Poor performance of microbiological sampling in the prediction of recurrent arthroplasty infection

During a two-stage revision for prosthetic joint infections (PJI), joint aspirations, open tissue sampling and serum inflammatory markers are performed before re-implantation to exclude ongoing silent infection. We investigated the performance of these diagnostic procedures on the risk of recurrence of PJI among asymptomatic patients undergoing a two-stage revision. A total of 62 PJI were found in 58 patients. All patients had intra-operative surgical exploration during re-implantation, and 48 of them had intra-operative microbiological swabs. Additionally, 18 joint aspirations and one open biopsy were performed before second-stage reimplantation. Recurrence or persistence of PJI occurred in 12 cases with a mean delay of 218 days after re-implantation, but only four pre- or intraoperative invasive joint samples had grown a pathogen in cultures. In at least seven recurrent PJIs (58%), patients had a normal C-reactive protein (CRP, < 10 mg/l) level before re-implantation. The sensitivity, specificity, positive predictive and negative predictive values of pre-operative invasive joint aspiration and CRP for the prediction of PJI recurrence was 0.58, 0.88, 0.5, 0.84 and 0.17, 0.81, 0.13, 0.86, respectively. As a conclusion, pre-operative joint aspiration, intraoperative bacterial sampling, surgical exploration and serum inflammatory markers are poor predictors of PJI recurrence. The onset of reinfection usually occurs far later than reimplantation.

Comparison of the diagnostic performance of the original and modified Wells score in inpatients and outpatients with suspected deep vein thrombosis

The original and modified Wells score are widely used prediction rules for pre-test probability assessment of deep vein thrombosis (DVT). The objective of this study was to compare the predictive performance of both Wells scores in unselected patients with clinical suspicion of DVT.

Validation of the Asthma Predictive Index and comparison with simpler clinical prediction rules

Background The loose and stringent Asthma Predictive Indices (API), developed in Tucson, are popular rules to predict asthma in preschool children. To be clinically useful, they require validation in different settings. Objective To assess the predictive performance of the API in an independent population and compare it with simpler rules based only on preschool wheeze. Methods We studied 1954 children of the population-based Leicester Respiratory Cohort, followed up from age 1 to 10 years. The API and frequency of wheeze were assessed at age 3 years, and we determined their association with asthma at ages 7 and 10 years by using logistic regression. We computed test characteristics and measures of predictive performance to validate the API and compare it with simpler rules. Results The ability of the API to predict asthma in Leicester was comparable to Tucson: for the loose API, odds ratios for asthma at age 7 years were 5.2 in Leicester (5.5 in Tucson), and positive predictive values were 26% (26%). For the stringent API, these values were 8.2 (9.8) and 40% (48%). For the simpler rule early wheeze, corresponding values were 5.4 and 21%; for early frequent wheeze, 6.7 and 36%. The discriminative ability of all prediction rules was moderate (c statistic ≤ 0.7) and overall predictive performance low (scaled Brier score < 20%). Conclusion Predictive performance of the API in Leicester, although comparable to the original study, was modest and similar to prediction based only on preschool wheeze. This highlights the need for better prediction rules.

Prospective, multicenter validation of prediction scores for major bleeding in elderly patients with venous thromboembolism

The Outpatient Bleeding Risk Index (OBRI) and the Kuijer, RIETE and Kearon scores are clinical prognostic scores for bleeding in patients receiving oral anticoagulants for venous thromboembolism (VTE). We prospectively compared the performance of these scores in elderly patients with VTE.

Framingham risk score and alternatives for prediction of coronary heart disease in older adults

Background Guidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS. Methods Among 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization. Results During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS. Conclusions The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.

Prognostic clinical prediction rules to identify a low-risk pulmonary embolism: a systematic review and meta-analysis

Prognostic assessment is important for the management of patients with a pulmonary embolism (PE). A number of clinical prediction rules (CPRs) have been proposed for stratifying PE mortality risk. The aim of this systematic review was to assess the performance of prognostic CPRs in identifying a low-risk PE.

Prediction of first coronary events with the Framingham score: a systematic review

BACKGROUND: Uncertainty exists about the performance of the Framingham risk score when applied in different populations. OBJECTIVE: We assessed calibration of the Framingham risk score (ie, relationship between predicted and observed coronary event rates) in US and non-US populations free of cardiovascular disease. METHODS: We reviewed studies that evaluated the performance of the Framingham risk score to predict first coronary events in a validation cohort, as identified by Medline, EMBASE, BIOSIS, and Cochrane library searches (through August 2005). Two reviewers independently assessed 1496 studies for eligibility, extracted data, and performed quality assessment using predefined forms. RESULTS: We included 25 validation cohorts of different population groups (n = 128,000) in our main analysis. Calibration varied over a wide range from under- to overprediction of absolute risk by factors of 0.57 to 2.7. Risk prediction for 7 cohorts (n = 18658) from the United States, Australia, and New Zealand was well calibrated (corresponding figures: 0.87-1.08; for the 5 biggest cohorts). The estimated population risks for first coronary events were strongly associated (goodness of fit: R2 = 0.84) and in good agreement with observed risks (coefficient for predicted risk: beta = 0.84; 95% CI 0.41-1.26). In 18 European cohorts (n = 109499), the corresponding figures indicated close association (R2 = 0.72) but substantial overprediction (beta = 0.58, 95% CI 0.39-0.77). The risk score was well calibrated on the intercept for both population clusters. CONCLUSION: The Framingham score is well calibrated to predict first coronary events in populations from the United States, Australia, and New Zealand. Overestimation of absolute risk in European cohorts requires recalibration procedures.

A simple asthma prediction tool for preschool children with wheeze or cough

BACKGROUND Many preschool children have wheeze or cough, but only some have asthma later. Existing prediction tools are difficult to apply in clinical practice or exhibit methodological weaknesses. OBJECTIVE We sought to develop a simple and robust tool for predicting asthma at school age in preschool children with wheeze or cough. METHODS From a population-based cohort in Leicestershire, United Kingdom, we included 1- to 3-year-old subjects seeing a doctor for wheeze or cough and assessed the prevalence of asthma 5 years later. We considered only noninvasive predictors that are easy to assess in primary care: demographic and perinatal data, eczema, upper and lower respiratory tract symptoms, and family history of atopy. We developed a model using logistic regression, avoided overfitting with the least absolute shrinkage and selection operator penalty, and then simplified it to a practical tool. We performed internal validation and assessed its predictive performance using the scaled Brier score and the area under the receiver operating characteristic curve. RESULTS Of 1226 symptomatic children with follow-up information, 345 (28%) had asthma 5 years later. The tool consists of 10 predictors yielding a total score between 0 and 15: sex, age, wheeze without colds, wheeze frequency, activity disturbance, shortness of breath, exercise-related and aeroallergen-related wheeze/cough, eczema, and parental history of asthma/bronchitis. The scaled Brier scores for the internally validated model and tool were 0.20 and 0.16, and the areas under the receiver operating characteristic curves were 0.76 and 0.74, respectively. CONCLUSION This tool represents a simple, low-cost, and noninvasive method to predict the risk of later asthma in symptomatic preschool children, which is ready to be tested in other populations.

Finite element analysis for prediction of bone strength

Article preview View full access options BoneKEy Reports | Review Print Email Share/bookmark Finite element analysis for prediction of bone strength Philippe K Zysset, Enrico Dall'Ara, Peter Varga & Dieter H Pahr Affiliations Corresponding author BoneKEy Reports (2013) 2, Article number: 386 (2013) doi:10.1038/bonekey.2013.120 Received 03 January 2013 Accepted 25 June 2013 Published online 07 August 2013 Article tools Citation Reprints Rights & permissions Abstract Abstract• References• Author information Finite element (FE) analysis has been applied for the past 40 years to simulate the mechanical behavior of bone. Although several validation studies have been performed on specific anatomical sites and load cases, this study aims to review the predictability of human bone strength at the three major osteoporotic fracture sites quantified in recently completed in vitro studies at our former institute. Specifically, the performance of FE analysis based on clinical computer tomography (QCT) is compared with the ones of the current densitometric standards, bone mineral content, bone mineral density (BMD) and areal BMD (aBMD). Clinical fractures were produced in monotonic axial compression of the distal radii, vertebral sections and in side loading of the proximal femora. QCT-based FE models of the three bones were developed to simulate as closely as possible the boundary conditions of each experiment. For all sites, the FE methodology exhibited the lowest errors and the highest correlations in predicting the experimental bone strength. Likely due to the improved CT image resolution, the quality of the FE prediction in the peripheral skeleton using high-resolution peripheral CT was superior to that in the axial skeleton with whole-body QCT. Because of its projective and scalar nature, the performance of aBMD in predicting bone strength depended on loading mode and was significantly inferior to FE in axial compression of radial or vertebral sections but not significantly inferior to FE in side loading of the femur. Considering the cumulated evidence from the published validation studies, it is concluded that FE models provide the most reliable surrogates of bone strength at any of the three fracture sites.

Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used.The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9),Maybar (84. 0.57, 2.5),Megech (85, 0.15, 2.6), andWondoGenet (86, 0.52, 2.7) indicating that themodels were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

Complex visual hallucinations and attentional performance in eye disease and dementia: a test of the Perception and Attention Deficit model

OBJECTIVE This study aimed to test the prediction from the Perception and Attention Deficit model of complex visual hallucinations (CVH) that impairments in visual attention and perception are key risk factors for complex hallucinations in eye disease and dementia. METHODS Two studies ran concurrently to investigate the relationship between CVH and impairments in perception (picture naming using the Graded Naming Test) and attention (Stroop task plus a novel Imagery task). The studies were in two populations-older patients with dementia (n = 28) and older people with eye disease (n = 50) with a shared control group (n = 37). The same methodology was used in both studies, and the North East Visual Hallucinations Inventory was used to identify CVH. RESULTS A reliable relationship was found for older patients with dementia between impaired perceptual and attentional performance and CVH. A reliable relationship was not found in the population of people with eye disease. CONCLUSIONS The results add to previous research that object perception and attentional deficits are associated with CVH in dementia, but that risk factors for CVH in eye disease are inconsistent, suggesting that dynamic rather than static impairments in attentional processes may be key in this population.

Understanding the performance of the FLake model over two African Great Lakes

The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from similar to 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

Mobility and bandwidth prediction in virtualized LTE systems: Architecture and challenges

Long Term Evolution (LTE) represents the fourth generation (4G) technology which is capable of providing high data rates as well as support of high speed mobility. The EU FP7 Mobile Cloud Networking (MCN) project integrates the use of cloud computing concepts in LTE mobile networks in order to increase LTE's performance. In this way a shared distributed virtualized LTE mobile network is built that can optimize the utilization of virtualized computing, storage and network resources and minimize communication delays. Two important features that can be used in such a virtualized system to improve its performance are the user mobility and bandwidth prediction. This paper introduces the architecture and challenges that are associated with user mobility and bandwidth prediction approaches in virtualized LTE systems.

The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach

Both theoretically and empirically there is a continuous interest in understanding the specific relation between cognitive and motor development in childhood. In the present longitudinal study including three measurement points, this relation was targeted. At the beginning of the study, the participating children were 5-6-year-olds. By assessing participants' fine motor skills, their executive functioning, and their non-verbal intelligence, their cross-sectional and cross-lagged interrelations were examined. Additionally, performance in these three areas was used to predict early school achievement (in terms of mathematics, reading, and spelling) at the end of participants' first grade. Correlational analyses and structural equation modeling revealed that fine motor skills, non-verbal intelligence and executive functioning were significantly interrelated. Both fine motor skills and intelligence had significant links to later school achievement. However, when executive functioning was additionally included into the prediction of early academic achievement, fine motor skills and non-verbal intelligence were no longer significantly associated with later school performance suggesting that executive functioning plays an important role for the motor-cognitive performance link.