Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning.

Activity monitors based on accelerometry are used to predict the speed and energy cost of walking at 0% slope, but not at other inclinations. Parallel measurements of body accelerations and altitude variation were studied to determine whether walking speed prediction could be improved. Fourteen subjects walked twice along a 1.3 km circuit with substantial slope variations (-17% to +17%). The parameters recorded were body acceleration using a uni-axial accelerometer, altitude variation using differential barometry, and walking speed using satellite positioning (DGPS). Linear regressions were calculated between acceleration and walking speed, and between acceleration/altitude and walking speed. These predictive models, calculated using the data from the first circuit run, were used to predict speed during the second circuit. Finally the predicted velocity was compared with the measured one. The result was that acceleration alone failed to predict speed (mean r = 0.4). Adding altitude variation improved the prediction (mean r = 0.7). With regard to the altitude/acceleration-speed relationship, substantial inter-individual variation was found. It is concluded that accelerometry, combined with altitude measurement, can assess position variations of humans provided inter-individual variation is taken into account. It is also confirmed that DGPS can be used for outdoor walking speed measurements, opening up new perspectives in the field of biomechanics.

V3 net charge: additional tool in HIV-1 tropism prediction.

Genotype-based algorithms are valuable tools for the identification of patients eligible for CCR5 inhibitors administration in clinical practice. Among the available methods, geno2pheno[coreceptor] (G2P) is the most used online tool for tropism prediction. This study was conceived to assess if the combination of G2P prediction with V3 peptide net charge (NC) value could improve the accuracy of tropism prediction. A total of 172 V3 bulk sequences from 143 patients were analyzed by G2P and NC values. A phenotypic assay was performed by cloning the complete env gene and tropism determination was assessed on U87_CCR5(+)/CXCR4(+) cells. Sequences were stratified according to the agreement between NC values and G2P results. Of sequences predicted as X4 by G2P, 61% showed NC values higher than 5; similarly, 76% of sequences predicted as R5 by G2P had NC values below 4. Sequences with NC values between 4 and 5 were associated with different G2P predictions: 65% of samples were predicted as R5-tropic and 35% of sequences as X4-tropic. Sequences identified as X4 by NC value had at least one positive residue at positions known to be involved in tropism prediction and positive residues in position 32. These data supported the hypothesis that NC values between 4 and 5 could be associated with the presence of dual/mixed-tropic (DM) variants. The phenotypic assay performed on a subset of sequences confirmed the tropism prediction for concordant sequences and showed that NC values between 4 and 5 are associated with DM tropism. These results suggest that the combination of G2P and NC could increase the accuracy of tropism prediction. A more reliable identification of X4 variants would be useful for better selecting candidates for Maraviroc (MVC) administration, but also as a predictive marker in coreceptor switching, strongly associated with the phase of infection.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.

New prognostic factors and calculators for outcome prediction in patients with recurrent glioblastoma: A pooled analysis of EORTC Brain Tumour Group phase I and II clinical trials.

BACKGROUND: Prognostic models have been developed to predict survival of patients with newly diagnosed glioblastoma (GBM). To improve predictions, models should be updated with information at the recurrence. We performed a pooled analysis of European Organization for Research and Treatment of Cancer (EORTC) trials on recurrent glioblastoma to validate existing clinical prognostic factors, identify new markers, and derive new predictions for overall survival (OS) and progression free survival (PFS).¦METHODS: Data from 300 patients with recurrent GBM recruited in eight phase I or II trials conducted by the EORTC Brain Tumour Group were used to evaluate patient's age, sex, World Health Organisation (WHO) performance status (PS), presence of neurological deficits, disease history, use of steroids or anti-epileptics and disease characteristics to predict PFS and OS. Prognostic calculators were developed in patients initially treated by chemoradiation with temozolomide.¦RESULTS: Poor PS and more than one target lesion had a significant negative prognostic impact for both PFS and OS. Patients with large tumours measured by the maximum diameter of the largest lesion (⩾42mm) and treated with steroids at baseline had shorter OS. Tumours with predominant frontal location had better survival. Age and sex did not show independent prognostic values for PFS or OS.¦CONCLUSIONS: This analysis confirms performance status but not age as a major prognostic factor for PFS and OS in recurrent GBM. Patients with multiple and large lesions have an increased risk of death. With these data prognostic calculators with confidence intervals for both medians and fixed time probabilities of survival were derived.

Assessment of cytomegalovirus-specific cell-mediated immunity for the prediction of cytomegalovirus disease in high-risk solid-organ transplant recipients: a multicenter cohort study.

BACKGROUND: Cytomegalovirus (CMV) disease remains an important problem in solid-organ transplant recipients, with the greatest risk among donor CMV-seropositive, recipient-seronegative (D(+)/R(-)) patients. CMV-specific cell-mediated immunity may be able to predict which patients will develop CMV disease. METHODS: We prospectively included D(+)/R(-) patients who received antiviral prophylaxis. We used the Quantiferon-CMV assay to measure interferon-γ levels following in vitro stimulation with CMV antigens. The test was performed at the end of prophylaxis and 1 and 2 months later. The primary outcome was the incidence of CMV disease at 12 months after transplant. We calculated positive and negative predictive values of the assay for protection from CMV disease. RESULTS: Overall, 28 of 127 (22%) patients developed CMV disease. Of 124 evaluable patients, 31 (25%) had a positive result, 81 (65.3%) had a negative result, and 12 (9.7%) had an indeterminate result (negative mitogen and CMV antigen) with the Quantiferon-CMV assay. At 12 months, patients with a positive result had a subsequent lower incidence of CMV disease than patients with a negative and an indeterminate result (6.4% vs 22.2% vs 58.3%, respectively; P < .001). Positive and negative predictive values of the assay for protection from CMV disease were 0.90 (95% confidence interval [CI], .74-.98) and 0.27 (95% CI, .18-.37), respectively. CONCLUSIONS: This assay may be useful to predict if patients are at low, intermediate, or high risk for the development of subsequent CMV disease after prophylaxis. CLINICAL TRIALS REGISTRATION: NCT00817908.

Cardiovascular Risk Prediction with Ultrasound

This paper addresses primary care physicians, cardiologists, internists, angiologists and doctors desirous of improving vascular risk prediction in primary care. Many cardiovascular risk factors act aggressively on the arterial wall and result in atherosclerosis and atherothrombosis. Cardiovascular prognosis derived from ultrasound imaging is, however, excellent in subjects without formation of intimal thickening or atheromas. Since ultrasound visualises the arterial wall directly, the information derived from the arterial wall may add independent incremental information to the knowledge of risk derived from global risk assessment. This paper provides an overview on plaque imaging for vascular risk prediction in two parts: Part 1: Carotid IMT is frequently used as a surrogate marker for outcome in intervention studies addressing rather large cohorts of subjects. Carotid IMT as a risk prediction tool for the prevention of acute myocardial infarction and stroke has been extensively studied in many patients since 1987, and has yielded incremental hazard ratios for these cardiovascular events independently of established cardiovascular risk factors. However, carotid IMT measurements are not used uniformly and therefore still lack widely accepted standardisation. Hence, at an individual, practicebased level, carotid IMT is not recommended as a risk assessment tool. The total plaque area of the carotid arteries (TPA) is a measure of the global plaque burden within both carotid arteries. It was recently shown in a large Norwegian cohort involving over 6000 subjects that TPA is a very good predictor for future myocardial infarction in women with an area under the curve (AUC) using a receiver operating curves (ROC) value of 0.73 (in men: 0.63). Further, the AUC for risk prediction is high both for vascular death in a vascular prevention clinic group (AUC 0.77) and fatal or nonfatal myocardial infarction in a true primary care group (AUC 0.79). Since TPA has acceptable reproducibility, allows calculation of posttest risk and is easily obtained at low cost, this risk assessment tool may come in for more widespread use in the future and also serve as a tool for atherosclerosis tracking and guidance for intensity of preventive therapy. However, more studies with TPA are needed. Part 2: Carotid and femoral plaque formation as detected by ultrasound offers a global view of the extent of atherosclerosis. Several prospective cohort studies have shown that cardiovascular risk prediction is greater for plaques than for carotid IMT. The number of arterial beds affected by significant atheromas may simply be added numerically to derive additional information on the risk of vascular events. A new atherosclerosis burden score (ABS) simply calculates the sum of carotid and femoral plaques encountered during ultrasound scanning. ABS correlates well and independently with the presence of coronary atherosclerosis and stenosis as measured by invasive coronary angiogram. However, the prognostic power of ABS as an independent marker of risk still needs to be elucidated in prospective studies. In summary, the large number of ways to measure atherosclerosis and related changes in human arteries by ultrasound indicates that this technology is not yet sufficiently perfected and needs more standardisation and workup on clearly defined outcome studies before it can be recommended as a practice-based additional risk modifier.

On the evolution of harming and recognition in finite panmictic and infinite structured populations.

Natural selection may favor two very different types of social behaviors that have costs in vital rates (fecundity and/or survival) to the actor: helping behaviors, which increase the vital rates of recipients, and harming behaviors, which reduce the vital rates of recipients. Although social evolutionary theory has mainly dealt with helping behaviors, competition for limited resources creates ecological conditions in which an actor may benefit from expressing behaviors that reduce the vital rates of neighbors. This may occur if the reduction in vital rates decreases the intensity of competition experienced by the actor or that experienced by its offspring. Here, we explore the joint evolution of neutral recognition markers and marker-based costly conditional harming whereby actors express harming, conditional on actor and recipient bearing different conspicuous markers. We do so for two complementary demographic scenarios: finite panmictic and infinite structured populations. We find that marker-based conditional harming can evolve under a large range of recombination rates and group sizes under both finite panmictic and infinite structured populations. A direct comparison with results for the evolution of marker-based conditional helping reveals that, if everything else is equal, marker-based conditional harming is often more likely to evolve than marker-based conditional helping.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Prediction of hemodynamic severity of coarctation by magnetic resonance imaging.

A published formula containing minimal aortic cross-sectional area and the flow deceleration pattern in the descending aorta obtained by cardiovascular magnetic resonance predicts significant coarctation of the aorta (CoA). However, the existing formula is complicated to use in clinical practice and has not been externally validated. Consequently, its clinical utility has been limited. The aim of this study was to derive a simple and clinically practical algorithm to predict severe CoA from data obtained by cardiovascular magnetic resonance. Seventy-nine consecutive patients who underwent cardiovascular magnetic resonance and cardiac catheterization for the evaluation of native or recurrent CoA at Children's Hospital Boston (n = 30) and the University of California, San Francisco (n = 49), were retrospectively reviewed. The published formula derived from data obtained at Children's Hospital Boston was first validated from data obtained at the University of California, San Francisco. Next, pooled data from the 2 institutions were analyzed, and a refined model was created using logistic regression methods. Finally, recursive partitioning was used to develop a clinically practical prediction tree to predict transcatheter systolic pressure gradient ≥ 20 mm Hg. Severe CoA was present in 48 patients (61%). Indexed minimal aortic cross-sectional area and heart rate-corrected flow deceleration time in the descending aorta were independent predictors of CoA gradient ≥ 20 mm Hg (p <0.01 for both). A prediction tree combining these variables reached a sensitivity and specificity of 90% and 76%, respectively. In conclusion, the presented prediction tree on the basis of cutoff values is easy to use and may help guide the management of patients investigated for CoA.