The results of the project will be used in two ways. The first is to deploy machine learning models that can assist clinicians in identifying patients at increased risk of treatment toxicity from radiation therapy into the clinic, assisting clinicians to individualise treatment for patients, or engage in early interventions to monitor and treat treatment toxicity. The second is to provide biosamples and high quality clinical outcome data for a series of subsequent translational research studies, that will correlate tumour biology with therapy response.


Research Activity Codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: CANCER AND NEOPLASMS
Award Title: Harnessing genetic information in the SDRNT1BIO to understand Type 1 diabetes and its complications
Funder: Diabetes UK

Abstract: This proposal is for costs to support an analysis of genome wide single nucleotide polymorphism (SNP) data in a large cohort  of people with Type 1 Diabetes (T1DM), and controls so as to:

To discover new  genetic loci associated  with type 1 diabetes  including adult onset T1DM
To discover new  genetic loci associated with  diabetes complications  and other relevant phenotypes in T1DM
To investigate mechanisms through which genotypic hits for T1DM or complications influence disease risk
With DUK support we recently enrolled 6152 people with type 1 diabetes, stored biosamples, and linked their study data to their electronic health care records (The Scottish Diabetes Research Network Type 1 Bioresource-SDRNT1BIO).   The JDRF recently supported the large scale genotyping of this cohort using spare lab and consumables capacity in an  initiative on genetics of nephropathy.  Only genotyping costs were covered.  This application is a request for support for two post-doctoral scientists to now utilise these genetic data.


Research Activity Codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health Categories: METABOLIC AND ENDOCRINE
Award Title: High sensitivity multiplex testing with a novel cardiac biomarker for rapid triage of acute chest pain patients enabling healthcare efficiencies and improved patient outcomes
Funder: Innovate UK

Abstract: Acute myocardial infarctions (AMI/heart attacks) are one of the most common causes of death throughout the developed world. Each year ~720,000 UK citizens attend an emergency department (ED) with acute chest pain (ACP), representing ~6% of ED visits. Whilst the majority will not be suffering an AMI, due to the life-threatening nature of these symptoms, all patients must be monitored until successfully triaged. This places a huge strain on the NHS.



The Gold Standard for ACP triage relies on assessment of clinical symptoms, electrocardiogram, and elevation/changes in troponin (cTn) biomarker levels (a marker of myocardial cell-death). Since most AMI patients display negative ECG symptoms, biomarkers play a crucial role ACP triage. However, cTn takes time to reach reliable measurable levels. This limits its usefulness for those presenting with early symptoms onset and requires cTn-levels to be monitored over time (a diagnostic pathway). Furthermore, cTn requires high-sensitivity testing (hs-cTn) undertaken in the hospital laboratory. These tests are slow restricting use to long (3-hr) diagnostic pathways. This results in an extended length-of-patient-stay, impacting on patient outcomes/experience and ED efficiency/productivity. Whilst point-of-care diagnostic devices are available, none can deliver reliable high-sensitivity testing.



'Cardiac-Myosin-binding-protein-C' (cMyC) is an emerging myocardial necrosis biomarker. Unlike cTn, cMyC reaches elevated levels in the blood soon after AMI onset, improving diagnostic accuracy and enabling use with early presenters. However, cMyC levels fall equally as fast with time raising reliability concerns for late presenters. There are also barriers to clinical take-up, requiring clinicians to move from the trusted Gold Standard of cTn, or pay for additional testing.



Osler seek to overcome these challenges through the application of a novel point-of-care diagnostic platform for the cost-efficient and reliable, simultaneously multiplexed, high-sensitivity testing of both cMyC and hs-cTn biomarkers.


Research Activity Codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: CARDIOVASCULAR