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Additional file 1. Database Search Strings.

This webpage consists of details and documentation regarding the forthcoming QUADAS-AI Quality Assessment Tool

Background: The implementation and efficacy of wearable sensors and alerting systems in acute secondary care have been poorly described.Objectives: to pragmatically test one such system and its influence on clinical outcomes in an acute surgical cohort.Methods: In this pragmatically designed, pre-post implementation trial, participants admitted to the acute surgical unit at our institution were recruited. In the pre-implementation phase (September 2017 to May 2019), the SensiumVitals™ monitoring system, which continuously measures temperature, heart, and respiratory rates, was used for monitoring alongside usual care (intermittent monitoring in accordance with the National Early Warning Score 2 [NEWS 2] protocol) without alerts being generated. In the post-implementation phase (May 2019 to March 2020), alerts were generated when pre-established thresholds for vital parameters were breached, requiring acknowledgement from healthcare staff on provided mobile devices. Hospital length of stay, intensive care use, and 28-days mortality were measured. Balanced cohorts were created with 1:1 ‘optimal’ propensity score logistic regression models.Results: The 1:1 matching method matched the post-implementation group (n = 141) with the same number of subjects from the pre-implementation group (n = 141). The median age of the entire cohort was 52 (range: 18–95) years and the median duration of wearing the sensor was 1.3 (interquartile range: 0.7–2.0) days. The median alert acknowledgement time was 111 (range: 1–2,146) minutes. There were no significant differences in critical care admission (planned or unplanned), hospital length of stay, or mortality.Conclusion: This study offered insight into the implementation of digital health technologies within our institution. Further work is required for optimisation of digital workflows, particularly given their more favourable acceptability in the post pandemic era. Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04638738.

Additional file 2. Video Feedback Questionnaire

This chapter explores AI through a philosophical and ethical lens. This includes an examination of how AI impacts on medicine in terms of uses and promises, limitations, and risks, as well as key questions to consider. While AI offers scope for complex and large-scale data processing, with the promise of an increase in efficiency and precision, some central limitations need to be highlighted. The use of AI also brings some pertinent and predictable, as well as unpredictable risks, such as those due to biases. Also considered is what may be lost where AI replaces established processes, not least those relational and interpersonal aspects that are central to healthcare. By covering these and related issues, this chapter offers ways to evaluate, and also balance, key benefits and risks arising from the application of AI to the medical sector.

Additional file 1. Table demonstrating the content of SMS messages sent in different arm, and by invitation type (open v. timed). Wording highlighted in yellow represents included behavioural change techniques. Wording highlighted in green represents the new video link.

Additional file 1: Appendix 1: Search Strategy for gut permeability and lactulose mannitol test in disease in Medline. Appendix 2: Search Strategy for gut permeability and lactulose mannitol test in disease in Embase. Appendix 3: Search Strategy for gut permeability and lactulose mannitol test in disease in Cochrane. Figure S1: PRISMA 2009 Flow diagram for coeliac disease. Figure S2: PRISMA 2009 Flow diagram for Crohn���s disease. Figure S3A: Standard Mean Difference (SMD) in LMR between treated coeliac disease and healthy controls. Figure S3B: Weighted Mean Difference (WMD) in LMR between treated coeliac disease and healthy controls. Figure S3C: Standard Mean Difference (SMD) in LMR between untreated coeliac disease and healthy controls. Figure S3D: Weighted Mean Difference (WMD) in LMR between untreated coeliac disease and healthy controls. Figure S3E: Standard Mean Difference (SMD) in LMR between untreated and treated coeliac disease. Figure S3F: Weighted Mean Difference (WMD) in LMR between untreated and treated coeliac disease. Figure S4A: Standard Mean Difference (SMD) in LMR between healthy controls and inactive Crohn's disease. Figure S4B: Weighted Mean Difference (WMD) in LMR between healthy controls and inactive Crohn's disease. Figure S4C: Standard Mean Difference (SMD) in LMR between active Crohn's disease and healthy controls. Figure S4D: Weighted Mean Difference (WMD) in LMR between active Crohn's disease and healthy controls. Figure S4E: Standard Mean Difference (SMD) in LMR between active and inactive Crohn's disease. Figure S4F: Weighted Mean Difference (WMD) in LMR between active and inactive Crohn's disease. Figure S5: Sensitivity and specificity of the L:M test in coeliac disease. Figure S6: Risk of bias for each risk of bias item in RCT studies. Figure S7: Risk of bias assessments presented per risk of bias domain in RCT studies. Figure S8: Risk of bias for each risk of bias item in non-randomised and cohort studies. Figure S9: Risk of bias assessments presented per risk of bias domain in non-randomised and cohort studies. Table S1: Summary of studies of gut permeability in Crohn���s disease. Table S2: Summary of studies of gut permeability in coeliac disease. Table S3: Sources of variability for the studies included in the meta-analysis. Table S4: Studies depicting sensitivity and specificity of LMR in screening for coeliac disease. Table B1: Newcastle Ottawa Score Assessing Risk of Bias for Case Control Studies. Table B2: Newcastle Ottawa Score Assessing Risk of Bias for Cross Sectional Studies. Table B3: Risk of Bias for Randomised Control Trials (RCT) using the Cochrane Risk of Bias Tool. Table B4: Risk of Bias in non-randomised trials and cohort studies using the ROBINS-I score.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes., The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

Objective Systemic inflammation is a recognised contributory factor in the pathogenesis of de novo post-operative atrial fibrillation after cardiac surgery. This study aims to determine whether serum soluble vascular endothelial cell adhesion molecule (sVCAM-1) may predict the onset of POAF in patients under going coronary artery bypass grafting. Methods 34 patients undergoing non-emergent, on-pump CABG were prospectively recruited. Plasma was obtained at 24 h pre-operatively and at 48 and 96 h post-operatively. POAF was defined by continuous Holter recording. Inter-group comparisons were performed using student t-test or ANOVA as appropriate. Results Thirteen (13/34) patients developed POAF at a mean of 2.5 days post-operatively. Serum sVCAM-1 was significantly increased in the pre-operative serum of POAF when compared to non-POAF patients (p = 0.022). No significant difference was observed between the groups at 48 h (p = 0.073) or 96 h (p = 0.135) post-operatively. sVCAM-1 had a sensitivity of 60.0% and specificity of 77.27%, with an overall diagnostic accuracy of 75.2% in predicting POAF. Conclusions sVCAM-1 concentration in the pre-operative serum of patients undergoing CABG may accurately predict the onset of de novo POAF. As such, serum sVCAM-1 may be used as a predictive biomarker for this common arrhythmia. Further work must now perform prospective, targeted validation of these results in a larger patient cohort.

The global pandemic of obesity continues to escalate worldwide and results in severe multisystem metabolic dysfunction in the expanding population of its sufferers. It is associated with the concurrent pathologies of type 2 diabetes, cardiovascular disease, cancer and sleep apnea, which have also increased in prevalence over the past decade. Obesity can be mapped in social networks and is recognised as a fundamental element of the metabolic syndrome contributing to the global burden of diabesity and oncobesity. Its impact on global health has resulted in a massive burden on healthcare services and is among the most prominent contributors to mounting healthcare costs. Despite its pathological impact, the non-surgical management of obesity through behavioural, lifestyle and pharmacotherapies has not offered dependable benefits in severely obese patients. Bariatric surgery has demonstrated consistent weight loss in morbidly obese subjects and is increasingly performed worldwide to treat morbid obesity. Furthermore, these operations may produce beneficial metabolic effects especially with respect to improvement in type 2 diabetes and the metabolic syndrome. Understanding surgical weight loss mechanisms and metabolic modulation is required to enhance patient benefits and operative outcomes. A surgical model of Roux-en-Y gastric bypass (RYGB) was developed as an experimental platform to investigate the metabolic effects of bariatric surgery. The model was studied through a systems biology approach to characterize systemic and gastro-intestinal surgical metabolic modulation. Analysis of postoperative faecal samples from this rodent model revealed a powerful shift in gut microbial ecology and also highlighted the role of RYGB surgery in regulating the cross-talk between the gut microbiome and its mammalian host. Similar metabolic changes were also identified in human subjects undergoing bariatric surgery. Analysis of plasma and cardiac tissue revealed shifts in metabolic activity, reflected by enhanced cardiac energy metabolism. Overall, the results of this work identify some of the potential mechanisms behind bariatric surgical weight loss and systemic metabolic enhancement. This study not only supports the term metabolic surgery but also identifies the global multi-systemic benefits of bariatric surgical procedures. As such, the findings presented in this thesis may in future contribute to the enhancement of current bariatric procedures and the development of the next-generation of innovative metabolic therapies. Open Access

Additional file 1: Figure S1. ROC analysis of cross-validated scores from OPLS-DA models. (A) NMR analysis of urine; Tcv1 and Tcv2 represent the first and second PLS component when 2 PLS components were applied to separate 3 classes; (B) LC-MS analysis of urine; (C) bile acid analysis of urine; (D) NMR analysis of serum; (E) NMR analysis of feces. Figure S2. Relative metabolite levels in urine from cohorts 1 (A) and 2 (B), plasma from cohorts 3 (C) and 2 (D), and feces from cohort 1 (E). Metabolite levels are indicated by relative peak heights from the median fold normalized spectra of urine and feces and non-normalized serum spectra. Error bars are presented in SEM. a.u. stands for arbitrary unit. Figure S3. OPLS-DA cross-validated scores plots of urinary 1H NMR spectra of the RYGB and LGB patients from cohort 3 at pre-op (Q2Y=0.49; R2X=12.35%; R2Y=89.6%; p = 0.006), and 6-month post-op (Q2Y=0.44; R2X=30.66%; R2Y=95.2%; p = 0.038). The metabolites that significantly contributed to the classification of different time points are shown in Fig. 2C. Figure S4. OPLS-DA cross-validated scores plots of urinary reverse-phase liquid chromatography-mass spectrometry profiles of the bariatric patients from cohort 1 in ESI positive mode (Q2Y=0.46; R2X=24.8%; R2Y=73.4%; CVANOVA p = 2.9 x 10-14) and ESI negative mode (Q2Y=0.52; R2X=14%; R2Y=76.6%; CVANOVA p = 6.2 x 10-17). The identified metabolites that significantly contributed to the classification between pre-op (black) and 2-6 months post-op (green) are listed in the table. Positive correlation (r) represents higher relative concentrations of these metabolites at the post-op compared to pre-op. p[1] is loadings from the OPLS-DA models. Coefficient of variation (CV) was calculated based on the quality control (QC) samples to evaluate the analytical variation of the features. Figure S5. OPLS-DA cross-validated scores plots of urinary bile acid profiles of the RYGB patients from cohort 1 at pre-op (black), 2-6 months (green) and 1-2 years (orange) post-op (A. Q2Y=0.21; R2X=16.9%; R2Y=48.9%; CVANOVA p = 5.8x10-9). Bar plots of relative intensities of urinary bile acids identified from cohort 2 RYGB patients at pre-op (black) and 6 months post-op (green). Wilcoxon matched pairs signed rank test was used. ** 0.001< p

Additional file 1: Figure S1. ROC analysis of cross-validated scores from OPLS-DA models. (A) NMR analysis of urine; Tcv1 and Tcv2 represent the first and second PLS component when 2 PLS components were applied to separate 3 classes; (B) LC-MS analysis of urine; (C) bile acid analysis of urine; (D) NMR analysis of serum; (E) NMR analysis of feces. Figure S2. Relative metabolite levels in urine from cohorts 1 (A) and 2 (B), plasma from cohorts 3 (C) and 2 (D), and feces from cohort 1 (E). Metabolite levels are indicated by relative peak heights from the median fold normalized spectra of urine and feces and non-normalized serum spectra. Error bars are presented in SEM. a.u. stands for arbitrary unit. Figure S3. OPLS-DA cross-validated scores plots of urinary 1H NMR spectra of the RYGB and LGB patients from cohort 3 at pre-op (Q2Y=0.49; R2X=12.35%; R2Y=89.6%; p = 0.006), and 6-month post-op (Q2Y=0.44; R2X=30.66%; R2Y=95.2%; p = 0.038). The metabolites that significantly contributed to the classification of different time points are shown in Fig. 2C. Figure S4. OPLS-DA cross-validated scores plots of urinary reverse-phase liquid chromatography-mass spectrometry profiles of the bariatric patients from cohort 1 in ESI positive mode (Q2Y=0.46; R2X=24.8%; R2Y=73.4%; CVANOVA p = 2.9 x 10-14) and ESI negative mode (Q2Y=0.52; R2X=14%; R2Y=76.6%; CVANOVA p = 6.2 x 10-17). The identified metabolites that significantly contributed to the classification between pre-op (black) and 2-6 months post-op (green) are listed in the table. Positive correlation (r) represents higher relative concentrations of these metabolites at the post-op compared to pre-op. p[1] is loadings from the OPLS-DA models. Coefficient of variation (CV) was calculated based on the quality control (QC) samples to evaluate the analytical variation of the features. Figure S5. OPLS-DA cross-validated scores plots of urinary bile acid profiles of the RYGB patients from cohort 1 at pre-op (black), 2-6 months (green) and 1-2 years (orange) post-op (A. Q2Y=0.21; R2X=16.9%; R2Y=48.9%; CVANOVA p = 5.8x10-9). Bar plots of relative intensities of urinary bile acids identified from cohort 2 RYGB patients at pre-op (black) and 6 months post-op (green). Wilcoxon matched pairs signed rank test was used. ** 0.001< p

Noncommunicable diseases (NCDs) are the main cause of death and disability worldwide. They are responsible for more than 70% of all global mortality. Furthermore, being among the biggest generators of health care expenditure, they pose important economic sustainability challenges to health care systems worldwide. After a slow start, the international community has come to prioritise these conditions, as evidenced by high-level meetings on NCDs at the UN General Assembly. In 2015, 193 countries committed to reducing premature deaths from NCDs by a third by 2030, as part of the Sustainable Development Goals. WHO Member States have also endorsed a menu of cost-effective NCDs best buy policy options that can be used to tackle the NCD pandemic. However, many governments have been grappling with this challenge and looking for effective policy-based solutions, particularly in the area of promoting healthy eating. In Europe, NCDs are responsible for approximately 80% of all health care spending. Nevertheless, 80% of such NCDs are preventable if their behavioural risk factors are properly addressed. This was why countries in the WHO European Region have implemented a wide range of mandatory and voluntary policies to promote healthy lifestyles, including a growing number of interpretative nutrition labelling schemes, targeted food and beverage taxes, comprehensive reformulation strategies, and restrictions on the marketing of unhealthy foods. Recently, the Portuguese example has been repeatedly used by the WHO as a good role model for other countries to follow in this area. The aim of this thesis was to assess whether Portugal is a strong implementer of the WHO’s recommended policies for tackling NCDs and whether the strategy used for the implementation of the healthy nutrition policies was therefore successful. In this thesis, five of the most challenging policies to implement were individually analysed and characterised using different health impact and effectiveness assessment methods. Furthermore, all the policy areas were critically assessed regarding their implementation challenges and determinants. Among the biggest scientific innovations resulting from this thesis research development is the usage of real food consumption data from Portuguese Population cohorts in order to model the impact of both mandatory and voluntary policies. Furthermore, this thesis presents research using total SSBs consumption data at the national level for the first time in this research area. Also, the ability to model the impact of a SSBs tax on obesity incidence is one of the biggest scientific break throughs of the hereby-presented research. The outcomes of the strategy developed by Portugal in order to address its unhealthy eating habits provide an opportunity for refining the reason why some countries score better than others at implementing the WHO’s NCD policies. Furthermore, the lessons learned from this case study may contribute to the improvement of change management as a way to push for high health impact implementation of NCD policies. Open Access

ObjectivesPatients in inpatient mental health settings face similar risks (eg, medication errors) to those in other areas of healthcare. In addition, some unsafe behaviours associated with serious mental health problems (eg, self-harm), and the measures taken to address these (eg, restraint), may result in further risks to patient safety. The objective of this review is to identify and synthesise the literature on patient safety within inpatient mental health settings using robust systematic methodology.DesignSystematic review and meta-synthesis. Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, MEDLINE, PsycINFO and Web of Science were systematically searched from 1999 to 2019. Search terms were related to ‘mental health’, ‘patient safety’, ‘inpatient setting’ and ‘research’. Study quality was assessed using the Hawker checklist. Data were extracted and grouped based on study focus and outcome. Safety incidents were meta-analysed where possible using a random-effects model.ResultsOf the 57 637 article titles and abstracts, 364 met inclusion criteria. Included publications came from 31 countries and included data from over 150 000 participants. Study quality varied and statistical heterogeneity was high. Ten research categories were identified: interpersonal violence, coercive interventions, safety culture, harm to self, safety of the physical environment, medication safety, unauthorised leave, clinical decision making, falls and infection prevention and control.ConclusionsPatient safety in inpatient mental health settings is under-researched in comparison to other non-mental health inpatient settings. Findings demonstrate that inpatient mental health settings pose unique challenges for patient safety, which require investment in research, policy development, and translation into clinical practice.PROSPERO registration numberCRD42016034057.

The incidence and prevalence of Obesity and T2DM is increasing at an alarming rate and now poses a global threat to mankind. Bariatric surgery is now an established strategy for combating both these conditions but recent years have also seen the emergence of endoscopic treatments designed to mimic the effects of surgery. These devices have the added advantage of being minimally invasive and easily reversible. The Endobarrier (EB) is an endoluminal duodenal-jejunal bypass liner (DJBL) licensed for up to 12 months of treatment in patients with type 2 diabetes who are obese. In this thesis I explored the feasibility and safety of this device as an effective method of weight loss and glycaemic control in the setting of nationally funded multicenter randomised control trial. This clinical trial compared the device implanted for 1 year versus standard medical therapy (control) in a cohort of 170 obese patients with type 2 diabetes. Body weight decreased by 10.85.3kg in the EB group and 12.17.8kg at 6 and 12 months respectively. In comparison the control group lost 6.35.5kg at 6 months and 6.2 6.4kg at 12 months (P=

The rising obesity pandemic and the concomitant rise in its co-morbidities are leading causes of global morbidity and mortality. Bariatric surgery is a form of gastrointestinal surgery that leads to sustained weight loss, diabetes resolution, reduction in cancer risk and other improvements in health. MicroRNAs are a family of small, endogenous, non-coding RNAs that regulate gene expression at the post-transcriptional level. MicroRNAs control expression of over half the human transcriptome and are involved in processes fundamental to both normal physiology and disease, including obesity and diabetes. This study hypothesizes that microRNAs are biomarkers for health improvements following bariatric surgery. Using quantitative PCR, a microRNA baseline was established in the serum and urine of an obese human population and increases in three anti-fibrotic microRNAs were found in urine following bariatric surgery. Circulating microRNA profiles were characterised in Roux-en-Y gastric bypass patients preoperatively and at 5 timepoints postoperatively. Roux-en-Y gastric bypass significantly altered circulating microRNA profiles in a time dependent manner. Relative to preoperative levels, of the 159 circulating microRNAs assayed 2 were significantly deregulated 1 month postoperatively, 5 were deregulated at 3 months, 10 at 6 months, 28 at 9 months and 31 at 12 months. Target prediction and pathway analysis revealed that these differentiated microRNAs regulate biological pathways that are involved in obesity and that microRNAs may contribute to the development of the beneficial post-bariatric phenotype. Both circulating and urinary post-bariatric microRNA levels correlated with measured clinical biomarkers such as BMI and blood glucose. These results indicate that bariatric operations fundamentally alter microRNA expression both in urine and in circulation and suggest that microRNAs represent not only potentially novel biomarkers for improvements in health following surgery, but are possible biological effectors that contribute to the mechanisms behind bariatric surgery. MicroRNA expression profiles could potentially be used to monitor operative outcomes and understanding the role of these differentially expressed microRNAs could shed light behind the mechanism by which bariatric surgery improves health. Open Access