BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canad

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Delayed Admission to the Intensive Care Unit and Mortality of Critically Ill Adults: Systematic Review and Meta-analysis

Delayed admission of patients to the intensive care unit (ICU) is increasing worldwide and can be followed by adverse outcomes when critical care treatment is not provided timely. This systematic review and meta-analysis appraised and synthesized the published literature about the association between delayed ICU admission and mortality of adult patients. Articles published from inception up to August 2021 in English-language, peer-reviewed journals indexed in CINAHL, PubMed, Scopus, Cochrane Library, and Web of Science were searched by using key terms. Delayed ICU admission constituted the intervention, while mortality for any predefined time period was the outcome. Risk for bias was evaluated with the Newcastle-Ottawa Scale and additional criteria. Study findings were synthesized qualitatively, while the odds ratios (ORs) for mortality with 95% confidence intervals (CIs) were combined quantitatively. Thirty-four observational studies met inclusion criteria. Risk for bias was low in most studies. Unadjusted mortality was reported in 33 studies and was significantly higher in the delayed ICU admission group in 23 studies. Adjusted mortality was reported in 18 studies, and delayed ICU admission was independently associated with significantly higher mortality in 13 studies. Overall, pooled OR for mortality in case of delayed ICU admission was 1.61 (95% CI 1.44-1.81). Interstudy heterogeneity was high ( I 2 = 66.96 % ). According to subgroup analysis, OR for mortality was remarkably higher in postoperative patients (OR, 2.44, 95% CI 1.49-4.01). These findings indicate that delayed ICU admission is significantly associated with mortality of critically ill adults and highlight the importance of providing timely critical care in non-ICU settings.</jats:p

Correlation of the severity of coronary artery disease with patients' metabolic profile- rationale, design and baseline patient characteristics of the CorLipid trial

Abstract Background Coronary artery disease (CAD) remains one of the leading causes of mortality and morbidity worldwide. As oxygen and nutrient supply to the myocardium significantly decrease during ischemic periods, important changes occur regarding myocardial intermediary energy metabolism. Metabolomics is an emerging field in systems biology, which quantifies metabolic changes in response to disease progression. This study aims to evaluate the diagnostic utility of plasma metabolomics-based biomarkers for determining the complexity and severity of CAD, as it is assessed via the SYNTAX score. Methods Corlipid is a prospective, non-interventional cohort trial empowered to enroll 1065 patients with no previous coronary intervention history, who undergo coronary angiography in University Hospital AHEPA, Thessaloniki. Venous blood samples are collected before coronary angiography. State-of the-art analytical methods are performed to calculate the serum levels of novel biomarkers: ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and the ratio of apolipoprotein B/apolipoprotein A1. Furthermore, all patients will be categorized based on the indication for coronary angiography (acute coronary syndrome, chronic coronary syndrome, preoperative coronary angiography) and on the severity of CAD using the SYNTAX score. Follow-up of 12 months after enrollment will be performed to record the occurrence of major adverse cardiovascular events. A risk prediction algorithm will be developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD based on their metabolite signatures. The first patient was enrolled in July 2019 and completion of enrollment is expected until May 2021. Discussion CorLipid is an ongoing trial aiming to investigate the correlation between metabolic profile and complexity of coronary artery disease in a cohort of patients undergoing coronary angiography with the potential to suggest a decision-making tool with high discriminative power for patients with CAD. To our knowledge, Corlipid is the first study aspiring to create an integrative metabolomic biomarkers-based algorithm by combining metabolites from multiple classes, involved in a wide range of pathways with well-established biochemical markers. Trial registration CorLipid trial registration: ClinicalTrials.gov number: NCT04580173. Registered 8 October 2020—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04580173. </jats:sec

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

Obesity in childhood and adolescence represents a major health problem. Novel e-Health technologies have been developed in order to provide a comprehensive and personalized plan of action for the prevention and management of overweight and obesity in childhood and adolescence. We used information and communication technologies to develop a &ldquo;National Registry for the Prevention and Management of Overweight and Obesity&rdquo; in order to register online children and adolescents nationwide, and to guide pediatricians and general practitioners regarding the management of overweight or obese subjects. Furthermore, intelligent multi-level information systems and specialized artificial intelligence algorithms are being developed with a view to offering precision and personalized medical management to obese or overweight subjects. Moreover, the Big Data against Childhood Obesity platform records behavioral data objectively by using inertial sensors and Global Positioning System (GPS) and combines them with data of the environment, in order to assess the full contextual framework that is associated with increased body mass index (BMI). Finally, a computerized decision-support tool was developed to assist pediatric health care professionals in delivering personalized nutrition and lifestyle optimization advice to overweight or obese children and their families. These e-Health applications are expected to play an important role in the management of overweight and obesity in childhood and adolescence

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

Obesity in childhood and adolescence represents a major health problem. Novel e-Health technologies have been developed in order to provide a comprehensive and personalized plan of action for the prevention and management of overweight and obesity in childhood and adolescence. We used information and communication technologies to develop a “National Registry for the Prevention and Management of Overweight and Obesity” in order to register online children and adolescents nationwide, and to guide pediatricians and general practitioners regarding the management of overweight or obese subjects. Furthermore, intelligent multi-level information systems and specialized artificial intelligence algorithms are being developed with a view to offering precision and personalized medical management to obese or overweight subjects. Moreover, the Big Data against Childhood Obesity platform records behavioral data objectively by using inertial sensors and Global Positioning System (GPS) and combines them with data of the environment, in order to assess the full contextual framework that is associated with increased body mass index (BMI). Finally, a computerized decision-support tool was developed to assist pediatric health care professionals in delivering personalized nutrition and lifestyle optimization advice to overweight or obese children and their families. These e-Health applications are expected to play an important role in the management of overweight and obesity in childhood and adolescence.</jats:p

Association of GRACE Risk Score with Coronary Artery Disease Complexity in Patients with Acute Coronary Syndrome

The GRACE score constitutes a useful tool for risk stratification in patients with acute coronary syndrome (ACS), while the SYNTAX score determines the complexity of coronary artery disease (CAD). This study sought to correlate these scores and assess the accuracy of the GRACE score in predicting the extent of CAD. A total of 539 patients with ACS undergoing coronary angiography were included in this analysis. The patients were classified into those with a SYNTAX score &lt; 33 and a SYNTAX score ≥ 33. Spearman’s correlation and receiver operator characteristic analysis were conducted to investigate the role of the GRACE score as a predictor of the SYNTAX score. There was a significantly positive correlation between the SYNTAX and the GRACE scores (r = 0.32, p &lt; 0.001). The GRACE score predicted severe CAD (SYNTAX ≥ 33) moderately well (the area under the curve was 0.595 (0.522–0.667)). A GRACE score of 126 was documented as the optimal cut-off for the prediction of a SYNTAX score ≥ 33 (sensitivity = 53.5% and specificity = 66%). Therefore, our study reports a significantly positive correlation between the GRACE and the SYNTAX score in patients with ACS. Notably, NSTEMI patients with a high-risk coronary anatomy have higher calculated GRACE scores. A multidisciplinary approach by a heart team could possibly alter the therapeutic approach and management in patients presenting with ACS and a high calculated GRACE score