Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.

(1)H MRS thermometry has been investigated for brain trauma and hypothermia monitoring applications but has not been explored in brain tumours. The proton resonance frequency (PRF) of water is dependent on temperature but is also influenced by microenvironment factors, such as fast proton exchange with macromolecules, ionic concentration and magnetic susceptibility. (1)H MRS has been utilized for brain tumour diagnostic and prognostic purposes in children; however, the water PRF measure may provide complementary information to further improve characterization. Water PRF values were investigated from a repository of MRS data acquired from childhood brain tumours and children with apparently normal brains. The cohort consisted of histologically proven glioma (22), medulloblastoma (19) and control groups (28, MRS in both the basal ganglia and parietal white matter regions). All data were acquired at 1.5 T using a short TE (30 ms) single voxel spectroscopy (PRESS) protocol. Water PRF values were calculated using methyl creatine and total choline. Spectral peak amplitude weighted averaging was used to improve the accuracy of the measurements. Mean PRF values were significantly larger for medulloblastoma compared with glioma, with a difference in the means of 0.0147 ppm (p < 0.05), while the mean PRF for glioma was significantly lower than for the healthy cohort, with a difference in the means of 0.0061 ppm (p < 0.05). This would suggest the apparent temperature of the glioma group was ~1.5 °C higher than the medulloblastomas and ~0.7 °C higher than a healthy brain. However, the PRF shift may not reflect a change in temperature, given that alterations in protein content, microstructure and ionic concentration contribute to PRF shifts. Measurement of these effects could also be used as a supplementary biomarker, and further investigation is required. This study has shown that the water PRF value has the potential to be used for characterizing childhood brain tumours, which has not been reported previously.

3T magnetic resonance scanners have boosted clinical application of 1 H-MR spectroscopy (MRS) by offering an improved signal-to-noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi-center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors.

Information flow between the thalamus and cerebral cortex is a crucial component of adaptive brain function, but the details of thalamocortical interactions in human subjects remain unclear. The principal aim of this study was to evaluate the agreement between functional thalamic network patterns, derived using seed-based connectivity analysis and independent component analysis (ICA) applied separately to resting state functional MRI (fMRI) data from 21 healthy participants. For the seed-based analysis, functional thalamic parcellation was achieved by computing functional connectivity (FC) between thalamic voxels and a set of pre-defined cortical regions. Thalamus-constrained ICA provided an alternative parcellation. Both FC analyses demonstrated plausible and comparable group-level thalamic subdivisions, in agreement with previous work. Quantitative assessment of the spatial overlap between FC thalamic segmentations, and comparison of each to a histological "gold-standard" thalamic atlas and a structurally-defined thalamic atlas, highlighted variations between them and, most notably, differences with both histological and structural results. Whilst deeper understanding of thalamocortical connectivity rests upon identification of features common to multiple non-invasive neuroimaging techniques (e.g. FC, structural connectivity and anatomical localisation of individual-specific nuclei), this work sheds further light on the functional organisation of the thalamus and the varying sensitivities of complementary analyses to resolve it.

The transition from wakefulness into sleep is accompanied by modified activity in the brain's thalamocortical network. Sleep-related decreases in thalamocortical functional connectivity (FC) have previously been reported, but the extent to which these changes differ between thalamocortical pathways, and patterns of intra-thalamic FC during sleep remain untested. To non-invasively investigate thalamocortical and intra-thalamic FC as a function of sleep stage we recorded simultaneous EEG-fMRI data in 13 healthy participants during their descent into light sleep. Visual scoring of EEG data permitted sleep staging. We derived a functional thalamic parcellation during wakefulness by computing seed-based FC, measured between thalamic voxels and a set of pre-defined cortical regions. Sleep differentially affected FC between these distinct thalamic subdivisions and their associated cortical projections, with significant increases in FC during sleep restricted to sensorimotor connections. In contrast, intra-thalamic FC, both within and between functional thalamic subdivisions, showed significant increases with advancement into sleep. This work demonstrates the complexity and state-specific nature of functional thalamic relationships--both with the cortex and internally--over the sleep/wake cycle, and further highlights the importance of a thalamocortical focus in the study of sleep mechanisms.

Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values.

Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus.

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.

Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice.

Increases in 1H nuclear magnetic resonance spectroscopy (NMR) visible lipids are a well-documented sign of treatment response in cancers. Lipids in cytoplasmic lipid droplets (LDs) are the main contributors to the NMR lipid signals. Two human primitive neuroectodermal tumour cell lines with different sensitivities to cisplatin treatment were studied. Increases in NMR visible saturated and unsaturated lipids in cisplatin treated DAOY cells were associated with the accumulation of LDs prior to DNA fragmentation due to apoptosis. An increase in unsaturated fatty acids (UFAs) was detected in isolated LDs from DAOY cells, in contrast to a slight decrease in UFAs in lipid extracts from whole cells. Oleic acid and linoleic acid were identified as the accumulating UFAs in LDs by heteronuclear single quantum coherence spectroscopy (HSQC). 1H NMR lipids in non-responding PFSK-1 cells were unchanged by exposure to 10 μM cisplatin. These findings support the potential of NMR detectable UFAs to serve as a non-invasive marker of tumour cell response to treatment.

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

Patients with diabetes are at an increased risk of readmission and mortality when discharged from hospital. Existing research identifies statistically significant risk factors that are thought to underpin these outcomes. Increasingly, these risk factors are being used to create risk prediction models, and target risk modifying interventions. These risk factors are typically reported in the literature accompanied by unstandardized effect sizes, which makes comparisons difficult. We demonstrate an assessment of variation between standardised effect sizes for such risk factors across care outcomes and patient cohorts. Such an approach will support development of more rigorous risk stratification tools and better targeting of intervention measures.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR.

COVID-19 placed significant challenges on healthcare systems. People with diabetes are at high risk of severe COVID-19 with poor outcomes. We describe the first reported use of inpatient digital flash glucose monitoring devices in a UK NHS hospital to support management of people with diabetes hospitalized for COVID-19.

The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification 'gold-standard', typically delivered 3-4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS).

Conventional functional connectivity (FC) analysis of fMRI data derives a single measurement from the entire scan, generally several minutes in duration, which neglects the brain's dynamic behaviour and potentially loses important temporal information. Short-interval dynamic FC is an attractive proposition if methodological issues can be resolved and the approach validated. This was addressed in two ways; firstly we assessed FC of the posterior cingulate cortex (PCC) node of the default mode network (DMN) using differing temporal intervals (8s to 5min) in the waking-resting state. We found that 30-second intervals and longer produce spatially similar correlation topography compared to 15-minute static FC measurements, while providing increased temporal information about changes in FC that were consistent across interval lengths. Secondly, we used NREM sleep as a behavioural validation for the use of 30-second temporal intervals due to the known fMRI FC changes with sleep stage that have been observed in previous studies using intervals of several minutes. We found significant decreases in DMN FC with sleep depth which were most pronounced during stage N2 and N3. Additionally, both the proportion of time with strong PCC-DMN connectivity and the variability in dynamic FC decreased with sleep. We therefore show that dynamic FC with epochs as short as tens of seconds is a viable method for characterising intrinsic brain activity.

Teaching of evidence-based medicine (EBM) has become widespread in medical education. Teaching the teachers (TTT) courses address the increased teaching demand and the need to improve effectiveness of EBM teaching. We conducted a systematic review of assessment tools for EBM TTT courses. To summarise and appraise existing assessment methods for teaching the teachers courses in EBM by a systematic review.

The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care.

Research into the ability to coordinate one's movements with external cues has focussed on the use of simple rhythmic, auditory and visual stimuli, or interpersonal coordination with another person. Coordinating movements with a virtual avatar has not been explored, in the context of responses to temporal cues. To determine whether cueing of movements using a virtual avatar is effective, people's ability to accurately coordinate with the stimuli needs to be investigated. Here we focus on temporal cues, as we know from timing studies that visual cues can be difficult to follow in the timing context. Real stepping movements were mapped onto an avatar using motion capture data. Healthy participants were then motion captured whilst stepping in time with the avatar's movements, as viewed through a virtual reality headset. The timing of one of the avatar step cycles was accelerated or decelerated by 15% to create a temporal perturbation, for which participants would need to correct to, in order to remain in time. Step onset times of participants relative to the corresponding step-onsets of the avatar were used to measure the timing errors (asynchronies) between them. Participants completed either a visual-only condition, or auditory-visual with footstep sounds included, at two stepping tempo conditions (Fast: 400ms interval, Slow: 800ms interval). Participants' asynchronies exhibited slow drift in the Visual-Only condition, but became stable in the Auditory-Visual condition. Moreover, we observed a clear corrective response to the phase perturbation in both the fast and slow tempo auditory-visual conditions. We conclude that an avatar's movements can be used to influence a person's own motion, but should include relevant auditory cues congruent with the movement to ensure a suitable level of entrainment is achieved. This approach has applications in physiotherapy, where virtual avatars present an opportunity to provide the guidance to assist patients in adhering to prescribed exercises.