Development and validation of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children

Background: Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children. Methods: Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model. Results: All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches. Conclusions: The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Von Willebrand Factor Improves Risk Prediction in Addition to N-Terminal Pro-B-type Natriuretic Peptide in Patients Referred to Coronary Angiography and Signs and Symptoms of Heart Failure and Preserved Ejection Fraction.

BACKGROUND Heart failure with preserved ejection fraction (HFpEF) represents a growing health burden associated with substantial mortality and morbidity. Consequently, risk prediction is of highest importance. Endothelial dysfunction has been recently shown to play an important role in the complex pathophysiology of HFpEF. We therefore aimed to assess von Willebrand factor (vWF), a marker of endothelial damage, as potential biomarker for risk assessment in patients with HFpEF. METHODS AND RESULTS Concentrations of vWF were assessed in 457 patients with HFpEF enrolled as part of the LUdwigshafen Risk and Cardiovascular Health (LURIC) study. All-cause mortality was observed in 40% of patients during a median follow-up time of 9.7 years. vWF significantly predicted mortality with a hazard ratio (HR) per increase of 1 SD of 1.45 (95% confidence interval, 1.26-1.68; P<0.001) and remained a significant predictor after adjustment for age, sex, body mass index, N-terminal pro-B-type natriuretic peptide (NT-proBNP), renal function, and frequent HFpEF-related comorbidities (adjusted HR per 1 SD, 1.22; 95% confidence interval, 1.05-1.42; P=0.001). Most notably, vWF showed additional prognostic value beyond that achievable with NT-proBNP indicated by improvements in C-Statistic (vWF×NT-proBNP: 0.65 versus NT-proBNP: 0.63; P for comparison, 0.004) and category-free net reclassification index (37.6%; P<0.001). CONCLUSIONS vWF is an independent predictor of long-term outcome in patients with HFpEF, which is in line with endothelial dysfunction as potential mediator in the pathophysiology of HFpEF. In particular, combined assessment of vWF and NT-proBNP improved risk prediction in this vulnerable group of patients.

Usefulness of electrocardiographic parameters for risk prediction in arrhythmogenic right ventricular dysplasia

The value of electrocardiographic findings predicting adverse outcome in patients with arrhythmogenic right ventricular dysplasia (ARVD) is not well known. We hypothesized that ventricular depolarization and repolarization abnormalities on the 12-lead surface electrocardiogram (ECG) predict adverse outcome in patients with ARVD. ECGs of 111 patients screened for the 2010 ARVD Task Force Criteria from 3 Swiss tertiary care centers were digitized and analyzed with a digital caliper by 2 independent observers blinded to the outcome. ECGs were compared in 2 patient groups: (1) patients with major adverse cardiovascular events (MACE: a composite of cardiac death, heart transplantation, survived sudden cardiac death, ventricular fibrillation, sustained ventricular tachycardia, or arrhythmic syncope) and (2) all remaining patients. A total of 51 patients (46%) experienced MACE during a follow-up period with median of 4.6 years (interquartile range 1.8 to 10.0). Kaplan-Meier analysis revealed reduced times to MACE for patients with repolarization abnormalities according to Task Force Criteria (p = 0.009), a precordial QRS amplitude ratio (∑QRS mV V1 to V3/∑QRS mV V1 to V6) of ≤ 0.48 (p = 0.019), and QRS fragmentation (p = 0.045). In multivariable Cox regression, a precordial QRS amplitude ratio of ≤ 0.48 (hazard ratio [HR] 2.92, 95% confidence interval [CI] 1.39 to 6.15, p = 0.005), inferior leads T-wave inversions (HR 2.44, 95% CI 1.15 to 5.18, p = 0.020), and QRS fragmentation (HR 2.65, 95% CI 1.1 to 6.34, p = 0.029) remained as independent predictors of MACE. In conclusion, in this multicenter, observational, long-term study, electrocardiographic findings were useful for risk stratification in patients with ARVD, with repolarization criteria, inferior leads TWI, a precordial QRS amplitude ratio of ≤ 0.48, and QRS fragmentation constituting valuable variables to predict adverse outcome.

Comparison of co-located independent ground-based middle atmospheric wind and temperature measurements with numerical weather prediction models

High-resolution, ground-based and independent observations including co-located wind radiometer, lidar stations, and infrasound instruments are used to evaluate the accuracy of general circulation models and data-constrained assimilation systems in the middle atmosphere at northern hemisphere midlatitudes. Systematic comparisons between observations, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses including the recent Integrated Forecast System cycles 38r1 and 38r2, the NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses, and the free-running climate Max Planck Institute–Earth System Model–Low Resolution (MPI-ESM-LR) are carried out in both temporal and spectral dom ains. We find that ECMWF and MERRA are broadly consistent with lidar and wind radiometer measurements up to ~40 km. For both temperature and horizontal wind components, deviations increase with altitude as the assimilated observations become sparser. Between 40 and 60 km altitude, the standard deviation of the mean difference exceeds 5 K for the temperature and 20 m/s for the zonal wind. The largest deviations are observed in winter when the variability from large-scale planetary waves dominates. Between lidar data and MPI-ESM-LR, there is an overall agreement in spectral amplitude down to 15–20 days. At shorter time scales, the variability is lacking in the model by ~10 dB. Infrasound observations indicate a general good agreement with ECWMF wind and temperature products. As such, this study demonstrates the potential of the infrastructure of the Atmospheric Dynamics Research Infrastructure in Europe project that integrates various measurements and provides a quantitative understanding of stratosphere-troposphere dynamical coupling for numerical weather prediction applications.

A meta-analysis of trabecular bone score in fracture risk prediction and its relationship to FRAX

Trabecular bone score (TBS) is a grey-level textural index of bone microarchitecture derived from lumbar spine dual-energy X-ray absorptiometry (DXA) images. TBS is a BMD-independent predictor of fracture risk. The objective of this meta-analysis was to determine whether TBS predicted fracture risk independently of FRAX probability and to examine their combined performance by adjusting the FRAX probability for TBS. We utilized individual level data from 17,809 men and women in 14 prospective population-based cohorts. Baseline evaluation included TBS and the FRAX risk variables and outcomes during follow up (mean 6.7 years) comprised major osteoporotic fractures. The association between TBS, FRAX probabilities and the risk of fracture was examined using an extension of the Poisson regression model in each cohort and for each sex and expressed as the gradient of risk (GR; hazard ratio per 1SD change in risk variable in direction of increased risk). FRAX probabilities were adjusted for TBS using an adjustment factor derived from an independent cohort (the Manitoba Bone Density Cohort). Overall, the GR of TBS for major osteoporotic fracture was 1.44 (95% CI: 1.35-1.53) when adjusted for age and time since baseline and was similar in men and women (p > 0.10). When additionally adjusted for FRAX 10-year probability of major osteoporotic fracture, TBS remained a significant, independent predictor for fracture (GR 1.32, 95%CI: 1.24-1.41). The adjustment of FRAX probability for TBS resulted in a small increase in the GR (1.76, 95%CI: 1.65, 1.87 vs. 1.70, 95%CI: 1.60-1.81). A smaller change in GR for hip fracture was observed (FRAX hip fracture probability GR 2.25 vs. 2.22). TBS is a significant predictor of fracture risk independently of FRAX. The findings support the use of TBS as a potential adjustment for FRAX probability, though the impact of the adjustment remains to be determined in the context of clinical assessment guidelines. This article is protected by copyright. All rights reserved.

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

The risk prediction for SPM and its impact on the survival in patients with head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth common malignancy in the world, with high rates of developing second primary malignancy (SPM) and moderately low survival rates. This disease has become an enormous challenge in the cancer research and treatments. For HNSCC patients, a highly significant cause of post-treatment mortality and morbidity is the development of SPM. Hence, assessment of predicting the risk for the development of SPM would be very helpful for patients, clinicians and policy makers to estimate the survival of patients with HNSCC. In this study, we built a prognostic model to predict the risk of developing SPM in patients with newly diagnosed HNSCC. The dataset used in this research was obtained from The University of Texas MD Anderson Cancer Center. For the first aim, we used stepwise logistic regression to identify the prognostic factors for the development of SPM. Our final model contained cancer site and overall cancer stage as our risk factors for SPM. The Hosmer-Lemeshow test (p-value= 0.15>0.05) showed the final prognostic model fit the data well. The area under the ROC curve was 0.72 that suggested the discrimination ability of our model was acceptable. The internal validation confirmed the prognostic model was a good fit and the final prognostic model would not over optimistically predict the risk of SPM. This model needs external validation by using large data sample size before it can be generalized to predict SPM risk for other HNSCC patients. For the second aim, we utilized a multistate survival analysis approach to estimate the probability of death for HNSCC patients taking into consideration of the possibility of SPM. Patients without SPM were associated with longer survival. These findings suggest that the development of SPM could be a predictor of survival rates among the patients with HNSCC.^

Sediment organic matter, grain size, and results of prediction models from northern North Atlantic and Arctic Seas

Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were conducted to collect hydrographic data and multicorer casts were conductd to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional geographic space. For these, two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over- and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future.

Neural bruise prediction models for fruit handling and machinery evaluation.

Some neural bruise prediction models have been implemented in the laboratory, for the most traded fruit species and varieties, allowing the prediction of the acceptability or rejectability for damages, with respect to the EC Standards. Different models have been built for both quasi-static (compression) and dynamic (impact) loads covering the whole commercial ripening period of fruits. A simulation process has been developed gathering the information on laboratory bruise models and load sensor calibrations for different electronic devices (IS-100 and DEA-1, for impact and compression loads respectively). Some evaluation methodology has been designed gathering the information on the mechanical properties of fruits and the loading records of electronic devices. The evaluation system allows to determine the current stage of fruit handling process and machinery.

Highway noise measurements for verification of prediction models. Final report.

Transportation Department, Office of the Assistant Secretary for Systems Development and Technology, Washington, D.C.