146 research outputs found

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Real-world evidence for the management of blood glucose in the intensive care unit

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    Glycaemic control is a core aspect of patient management in the intensive care unit (ICU). Blood glucose has a well-known U-shaped relationship with mortality and morbidity in ICU patients, with both hypo- and hyper-glycaemia associated with poor patient outcomes. As a result, up to 40-90% of ICU patients receive insulin, depending on illness severity and variation in clinical practice. Generally, clinical guidelines for glycaemic control are based on a series of trials that culminated in the NICE-SUGAR study in 2009, a multicentre study demonstrating that tight glycaemic control (a target of 80-110 mg/dL) did not improve patient outcomes compared to moderate control (<180 mg/dL). However, there remain open questions around the potential for more personalised blood glucose management, which real-world evidence sources such as electronic medical records (EMRs) can play a role in answering. This thesis investigates the role that EMRs can play in glycaemic control in the ICU using open access EMR databases, covering a heterogenous 208 hospital USA based patient cohort (the eICU collaborative research database, eICU-CRD) and a large tertiary medical centre in Boston, USA (MIMIC-III and MIMIC-IV). This thesis covers: i) curation and characterisation of the eICU-CRD cohort as a data resource for real-world evidence in glycaemic control; ii) investigation of whether blood lactate modifies the relationship between blood glucose and patient outcome across different subgroups; and iii) the development and comparison of machine learning and deep learning probabilistic forecasting algorithms for blood glucose. The analysis of the eICU-CRD demonstrated that there is wide variety in clinical practice around glycaemic control in the ICU. The results enable comparison with other data resources and assessment of the suitability of the eICU-CRD for addressing specific research questions related to glycaemic control and nutrition support. Informed by this descriptive analysis, the eICU-CRD was used to examine whether blood lactate modifies the relationship between blood glucose and patient outcome across different subgroups. While adjustment for blood lactate attenuated the relationship between blood glucose and patient outcome, blood glucose remained a marker of poor prognosis. Diabetic status was found to influence this relationship, in line with increasing evidence that diabetics and non-diabetics should be considered distinct populations for the purpose of glycaemic control in the ICU. The forecasting algorithms developed using MIMIC-III and MIMIC-IV were designed to account for the intrinsic statistical difficulties present in EMRs. These include large numbers of potentially sparsely and irregularly measured input variables. The focus was on development of probabilistic approaches given the measurement error in blood glucose measures, and their potential conversion into categorical forecasts if required. Two alternative approaches were proposed. The first was to use gradient boosted tree (GBT) algorithms, along with extensive feature engineering. The second was to use continuous time recurrent neural networks (CTRNNs), which learn their own hidden features and account for irregular measurements through evolving the model hidden state using continuous time dynamics. However, several CTRNN architectures are outperformed by an autoregressive GBT model (Catboost), with only a long short-term memory (LSTM) and neural ODE based architecture (ODE-LSTM) achieving comparable performance on probabilistic forecasting metrics such as the continuous ranked probability score (ODE-LSTM: 0.118±0.001; Catboost: 0.118±0.001), ignorance score (0.152±0.008; 0.149±0.002) and interval score (175±1; 176±1). Further, the GBT method was far easier and faster to train, highlighting the importance of using appropriate non-deep learning benchmarks in the academic literature on novel statistical methodologies for analysis of EMRs. The findings highlight that EMRs are a valuable resource in medical evidence generation and characterisation of current clinical practice. Future research should aim to continue investigation of subgroup differences and utilise the forecasting algorithms as part of broader goals such as development of personalised insulin recommendation algorithms

    Biotechnology to Combat COVID-19

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    This book provides an inclusive and comprehensive discussion of the transmission, science, biology, genome sequencing, diagnostics, and therapeutics of COVID-19. It also discusses public and government health measures and the roles of media as well as the impact of society on the ongoing efforts to combat the global pandemic. It addresses almost every topic that has been studied so far in the research on SARS-CoV-2 to gain insights into the fundamentals of the disease and mitigation strategies. This volume is a useful resource for virologists, epidemiologists, biologists, medical professionals, public health and government professionals, and all global citizens who have endured and battled against the pandemic

    Social, Technological and Health Innovation: Opportunities and Limitations for Social Policy, Health Policy, and Environmental Policy

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    This Research Topic focuses on both strengths and weaknesses of social innovation, technological innovation, and health innovation that are increasingly recognized as crucial concepts related to the formulation of responses to the social, health, and environmental challenges. Goals of this Research Topic: (1) to identify and share the best recent practices and innovations related to social, environmental and health policies; (2) to debate on relevant governance modes, management tools as well as evaluation and impact assessment techniques; (3) to discuss dilemmas in the fields of management, financing, designing, implementing, testing, and maintaining the sustainability of innovative models of delivering social, health and care services; and (4) to recognize and analyze social, technological and health innovation that has emerged or has been scaled-up to respond to crisis situations, for example, a pandemic of the COVID-19 coronavirus disease

    Applying Joint Modelling Regression Approaches in Biomedical Data Science

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    In recent years, the technological revolution is allowing the collection of an enormous amount of data of different types, creating enormously complex databases that require the collaboration of statisticians and clinicians to carry out a biomedical study with guarantees, applying the tools of data science. This requires the development of new statistical techniques. This thesis focuses on joint modelling regression models for multivariate responses. Specifically, we study the cases of two and three continuous outcomes, as well as models for longitudinal and survival data. These techniques are applied in three studies of major epidemiological importance: liver damage and survival in COVID-19 patients, perinatal mental health during the COVID-19 pandemic, and the study of thyroid-related hormones

    Social, Technological and Health Innovation: Opportunities and Limitations for Social Policy, Health Policy, and Environmental Policy

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    This Research Topic focuses on both strengths and weaknesses of social innovation, technological innovation, and health innovation that are increasingly recognized as crucial concepts related to the formulation of responses to the social, health, and environmental challenges. Goals of this Research Topic: (1) to identify and share the best recent practices and innovations related to social, environmental and health policies; (2) to debate on relevant governance modes, management tools as well as evaluation and impact assessment techniques; (3) to discuss dilemmas in the fields of management, financing, designing, implementing, testing, and maintaining the sustainability of innovative models of delivering social, health and care services; and (4) to recognize and analyze social, technological and health innovation that has emerged or has been scaled-up to respond to crisis situations, for example, a pandemic of the COVID-19 coronavirus disease

    Assessing seismic collapse of structures using digital cloning techniques.

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    Rapid, reliable information on earthquake-affected structures' current damage/health conditions and predicting what would happen to these structures under future seismic events play a vital role in accelerating post-event evaluations, leading to optimized on-time decisions. Such rapid and informative post-event evaluations are crucial for earthquake-prone areas, where each earthquake can potentially trigger a series of significant aftershocks, endangering the community's health and wealth by further damaging the already-affected structures. Such reliable post-earthquake evaluations can provide information to decide whether an affected structure is safe to stay in operation, thus saving many lives. Furthermore, they can lead to more optimal recovery plans, thus saving costs and time. The inherent deficiency of visual-based post-earthquake evaluations and the importance of structural health monitoring (SHM) methods and SHM instrumentation have been highlighted within this thesis, using two earthquake-affected structures in New Zealand: 1) the Canterbury Television (CTV) building, Christchurch; 2) the Bank of New Zealand (BNZ) building, Wellington. For the first time, this thesis verifies the theoretically- and experimentally validated hysteresis loop analysis (HLA) SHM method for the real-world instrumented structure of the BNZ building, which was damaged severely due to three earthquakes. Results indicate the HLA-SHM method can accurately estimate elastic stiffness degradation for this reinforced concrete (RC) pinched structure across the three earthquakes, which remained unseen until after the third seismic event. Furthermore, the HLA results help investigate the pinching effects on the BNZ building's seismic response. This thesis introduces a novel digital clone modelling method based on the robust and accurate SHM results delivered by the HLA method for physical parameters of the monitored structure and basis functions predicting the changes of these physical parameters due to future earthquake excitations. Contrary to artificial intelligence (AI) based predictive methods with black-box designs, the proposed predictive method is entirely mechanics-based with an explicitly-understandable design, making them more trusted and explicable to stakeholders engaging in post-earthquake evaluations, such as building owners and insurance firms. The proposed digital clone modelling framework is validated using the BNZ building and an experimental RC test structure damaged severely due to three successive shake-table excitations. In both structures, structural damage intensifies the pinching effects in hysteresis responses. Results show the basis functions identified from the HLA-SHM results for both structures under Event 1 can online estimate structural damage due to subsequent Events 2-3 from the measured structural responses, making them valuable tool for rapid warning systems. Moreover, the digital twins derived for these two structures under Event 1 can successfully predict structural responses and damage under Events 2-3, which can be integrated with the incremental dynamic analysis (IDA) method to assess structural collapse and its financial risks. Furthermore, it enables multi-step IDA to evaluate earthquake series' impacts on structures. Overall, this thesis develops an efficient method for providing reliable information on earthquake-affected structures' current and future status during or immediately after an earthquake, considerably guaranteeing safety. Significant validation is implemented against both experimental and real data of RC structures, which thus clearly indicate the accurate predictive performance of this HLA-based method

    Machine Learning for Diabetes and Mortality Risk Prediction From Electronic Health Records

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    Data science can provide invaluable tools to better exploit healthcare data to improve patient outcomes and increase cost-effectiveness. Today, electronic health records (EHR) systems provide a fascinating array of data that data science applications can use to revolutionise the healthcare industry. Utilising EHR data to improve the early diagnosis of a variety of medical conditions/events is a rapidly developing area that, if successful, can help to improve healthcare services across the board. Specifically, as Type-2 Diabetes Mellitus (T2DM) represents one of the most serious threats to health across the globe, analysing the huge volumes of data provided by EHR systems to investigate approaches for early accurately predicting the onset of T2DM, and medical events such as in-hospital mortality, are two of the most important challenges data science currently faces. The present thesis addresses these challenges by examining the research gaps in the existing literature, pinpointing the un-investigated areas, and proposing a novel machine learning modelling given the difficulties inherent in EHR data. To achieve these aims, the present thesis firstly introduces a unique and large EHR dataset collected from Saudi Arabia. Then we investigate the use of a state-of-the-art machine learning predictive models that exploits this dataset for diabetes diagnosis and the early identification of patients with pre-diabetes by predicting the blood levels of one of the main indicators of diabetes and pre-diabetes: elevated Glycated Haemoglobin (HbA1c) levels. A novel collaborative denoising autoencoder (Col-DAE) framework is adopted to predict the diabetes (high) HbA1c levels. We also employ several machine learning approaches (random forest, logistic regression, support vector machine, and multilayer perceptron) for the identification of patients with pre-diabetes (elevated HbA1c levels). The models employed demonstrate that a patient's risk of diabetes/pre-diabetes can be reliably predicted from EHR records. We then extend this work to include pioneering adoption of recent technologies to investigate the outcomes of the predictive models employed by using recent explainable methods. This work also investigates the effect of using longitudinal data and more of the features available in the EHR systems on the performance and features ranking of the employed machine learning models for predicting elevated HbA1c levels in non-diabetic patients. This work demonstrates that longitudinal data and available EHR features can improve the performance of the machine learning models and can affect the relative order of importance of the features. Secondly, we develop a machine learning model for the early and accurate prediction all in-hospital mortality events for such patients utilising EHR data. This work investigates a novel application of the Stacked Denoising Autoencoder (SDA) to predict in-hospital patient mortality risk. In doing so, we demonstrate how our approach uniquely overcomes the issues associated with imbalanced datasets to which existing solutions are subject. The proposed model –– using clinical patient data on a variety of health conditions and without intensive feature engineering –– is demonstrated to achieve robust and promising results using EHR patient data recorded during the first 24 hours after admission

    Quality of care in incident type 2 diabetes and initial presentation of vascular complications: Prospective cohort study using linked electronic health records from CALIBER research platform

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    Background. Numbers of new cases of type 2 diabetes (T2D) are increasing rapidly. Early and continuing intervention after T2D presentation is crucial for best possible outcomes, ensuring that the existing high burden of T2D will not be aggravated. Identification of patterns of continuous care and predictors for meeting key targets for T2D management can improve quality of care. Glycaemic control is particularly important for primary prevention of vascular complications but its relationship with contemporary cardiovascular diseases (CVDs) has been less explored. More importantly, long-term glycaemic control can be assessed from routine monitoring, potentially providing new insight into T2D management to prevent vascular complications. Linked electronic health records are invaluable data resources for investigating these issues. Objective. To examine the quality of care in an incident T2D cohort through assessment of temporal trends of care, predictors of glycaemic, blood pressure and lipid control, and associations of short-term and long-term glycaemic control with chronic vascular complications. Methods. The data source for studies in this thesis was CALIBER which links electronic health records from primary care, hospitalisation, myocardial infarction and mortality registries. Patients newly diagnosed with T2D between 1998 and 2010 were followed-up until a censoring administrative date or initial occurrence of vascular complications. Trends in receipt of care and attainment of glycaemic, blood pressure and total cholesterol targets were examined. Predictors for meeting the targets were explored using multinomial logistic regressions. Association of early glycaemic control with a range of specific cardiovascular complications were investigated using Cox regressions. A longitudinal metric for glycaemic control was developed by quantifying time spent at target during follow-up and was tested for its association with cardiovascular and microvascular outcomes using mixed logistic regressions. Results. A total of 52,379 incident T2D patients were identified with a median follow-up of over 4 years. Positive trends were observed for blood pressure and total cholesterol control, but not for glycaemic control, whilst attainment of HbA1c and blood pressure targets over time consistently fell short. Older age at diagnosis was an important predictor for meeting the key targets. In 36,149 patients free from prior CVD, early glycaemic and blood pressure control was associated with lower risk for heart failure and peripheral arterial disease, whereas cholesterol control with myocardial infarction and transient ischaemic attack. Shorter duration at glycaemic target was associated with higher risk of major adverse cardiovascular events, cardiovascular death and diabetic retinopathy. Conclusions. This thesis highlights missed opportunities and inequality in T2D care. Both short-term and long-term glycaemic control are important for reducing risk of vascular complications. Limitations and implications of the findings for clinical practice and research were discussed
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