Serum Phthalate Concentrations and Biomarkers of Oxidative Stress in Adipose Tissue in a Spanish Adult Cohort.

The relationship between phthalates, a group of chemical pollutants classified as endocrine disruptors, and oxidative stress is not fully understood. The aim of the present hospital-based study was to explore the associations between circulating levels of 10 phthalate metabolites and 8 biomarkers of oxidative stress in adipose tissue. The study population (n = 143) was recruited in two hospitals in the province of Granada (Spain). Phthalate metabolite concentrations were analyzed by isotope diluted online-TurboFlow-LC-MS/MS in serum samples, while oxidative stress markers were measured by commercially available kits in adipose tissue collected during routine surgery. Statistical analyses were performed by MM estimators' robust linear regression and weighted quantile sum regression. Mainly, positive associations were observed of monomethyl phthalate (MMP), monoiso-butyl phthalate (MiBP), and mono-n-butyl phthalate (MnBP) (all low molecular weight phthalates) with glutathione peroxidase (GPx) and thiobarbituric acid reactive substances (TBARS), while an inverse association was found between monoiso-nonyl phthalate (MiNP) (high molecular weight phthalate) and the same biomarkers. WQS analyses showed significant effects of the phthalate mixture on GSH (ß = -30.089; p-value = 0.025) and GSSG levels (ß = -19.591; p-value = 0.030). Despite the limitations inherent to the cross-sectional design, our novel study underlines the potential influence of phthalate exposure on redox homeostasis, which warrants confirmation in further research.

Predicting Adverse Outcomes Following Catheter Ablation Treatment for Atrial Flutter/Fibrillation.

BACKGROUND & AIM: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF) and/or atrial flutter (AFL). METHODS: We used a linked dataset including hospital administrative data, prescription medicine claims, emergency department presentations, and death registrations of patients in New South Wales, Australia. The cohort included patients who received catheter ablation for AF and/or AFL. Traditional and deep survival models were trained to predict major bleeding events and a composite of heart failure, stroke, cardiac arrest, and death. RESULTS: Out of a total of 3,285 patients in the cohort, 177 (5.3%) experienced the composite outcome-heart failure, stroke, cardiac arrest, death-and 167 (5.1%) experienced major bleeding events after catheter ablation treatment. Models predicting the composite outcome had high-risk discrimination accuracy, with the best model having a concordance index >0.79 at the evaluated time horizons. Models for predicting major bleeding events had poor risk discrimination performance, with all models having a concordance index <0.66. The most impactful features for the models predicting higher risk were comorbidities indicative of poor health, older age, and therapies commonly used in sicker patients to treat heart failure and AF and AFL. DISCUSSION: Diagnosis and medication history did not contain sufficient information for precise risk prediction of experiencing major bleeding events. Predicting the composite outcome yielded promising results, but future research is needed to validate the usefulness of these models in clinical practice. CONCLUSIONS: Machine learning models for predicting the composite outcome have the potential to enable clinicians to identify and manage high-risk patients following catheter ablation for AF and AFL proactively.

Researchers in rheumatology should avoid categorization of continuous predictor variables.

BACKGROUND: Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS: We conducted and compared the results of two analyses of the association between our predictor variable (percentage change in body mass index [BMI] from baseline to four years) and two outcome variable domains of structure and pain in knee and hip osteoarthritis. These two outcome variable domains covered 26 different outcomes for knee and hip combined. In the first analysis (categorical analysis), percentage change in BMI was categorized as ≥ 5% decrease in BMI, < 5% change in BMI, and ≥ 5% increase in BMI, while in the second analysis (continuous analysis), it was left as a continuous variable. In both analyses (categorical and continuous), we used generalized estimating equations with a logistic link function to investigate the association between the percentage change in BMI and the outcomes. RESULTS: For eight of the 26 investigated outcomes (31%), the results from the categorical analyses were different from the results from the continuous analyses. These differences were of three types: 1) for six of these eight outcomes, while the continuous analyses revealed associations in both directions (i.e., a decrease in BMI had one effect, while an increase in BMI had the opposite effect), the categorical analyses showed associations only in one direction of BMI change, not both; 2) for another one of these eight outcomes, the categorical analyses suggested an association with change in BMI, while this association was not shown in the continuous analyses (this is potentially a false positive association); 3) for the last of the eight outcomes, the continuous analyses suggested an association of change in BMI, while this association was not shown in the categorical analyses (this is potentially a false negative association). CONCLUSIONS: Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.

Carbon dioxide (CO2) emissions and adherence to Mediterranean diet in an adult population: the Mediterranean diet index as a pollution level index.

BACKGROUND: Research related to sustainable diets is is highly relevant to provide better understanding of the impact of dietary intake on the health and the environment. AIM: To assess the association between the adherence to an energy-restricted Mediterranean diet and the amount of CO2 emitted in an older adult population. DESIGN AND POPULATION: Using a cross-sectional design, the association between the adherence to an energy-reduced Mediterranean Diet (erMedDiet) score and dietary CO2 emissions in 6646 participants was assessed. METHODS: Food intake and adherence to the erMedDiet was assessed using validated food frequency questionnaire and 17-item Mediterranean questionnaire. Sociodemographic characteristics were documented. Environmental impact was calculated through greenhouse gas emissions estimations, specifically CO2 emissions of each participant diet per day, using a European database. Participants were distributed in quartiles according to their estimated CO2 emissions expressed in kg/day: Q1 (≤2.01 kg CO2), Q2 (2.02-2.34 kg CO2), Q3 (2.35-2.79 kg CO2) and Q4 (≥2.80 kg CO2). RESULTS: More men than women induced higher dietary levels of CO2 emissions. Participants reporting higher consumption of vegetables, fruits, legumes, nuts, whole cereals, preferring white meat, and having less consumption of red meat were mostly emitting less kg of CO2 through diet. Participants with higher adherence to the Mediterranean Diet showed lower odds for dietary CO2 emissions: Q2 (OR 0.87; 95%CI: 0.76-1.00), Q3 (OR 0.69; 95%CI: 0.69-0.79) and Q4 (OR 0.48; 95%CI: 0.42-0.55) vs Q1 (reference). CONCLUSIONS: The Mediterranean diet can be environmentally protective since the higher the adherence to the Mediterranean diet, the lower total dietary CO2 emissions. Mediterranean Diet index may be used as a pollution level index.

Metabolic syndrome criteria and severity and carbon dioxide (CO2) emissions in an adult population.

BACKGROUND: Metabolic syndrome (MetS) has become a growing risk factor of some non-communicable diseases. Increase of greenhouse gas emissions affects the planet. AIMS: To assess the association between MetS severity and amount of carbon dioxide (CO2) emitted in an adult population. DESIGN: Cross-sectional study (n = 6646; 55-76-year-old-men; 60-75-year-old-women with MetS). METHODS: Dietary habits were assessed using a pre-validated semi quantitative 143-item food frequency questionnaire. The amount of CO2 emitted due to the production of food consumed by person and day was calculated using a European database, and the severity of the MetS was calculated with the MetS Severity Score. RESULTS: Higher glycaemia levels were found in people with higher CO2 emissions. The risk of having high severe MetS was related to high CO2 emissions. CONCLUSIONS: Low CO2 emissions diet would help to reduce MetS severity. Advantages for both health and the environment were found following a more sustainable diet. TRIAL REGISTRATION: ISRCTN, ISRCTN89898870 . Registered 05 September 2013.

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks.

Non-alcoholic fatty liver disease (NAFLD) seems to have some molecular links with atherosclerosis (ATH); however, the molecular pathways which connect both pathologies remain unexplored to date. The identification of common factors is of great interest to explore some therapeutic strategies to improve the outcomes for those affected patients. Differentially expressed genes (DEGs) for NAFLD and ATH were extracted from the GSE89632 and GSE100927 datasets, and common up- and downregulated DEGs were identified. Subsequently, a protein-protein interaction (PPI) network based on the common DEGs was performed. Functional modules were identified, and the hub genes were extracted. Then, a Gene Ontology (GO) and pathway analysis of common DEGs was performed. DEGs analysis in NAFLD and ATH showed 21 genes that were regulated similarly in both pathologies. The common DEGs with high centrality scores were ADAMTS1 and CEBPA which appeared to be down- and up-regulated in both disorders, respectively. For the analysis of functional modules, two modules were identified. The first one was oriented to post-translational protein modification, where ADAMTS1 and ADAMTS4 were identified, and the second one mainly related to the immune response, where CSF3 was identified. These factors could be key proteins with an important role in the NAFLD/ATH axis.

Pandemic-related stress in pregnant women during the first COVID-19 lockdown and neonatal development.

BACKGROUND: Maternal stress and psychopathology have a negative effect on mothers and neonates. Maternal stress may affect neonatal growth and development both physically and psychologically. PURPOSE: To study the impact of pandemic-related pregnancy stress and maternal psychopathological symptoms during the COVID-19 lockdown in 2020 on neonatal development. METHODS: A two-phase prospective study was carried out on a sample of 181 pregnant women ranged from 18 to 40 years old in Spain (Europe). Phase 1: Pandemic-related pregnancy stress (PREPS), Prenatal Distress Questionnaire (PDQ), Perceived Stress Scale (PSS) and the revised version of the Symptom Checklist-90 (SCL-90-R) were used to assess psychological symptoms during the lockdown. In the follow-up (Phase 2), obstetric, birth-related and anthropometric variables were collected from 81 pregnant women-neonates dyads. RESULTS: Primiparous women showed higher psychopathological symptoms and higher levels of pandemic-related pregnancy stress than multiparous women. A multiple linear regression model showed that pandemic-related pregnancy stress could predict the length of neonate by adjusting for maternal age and gestational age, especially for primiparous women. IMPLICATIONS FOR RESEARCH: Studies assessing neonates development should evaluate the long-term effect of the COVID-19 pandemic on neonates´ length. IMPLICATIONS FOR PRACTICE: States the relation between pandemic-related pregnancy stress and neonatal development by being able to track the effects on neonates whose mothers had high levels of stress during the COVID-19 pandemic.

Weight loss is associated with reduced risk of knee and hip replacement: a survival analysis using Osteoarthritis Initiative data.

OBJECTIVE: To describe the association between body weight change and the risk of knee replacement and hip replacement. DESIGN: Time-to-event survival analysis from a population-based cohort of participants who had or were at risk of clinically significant knee osteoarthritis at baseline. SETTING: Data from the Osteoarthritis Initiative (OAI), which collected data from four clinical centres in the United States. PARTICIPANTS: A total of 8069 knees from 4081 participants, and 8076 hips from 4064 participants (59.3% female) aged 45-79 years, with mean ± SD body mass index (BMI) of 28.7 ± 4.8 kg/m2, were included in the knee and hip analyses, respectively. EXPOSURE: Body weight change from baseline as a percentage of baseline at repeated follow-up visits over 8 years. MAIN OUTCOME MEASURE: Incidence of primary knee or hip replacement during 8-year follow-up. RESULTS: Body weight change had a small, positive, linear association with the risk of knee replacement (adjusted hazard ratio [HR] 1.02; 95% confidence interval [CI] 1.00-1.04). Body weight change was also positively and linearly associated with the risk of hip replacement in hips that were persistently painful at baseline (adjusted HR 1.03; 95% CI 1.01-1.05), but not in hips that were not persistently painful at baseline. There were no significant interactions between body weight change and baseline BMI in the association with knee or hip replacement. CONCLUSIONS: In people with or at risk of clinically significant knee osteoarthritis, every 1% weight loss was associated with a 2% reduced risk of knee replacement and - in those people who also had one or more persistently painful hips - a 3% reduced risk of hip replacement, regardless of baseline BMI. Public health strategies that incorporate weight loss interventions have the potential to reduce the burden of knee and hip replacement surgery.

Influence of placebo effect in mental disorders research: A systematic review and meta-analysis.

BACKGROUND: Randomized controlled trials (RCT) in mental disorders research commonly use active control groups including psychotherapeutic shams or inactive medication. This meta-analysis assessed whether placebo conditions (active controls) had an effect compared to no treatment or usual care (passive controls). METHODS: PubMed, Scopus, PsycINFO, PsycARTICLES, Ovid, the Cochrane Central Register of Controlled Trials and Web of Science were searched from inception to April 2021 and reference lists of relevant articles. Three-arm RCTs, including active and passive control groups, were selected. Where individual standardized mean difference (SMD) was calculable, random effects meta-analyses were performed to estimate an overall effect size with 95% confidence intervals (CI) comparing active vs passive controls. Heterogeneity was assessed using I² statistic and meta-regression. Funnel asymmetry was evaluated using Egger's test (Prospero registration: CRD42021242940). RESULTS: 24 articles with 25 relevant RCTs were included in the review, of which 11 studies were of high risk of bias. There was an improvement in outcomes favouring the placebo conditions, compared to passive controls, overall (25 studies, SMD 0.24, 95% CI 0.06-0.42, I² = 43%) and in subgroups with anxiety (SMD 0.45, 95% CI 0.07-0.84, I² = 59%) or depression (SMD 0.22, 95% CI 0.04-0.39, I² = 0%). Meta-regression did not show a significant explanation for heterogeneity. Egger's test showed no asymmetry (p = .200). CONCLUSIONS: A small placebo effect was observed in mental disorders research overall, and in patients with anxiety or depression. These findings should be interpreted with caution in the light of heterogeneity and risk of bias.

Assessing the effectiveness of empirical calibration under different bias scenarios.

BACKGROUND: Estimations of causal effects from observational data are subject to various sources of bias. One method for adjusting for the residual biases in the estimation of treatment effects is through the use of negative control outcomes, which are outcomes not believed to be affected by the treatment of interest. The empirical calibration procedure is a technique that uses negative control outcomes to calibrate p-values. An extension of this technique calibrates the coverage of the 95% confidence interval of a treatment effect estimate by using negative control outcomes as well as positive control outcomes, which are outcomes for which the treatment of interest has known effects. Although empirical calibration has been used in several large observational studies, there is no systematic examination of its effect under different bias scenarios. METHODS: The effect of empirical calibration of confidence intervals was analyzed using simulated datasets with known treatment effects. The simulations consisted of binary treatment and binary outcome, with biases resulting from unmeasured confounder, model misspecification, measurement error, and lack of positivity. The performance of the empirical calibration was evaluated by determining the change in the coverage of the confidence interval and the bias in the treatment effect estimate. RESULTS: Empirical calibration increased coverage of the 95% confidence interval of the treatment effect estimate under most bias scenarios but was inconsistent in adjusting the bias in the treatment effect estimate. Empirical calibration of confidence intervals was most effective when adjusting for the unmeasured confounding bias. Suitable negative controls had a large impact on the adjustment made by empirical calibration, but small improvements in the coverage of the outcome of interest were also observable when using unsuitable negative controls. CONCLUSIONS: This work adds evidence to the efficacy of empirical calibration of the confidence intervals in observational studies. Calibration of confidence intervals is most effective where there are biases due to unmeasured confounding. Further research is needed on the selection of suitable negative controls.

Causal inference for observational longitudinal studies using deep survival models.

OBJECTIVE: Causal inference for observational longitudinal studies often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-dependent patient history and time-dependent covariates. MATERIALS AND METHODS: To tackle this longitudinal treatment effect estimation problem, we have developed a time-variant causal survival (TCS) model that uses the potential outcomes framework with an ensemble of recurrent subnetworks to estimate the difference in survival probabilities and its confidence interval over time as a function of time-dependent covariates and treatments. RESULTS: Using simulated survival datasets, the TCS model showed good causal effect estimation performance across scenarios of varying sample dimensions, event rates, confounding and overlapping. However, increasing the sample size was not effective in alleviating the adverse impact of a high level of confounding. In a large clinical cohort study, TCS identified the expected conditional average treatment effect and detected individual treatment effect heterogeneity over time. TCS provides an efficient way to estimate and update individualized treatment effects over time, in order to improve clinical decisions. DISCUSSION: The use of a propensity score layer and potential outcome subnetworks helps correcting for selection bias. However, the proposed model is limited in its ability to correct the bias from unmeasured confounding, and more extensive testing of TCS under extreme scenarios such as low overlapping and the presence of unmeasured confounders is desired and left for future work. CONCLUSION: TCS fills the gap in causal inference using deep learning techniques in survival analysis. It considers time-varying confounders and treatment options. Its treatment effect estimation can be easily compared with the conventional literature, which uses relative measures of treatment effect. We expect TCS will be particularly useful for identifying and quantifying treatment effect heterogeneity over time under the ever complex observational health care environment.

An Observational Study of Clinical and Health System Factors Associated With Catheter Ablation and Early Ablation Treatment for Atrial Fibrillation in Australia.

OBJECTIVE: To investigate clinical and health system factors associated with receiving catheter ablation (CA) and earlier ablation for non-valvular atrial fibrillation (AF). METHODS: We used hospital administrative data linked with death registrations in New South Wales, Australia for patients with a primary diagnosis of AF between 2009 and 2017. Outcome measures included receipt of CA versus not receiving CA during follow-up (using Cox regression) and receipt of early ablation (using logistic regression). RESULTS: Cardioversion during index admission (hazard ratio [HR] 1.96; 95% CI 1.75-2.19), year of index admission (HR 1.07; 95% CI 1.05-1.10), private patient status (HR 2.65; 95% CI 2.35-2.97), and living in more advantaged areas (HR 1.18; 95% CI 1.13-1.22) were associated with a higher likelihood of receiving CA. A history of congestive heart failure, hypertension, diabetes, and myocardial infarction were associated with a lower likelihood of receiving CA. Private patient status (odds ratio [OR] 2.04; 95% CI 1.59-2.61), cardioversion during index admission (OR 1.25; 95% CI 1.0-1.57), and history of diabetes (OR 1.6; 95% CI 1.06-2.41) were associated with receiving early ablation. CONCLUSIONS: Beyond clinical factors, private patients are more likely to receive CA and earlier ablation than their public counterparts. Whether the earlier access to ablation procedures in private patients is leading to differences in outcomes among patients with atrial fibrillation remains to be explored.

Fruit consumption and cardiometabolic risk in the PREDIMED-plus study: A cross-sectional analysis.

BACKGROUND AND AIMS: Total fruit consumption is important for cardiovascular disease prevention, but also the variety and form in which is consumed. The aim of the study was to assess the associations between total fruit, subgroups of fruits based on their color and fruit juices consumption with different cardiometabolic parameters. METHODS AND RESULTS: A total of 6633 elderly participants (aged 55-75 years) with metabolic syndrome from the PREDIMED-Plus study were included in this analysis. Fruit and fruit juice consumption was assessed using a food frequency questionnaire. Linear regression models were fitted to evaluate the association between exposure variables (total fruit, subgroups based on the color, and fruit juices) and different cardiometabolic risk factors. Individuals in the highest category of total fruit consumption (≥3 servings/d) had lower waist circumference (WC) (ß = -1.04 cm; 95%CI:-1.81, -0.26), fasting glucose levels (ß = -2.41 mg/dL; 95%CI(-4.19, -0.63) and LDL-cholesterol (ß = -4.11 mg/dL; 95%CI:-6.93, -1.36), but, unexpectedly, higher systolic blood pressure (BP) (ß = 1.84 mmHg; 95%CI: 0.37, 3.30) and diastolic BP (ß = 1.69 mmHg; 95%CI:0.83, 2.56) when compared to those in the lowest category of consumption (<1 servings/d). Participants consuming ≥1 serving/day of total fruit juice had lower WC (ß = -0.92 cm; 95%CI:-1.56, -0.27) and glucose levels (ß = -1.59 mg/dL; 95%CI:-2.95, -0.23) than those consuming <1 serving/month. The associations with cardiometabolic risk factors differed according to the color of fruits. CONCLUSION: Fruit consumption is associated with several cardiometabolic risk factors in Mediterranean elders with metabolic syndrome. The associations regarding BP levels could be attributed, at least partially, to reverse causality bias inherent to the cross-sectional design of the study.

Improving type 2 diabetes mellitus glycaemic control through lifestyle modification implementing diet intervention: a systematic review and meta-analysis.

PURPOSE: Type 2 diabetes mellitus represents a significant health problem. Many studies have reported that intensive nutritional intervention by itself or in addition to medications is the best method to improve glycaemic control in type 2 diabetes mellitus. However, in clinical practice, dietary education is not implemented as an integral part in the management of type 2 diabetes mellitus. The purpose of this systematic review and meta-analysis is to analyse the scientific evidence concerning the role of nutritional intervention in the glycaemic control of type 2 diabetes mellitus. METHODS: We searched Pubmed, Scopus, Cochrane Library and Web of Science databases from inception till May 2019 for randomised controlled trials (RCTs) that include dietary interventions in the management of patients with type 2 diabetes mellitus. RESULTS: A total of 28 studies were included. Our results demonstrated that lifestyle interventions significantly lowered glycosylated haemoglobin (HbA1c) levels compared to the usual care for patients with type 2 diabetes mellitus, overall weighted mean difference, WMD = - 0.51 (- 0.67, - 0.35). Strategies combining individualized and group-based activities were the most effective, WMD = - 0.95 (- 1.24, - 0.66). Most of stratified analyses did not totally resolve heterogeneity, but improvement in HbA1c levels has been consistently observed. CONCLUSIONS: The available evidence from RCTs shows that lifestyle intervention is more effective than the standard care regarding the glycaemic control of type 2 diabetic patients, particularly when there is a weight loss. It is time to translate this evidence to the primary health care practice. The protocol of the present systematic review was registered in PROSPERO, registration number CRD42018090469.

Fluid and total water intake in a senior mediterranean population at high cardiovascular risk: demographic and lifestyle determinants in the PREDIMED-Plus study.

PURPOSE: We aimed to evaluate associations between compliance with recommendations for total water intake (TWI) and total water intake from fluids (TWIF), and some socio-demographic and lifestyle factors of a senior Mediterranean population at high cardiovascular risk. METHODS: Cross-sectional analysis with data of 1902 participants from the PREDIMED-Plus study. A validated 32-item Spanish fluid-intake questionnaire was used to assess beverage consumption and water intake. Multivariable logistic regression models were used to assess the odds ratio (OR) and the 95% confidence interval (CI) for complying with European Food Safety Agency recommendations for TWI and TWIF according to various socio-demographic and lifestyle factors, and for the joint associations of Mediterranean diet (MedDiet) adherence and moderate-vigorous physical activity (MVPA). RESULTS: The mean total volume of fluid intake in the population studied was 1934 ± 617 mL/day. Water was the most frequently consumed beverage. Significant differences between sex were only observed in alcoholic and hot beverage consumption. Compliance with TWIF was associated with being women (OR 3.02; 2.40, 3.80), high adherence to MedDiet (OR 1.07; 1.02, 1.12), and participants who were more engaged in physical activity (PA) (OR 1.07; 1.02, 1.13). Age was inversely associated (OR 0.96; 0.94, 0.98). Similar results for TWI recommendations compliance were observed in relation to being women (OR 5.34; 3.85, 7.42), adherence to MedDiet (OR 1.16; 1.02, 1.31) and PA (OR 1.07; 1.00, 1.15). The joint association of PA and MedDiet, showed that participants with higher adherence to MedDiet and meeting WHO recommendations for MVPA complied better with the TWI recommendations (OR 1.66; 1.19, 2.32). CONCLUSIONS: High compliance with recommendations for TWI was associated with being a woman, and a healthy lifestyle characterized by high adherence to the MedDiet and PA.

Targeted estimation of heterogeneous treatment effect in observational survival analysis.

The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings. Since there are several reasons why the net benefit of intervention may differ across patients, current comparative effectiveness literature focuses on investigating heterogeneous treatment effect and predicting whether an individual might benefit from an intervention. The majority of this literature has concentrated on the estimation of the effect of treatment on binary outcomes. However, many medical interventions are evaluated in terms of their effect on future events, which are subject to loss to follow-up. In this study, we describe a framework for the estimation of heterogeneous treatment effect in terms of differences in time-to-event (survival) probabilities. We divide the problem into three phases: (1) estimation of treatment effect conditioned on unique sets of the covariate vector; (2) identification of features important for heterogeneity using non-parametric variable importance methods; and (3) estimation of treatment effect on the reference classes defined by the previously selected features, using one-step Targeted Maximum Likelihood Estimation. We conducted a series of simulation studies and found that this method performs well when either sample size or event rate is high enough and the number of covariates contributing to the effect heterogeneity is moderate. An application of this method to a clinical case study was conducted by estimating the effect of oral anticoagulants on newly diagnosed non-valvular atrial fibrillation patients using data from the UK Clinical Practice Research Datalink.

Isotemporal substitution of inactive time with physical activity and time in bed: cross-sectional associations with cardiometabolic health in the PREDIMED-Plus study.

BACKGROUND: This study explored the association between inactive time and measures of adiposity, clinical parameters, obesity, type 2 diabetes and metabolic syndrome components. It further examined the impact of reallocating inactive time to time in bed, light physical activity (LPA) or moderate-to-vigorous physical activity (MVPA) on cardio-metabolic risk factors, including measures of adiposity and body composition, biochemical parameters and blood pressure in older adults. METHODS: This is a cross-sectional analysis of baseline data from 2189 Caucasian men and women (age 55-75 years, BMI 27-40 Kg/m2) from the PREDIMED-Plus study (http://www.predimedplus.com/). All participants had ≥3 components of the metabolic syndrome. Inactive time, physical activity and time in bed were objectively determined using triaxial accelerometers GENEActiv during 7 days (ActivInsights Ltd., Kimbolton, United Kingdom). Multiple adjusted linear and logistic regression models were used. Isotemporal substitution regression modelling was performed to assess the relationship of replacing the amount of time spent in one activity for another, on each outcome, including measures of adiposity and body composition, biochemical parameters and blood pressure in older adults. RESULTS: Inactive time was associated with indicators of obesity and the metabolic syndrome. Reallocating 30 min per day of inactive time to 30 min per day of time in bed was associated with lower BMI, waist circumference and glycated hemoglobin (HbA1c) (all p-values < 0.05). Reallocating 30 min per day of inactive time with 30 min per day of LPA or MVPA was associated with lower BMI, waist circumference, total fat, visceral adipose tissue, HbA1c, glucose, triglycerides, and higher body muscle mass and HDL cholesterol (all p-values < 0.05). CONCLUSIONS: Inactive time was associated with a poor cardio-metabolic profile. Isotemporal substitution of inactive time with MVPA and LPA or time in bed could have beneficial impact on cardio-metabolic health. TRIAL REGISTRATION: The trial was registered at the International Standard Randomized Controlled Trial (ISRCTN: http://www.isrctn.com/ISRCTN89898870) with number 89898870 and registration date of 24 July 2014, retrospectively registered.

Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support.

BACKGROUND: Clinical predictive tools quantify contributions of relevant patient characteristics to derive likelihood of diseases or predict clinical outcomes. When selecting predictive tools for implementation at clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever-growing number of tools, most of which have never been implemented or assessed for comparative effectiveness. To overcome this challenge, we have developed a conceptual framework to Grade and Assess Predictive tools (GRASP) that can provide clinicians with a standardised, evidence-based system to support their search for and selection of efficient tools. METHODS: A focused review of the literature was conducted to extract criteria along which tools should be evaluated. An initial framework was designed and applied to assess and grade five tools: LACE Index, Centor Score, Well's Criteria, Modified Early Warning Score, and Ottawa knee rule. After peer review, by six expert clinicians and healthcare researchers, the framework and the grading of the tools were updated. RESULTS: GRASP framework grades predictive tools based on published evidence across three dimensions: 1) Phase of evaluation; 2) Level of evidence; and 3) Direction of evidence. The final grade of a tool is based on the highest phase of evaluation, supported by the highest level of positive evidence, or mixed evidence that supports a positive conclusion. Ottawa knee rule had the highest grade since it has demonstrated positive post-implementation impact on healthcare. LACE Index had the lowest grade, having demonstrated only pre-implementation positive predictive performance. CONCLUSION: GRASP framework builds on widely accepted concepts to provide standardised assessment and evidence-based grading of predictive tools. Unlike other methods, GRASP is based on the critical appraisal of published evidence reporting the tools' predictive performance before implementation, potential effect and usability during implementation, and their post-implementation impact. Implementing the GRASP framework as an online platform can enable clinicians and guideline developers to access standardised and structured reported evidence of existing predictive tools. However, keeping GRASP reports up-to-date would require updating tools' assessments and grades when new evidence becomes available, which can only be done efficiently by employing semi-automated methods for searching and processing the incoming information.

Grading and assessment of clinical predictive tools for paediatric head injury: a new evidence-based approach.

BACKGROUND: Many clinical predictive tools have been developed to diagnose traumatic brain injury among children and guide the use of computed tomography in the emergency department. It is not always feasible to compare tools due to the diversity of their development methodologies, clinical variables, target populations, and predictive performances. The objectives of this study are to grade and assess paediatric head injury predictive tools, using a new evidence-based approach, and to provide emergency clinicians with standardised objective information on predictive tools to support their search for and selection of effective tools. METHODS: Paediatric head injury predictive tools were identified through a focused review of literature. Based on the critical appraisal of published evidence about predictive performance, usability, potential effect, and post-implementation impact, tools were evaluated using a new framework for grading and assessment of predictive tools (GRASP). A comprehensive analysis was conducted to explain why certain tools were more successful. RESULTS: Fourteen tools were identified and evaluated. The highest-grade tool is PECARN; the only tool evaluated in post-implementation impact studies. PECARN and CHALICE were evaluated for their potential effect on healthcare, while the remaining 12 tools were only evaluated for predictive performance. Three tools; CATCH, NEXUS II, and Palchak, were externally validated. Three tools; Haydel, Atabaki, and Buchanich, were only internally validated. The remaining six tools; Da Dalt, Greenes, Klemetti, Quayle, Dietrich, and Güzel did not show sufficient internal validity for use in clinical practice. CONCLUSIONS: The GRASP framework provides clinicians with a high-level, evidence-based, comprehensive, yet simple and feasible approach to grade, compare, and select effective predictive tools. Comparing the three main tools which were assigned the highest grades; PECARN, CHALICE and CATCH, to the remaining 11, we find that the quality of tools' development studies, the experience and credibility of their authors, and the support by well-funded research programs were correlated with the tools' evidence-based assigned grades, and were more influential, than the sole high predictive performance, on the wide acceptance and successful implementation of the tools. Tools' simplicity and feasibility, in terms of resources needed, technical requirements, and training, are also crucial factors for their success.

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital.

BACKGROUND: The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. METHODS: A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. RESULTS: The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. CONCLUSIONS: This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.