Tag: pubmed

J Thromb Thrombolysis. 2021 Aug 28. doi: 10.1007/s11239-021-02463-x. Online ahead of print.

ABSTRACT

RE-COVERY DVT/PE is a two-phase, international, observational study of anticoagulant therapy in patients with deep vein thrombosis and/or pulmonary embolism (DVT/PE). The objective of the second phase was to compare the safety and effectiveness of dabigatran versus a vitamin K antagonist (VKA) over 1 year of follow-up. Primary safety and effectiveness outcomes were major or clinically relevant nonmajor bleeding events (MBE/CRNMBEs) and symptomatic recurrent venous thromboembolism (VTE) (including deaths related to recurrent VTE). To minimize bias due to unbalanced patient characteristics, only patients in an overlapping range of estimated propensity scores were included (analytic set), and propensity score weighting was applied to compare outcomes. Outcome analysis used an as-treated approach, censoring patients after they stopped or switched their initial anticoagulant. Overall, 3009 patients enrolled from 2016 to 2018 were eligible: 60% were diagnosed with DVT alone, 21% with PE alone, and 19% with DVT plus PE. The analytic set consisted of 2969 patients. The incidence rate in %/year (95% confidence interval [CI]) of MBE/CRNMBEs was 2.63 (1.79-3.74) with dabigatran versus 4.48 (3.23-6.06) with warfarin; hazard ratio 0.63 (95% CI 0.32-1.25). For symptomatic recurrent nonfatal or fatal VTE the incidence rate was 1.53 (0.91-2.42) with dabigatran versus 2.01 (1.21-3.14) with VKAs; hazard ratio 0.78 (95% CI 0.30-2.02). In conclusion, we found lower annualized rates of MBE/CRNMBEs with dabigatran than VKA, although the difference was not statistically significant. Annualized rates of symptomatic VTE or related mortality were similar with dabigatran and VKA. These observational results with 1 year of follow-up reflect those of the randomized clinical trials. Trial registration: ClinicalTrials.gov identifier NCT02596230, first registered November 4, 2015.

PMID:34453675 | DOI:10.1007/s11239-021-02463-x

J Cardiovasc Transl Res. 2021 Aug 28. doi: 10.1007/s12265-021-10151-7. Online ahead of print.

ABSTRACT

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care. Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.

PMID:34453676 | DOI:10.1007/s12265-021-10151-7

Environ Sci Pollut Res Int. 2021 Aug 28. doi: 10.1007/s11356-021-16089-2. Online ahead of print.

ABSTRACT

Precise carbon price forecasting matters a lot for both regulators and investors. The improvement of carbon price forecasting can not only provide investors with rational advice but also make for energy conservation and emission reduction. But traditional methods do not perform well in prediction because of the nonlinearity and non-stationarity of carbon price. In this study, an innovative multiscale nonlinear integration model is proposed to improve the accuracy of carbon price forecasting, which combines optimal feature reconstruction and biphasic deep learning. For one thing, the optimal feature reconstruction, including variational mode decomposition (VMD) and sample entropy (SE), is used to extract different features from the original carbon price effectively. For another thing, biphasic deep learning based on deep recurrent neural network (DRNN) and gate recurrent unit (GRU) is applied to predict carbon price. DRNN, a novel framework of deep learning, is applied to predict each component. Meanwhile, GRU is used for nonlinear integration, and the final prediction of carbon price can be acquired through this procedure. For illustration and comparison, this study takes carbon price from Beijing, Hubei, and Shanghai in China as sample data to examine the capability of the proposed model. The empirical result proves that the new hybrid model can improve the carbon price predictive accuracy in consideration of statistical measurement. Hence, the novel hybrid model can be considered as an efficient way of predicting carbon prices.

PMID:34453680 | DOI:10.1007/s11356-021-16089-2

Biometrics. 2021 Aug 27. doi: 10.1111/biom.13555. Online ahead of print.

ABSTRACT

Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin’s combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the non-parametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinical trial. This article is protected by copyright. All rights reserved.

PMID:34453313 | DOI:10.1111/biom.13555

Eur J Clin Invest. 2021 Aug 28:e13674. doi: 10.1111/eci.13674. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to investigate the putative role of the Triglyceride-glucose index (TyG index) computed as ln[TG (mg/dL) × glucose (mg/dL)/2] and derived proxies as predictors of adiposity and weight loss changes after a low calorie diet (LCD) intervention.

METHODS: A total of 744 adult participants from the multicenter DIOGenes intervention study were prescribed a LCD (800 kcal/day) during 8 weeks. Body composition and fat content at baseline and after 8 weeks were estimated by DEXA/BIA. A multivariate analysis approach was used to estimate the difference in ∆Weight_1-2 (kg), ∆BMI_1-2 (kg/m² ) or ∆Fat_1-2 (%) between the basal value (point 1) and after 8 weeks following a LCD (point 2), respectively. The TyG index at baseline (TyG₁ ), after following the LCD for 8 weeks (TyG₂ ), or the TyG index differences between both time points (∆TyG_1-2 ) were analyzed as predictors of weight and fat changes.

RESULTS: TyG₁ was associated with ∆Weight_1-2 (kg) and ∆BMI_1-2 (kg/m² ), with β=0.812 (p=0.017) and β=0.265 (p=0.018), respectively. Also, TyG₂ values were inversely related to ∆Fat_1-2 (%), β=-1.473 (p=0.015). Moreover, ∆TyG_1-2 was associated with ∆Weight_1-2 (kg) and ∆Fat_1-2 (%), β= 0.689 (p=0.045) and β=1.764 (p=0.002), respectively. Furthermore, an association between TyG₂ and resistance to fat loss was found (p=0.015).

CONCLUSION: TyG₁ index is a good predictor of weight loss induced by LCD. Moreover, TyG₂ was closely related to resistance to fat loss, while ∆TyG_1-2 values were positively associated with body fat changes. Therefore, TyG index and derived estimations could be used as markers of individualized responses to energy restriction and a surrogate of body composition outcomes in clinical/epidemiological settings in obesity conditions.

PMID:34453322 | DOI:10.1111/eci.13674

Ther Innov Regul Sci. 2021 Aug 27. doi: 10.1007/s43441-021-00335-3. Online ahead of print.

ABSTRACT

BACKGROUND: In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced quality tolerance limits (QTLs) as a quality control in clinical trials. Previously, TransCelerate proposed a framework for QTL implementation and parameters. Historical data can be important in helping to determine QTL thresholds in new clinical trials.

METHODS: This article presents results of historical data analyses for the previously proposed parameters based on data from 294 clinical trials from seven TransCelerate member companies. The differences across therapeutic areas were assessed by comparing Alzheimer’s disease (AD) and oncology trials using a separate dataset provided by Medidata.

RESULTS: TransCelerate member companies provided historical data on 11 QTL parameters with data sufficient for analysis for parameters. The distribution of values was similar for most parameters with a relatively small number of outlying trials with high parameter values. Medidata provided values for three parameters in a total of 45 AD and oncology trials with no obvious differences between the therapeutic areas.

CONCLUSION: Historical parameter values can provide helpful benchmark information for quality control activities in future trials.

PMID:34453269 | DOI:10.1007/s43441-021-00335-3

Biol Trace Elem Res. 2021 Aug 27. doi: 10.1007/s12011-021-02897-7. Online ahead of print.

ABSTRACT

The existing evidence on the relationships of serum zinc, copper, and zinc/copper ratio with sleep duration is limited and conflicting. The present cross-sectional study aimed to investigate these associations in general adults by utilizing data from the 2011-2016 National Health and Nutrition Examination Survey. The concentrations of zinc and copper were measured in serum samples. Sleep duration (self-reported usual sleep duration) was categorized as < 7 h/night (short sleep duration), 7-8 h/night (optimal sleep duration), and > 8 h/night (long sleep duration). Multinomial logistic regression models and restricted cubic splines were constructed to examine the associations of serum zinc, copper, and zinc/copper ratio with sleep duration. A total of 5067 adults were included. After multivariate adjustment, compared with the optimal sleep duration group, the odds ratios (ORs) (95% confidence intervals, CIs) in the long sleep duration group for the highest versus lowest quartile of serum zinc concentration and zinc/copper ratio were 0.61 (0.39-0.96) and 0.58 (0.38-0.89), respectively. Furthermore, among males, the OR (95% CI) of long sleep duration for the highest versus lowest quartile of serum copper concentration was 2.23 (1.15-4.32). Finally, the dose-response trends suggested that participants with optimal sleep duration had the highest serum zinc concentration and zinc/copper ratio and a slightly lower serum copper concentration. No significant association was found between serum zinc, copper concentrations and the zinc/copper ratio and short sleep duration. In conclusion, serum zinc and zinc/copper ratio were inversely related to long sleep duration in adults, while serum copper was positively associated with long sleep duration in males.

PMID:34453310 | DOI:10.1007/s12011-021-02897-7

Environ Sci Pollut Res Int. 2021 Aug 27. doi: 10.1007/s11356-021-16110-8. Online ahead of print.

ABSTRACT

Environmental challenges are as vast as the universe, allowing for numerous studies on their various dimensions. Using 17 data sets from Asia-Pacific countries between 2000 and 2017, this study attempted to investigate the economic factors influencing the ecological footprint of the fishing sector. The primary contribution of this study is to examine the effects of nine economic freedom indicators, as well as other control variables, on the status of fishery resources due to environmental pressure. The findings confirm the environmental Kuznets curve (EKC) hypothesis in the fishing grounds footprint, indicating that GDP per capita growth has a positive and significant effect, even though its squared form coefficient is negative. Other control variables, including natural resource rents, urbanization, and energy intensity, do not significantly affect the fishing footprint. The different components of economic freedom show different effects, while their cumulative effects in the form of the total economic freedom index positively affect the footprint of fishing and lead to increased extraction from fishing resources. The results show that the government integrity, tax burden, business freedom, and monetary freedom indices increase the fishing footprint. In contrast, indices of trade freedom and investment freedom, by highlighting the adverse effects of fishing on the environment, help countries reduce pressure on their aquatic resources. The findings of this study highlight the importance of examining how various dimensions of economic freedom affect the ability to manage fishery resources effectively.

PMID:34453249 | DOI:10.1007/s11356-021-16110-8

J Immigr Minor Health. 2021 Aug 28. doi: 10.1007/s10903-021-01265-w. Online ahead of print.

ABSTRACT

Considering the critical role of oral health on people’s well-being, access to regular dental care to improve oral health may be a useful medium for improving immigrant integration and settlement in Canada. Using the 2013-14 Canadian Community Health Survey, this study contributes to the literature and policy by examining if there are disparities in regular utilization of dental care among recent immigrants, established immigrants, and the native-born in Ontario, Canada. Adopting Andersen’s behavioural model of health services use as a conceptual framework, we introduce three sets of variables in our statistical analysis including predisposing, need, and enabling factors. At the bivariate level, recent (OR = 0.42, p < 0.001) and established immigrants (OR = 0.81, p < 0.001) are less likely to use dental care at least once a year than their native-born counterparts. Once accounting for enabling characteristics, however, we observe that the direction of the association becomes positive for established immigrants (OR = 1.15, p < 0.05). The difference between recent immigrants and the native-born is partially attenuated when we control for enabling characteristics but remains statistically significant (OR = 0.73, p < 0.05). Based on these findings, we provide several implications for policymakers and future research.

PMID:34453263 | DOI:10.1007/s10903-021-01265-w

Clin Res Cardiol. 2021 Aug 28. doi: 10.1007/s00392-021-01925-9. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate associations of omega-3 fatty acid (O3-FA) blood levels with cardiometabolic risk markers, functional capacity and cardiac function/morphology in patients with heart failure with preserved ejection fraction (HFpEF).

BACKGROUND: O3-FA have been linked to reduced risk for HF and associated phenotypic traits in experimental/clinical studies.

METHODS: This is a cross-sectional analysis of data from the Aldo-DHF-RCT. From 422 patients, the omega-3-index (O3I = EPA + DHA) was analyzed at baseline in n = 404 using the HS-Omega-3-Index^® methodology. Patient characteristics were; 67 ± 8 years, 53% female, NYHA II/III (87/13%), ejection fraction ≥ 50%, E/e’ 7.1 ± 1.5; median NT-proBNP 158 ng/L (IQR 82-298). Pearson’s correlation coefficient and multiple linear regression analyses, using sex and age as covariates, were used to describe associations of the O3I with metabolic phenotype, functional capacity, echocardiographic markers for LVDF, and neurohumoral activation at baseline/12 months.

RESULTS: The O3I was below (< 8%), within (8-11%), and higher (> 11%) than the target range in 374 (93%), 29 (7%), and 1 (0.2%) patients, respectively. Mean O3I was 5.7 ± 1.7%. The O3I was inversely associated with HbA1c (r = – 0.139, p = 0.006), triglycerides-to-HDL-C ratio (r = – 0.12, p = 0.017), triglycerides (r = – 0.117, p = 0.02), non-HDL-C (r = – 0.101, p = 0.044), body-mass-index (r = – 0.149, p = 0.003), waist circumference (r = – 0.121, p = 0.015), waist-to-height ratio (r = – 0.141, p = 0.005), and positively associated with submaximal aerobic capacity (r = 0.113, p = 0.023) and LVEF (r = 0.211, p < 0.001) at baseline. Higher O3I at baseline was predictive of submaximal aerobic capacity (β = 15.614, p < 0,001), maximal aerobic capacity (β = 0.399, p = 0.005) and LVEF (β = 0.698, p = 0.007) at 12 months.

CONCLUSIONS: Higher O3I was associated with a more favorable cardiometabolic risk profile and predictive of higher submaximal/maximal aerobic capacity and lower BMI/truncal adiposity in HFpEF patients. Omega-3 fatty acid blood levels are inversely associated with cardiometabolic risk factors in HFpEF patients. Higher O3I was associated with a more favorable cardiometabolic risk profile and aerobic capacity (left) but did not correlate with echocardiographic markers for left ventricular diastolic function or neurohumoral activation (right). An O3I-driven intervention trial might be warranted to answer the question whether O3-FA in therapeutic doses (with the target O3I 8-11%) impact on echocardiographic markers for left ventricular diastolic function and neurohumoral activation in patients with HFpEF. This figure contains modified images from Servier Medical Art ( https://smart.servier.com ) licensed by a Creative Commons Attribution 3.0 Unported License.

PMID:34453204 | DOI:10.1007/s00392-021-01925-9