Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Biodiversity effects on nitrate concentrations in soil solution: a Bayesian model

Ecosystems are faced with high rates of species loss which has consequences for their functions and services. To assess the effects of plant species diversity on the nitrogen (N) cycle, we developed a model for monthly mean nitrate (NO3-N) concentrations in soil solution in 0-30 cm mineral soil depth using plant species and functional group richness and functional composition as drivers and assessing the effects of conversion of arable land to grassland, spatially heterogeneous soil properties, and climate. We used monthly mean NO3-N concentrations from 62 plots of a grassland plant diversity experiment from 2003 to 2006. Plant species richness (1-60) and functional group composition (1-4 functional groups: legumes, grasses, non-leguminous tall herbs, non-leguminous small herbs) were manipulated in a factorial design. Plant community composition, time since conversion from arable land to grassland, soil texture, and climate data (precipitation, soil moisture, air and soil temperature) were used to develop one general Bayesian multiple regression model for the 62 plots to allow an in-depth evaluation using the experimental design. The model simulated NO3-N concentrations with an overall Bayesian coefficient of determination of 0.48. The temporal course of NO3-N concentrations was simulated differently well for the individual plots with a maximum plot-specific Nash-Sutcliffe Efficiency of 0.57. The model shows that NO3-N concentrations decrease with species richness, but this relation reverses if more than approx. 25 % of legume species are included in the mixture. Presence of legumes increases and presence of grasses decreases NO3-N concentrations compared to mixtures containing only small and tall herbs. Altogether, our model shows that there is a strong influence of plant community composition on NO3-N concentrations.

The nasal microbiota in infants with cystic fibrosis in the first year of life: a prospective cohort study.

BACKGROUND Respiratory tract infections and subsequent airway inflammation occur early in the life of infants with cystic fibrosis. However, detailed information about the microbial composition of the respiratory tract in infants with this disorder is scarce. We aimed to undertake longitudinal in-depth characterisation of the upper respiratory tract microbiota in infants with cystic fibrosis during the first year of life. METHODS We did this prospective cohort study at seven cystic fibrosis centres in Switzerland. Between Feb 1, 2011, and May 31, 2014, we enrolled 30 infants with a diagnosis of cystic fibrosis. Microbiota characterisation was done with 16S rRNA gene pyrosequencing and oligotyping of nasal swabs collected every 2 weeks from the infants with cystic fibrosis. We compared these data with data for an age-matched cohort of 47 healthy infants. We additionally investigated the effect of antibiotic treatment on the microbiota of infants with cystic fibrosis. Statistical methods included regression analyses with a multivariable multilevel linear model with random effects to correct for clustering on the individual level. FINDINGS We analysed 461 nasal swabs taken from the infants with cystic fibrosis; the cohort of healthy infants comprised 872 samples. The microbiota of infants with cystic fibrosis differed compositionally from that of healthy infants (p=0·001). This difference was also found in exclusively antibiotic-naive samples (p=0·001). The disordering was mainly, but not solely, due to an overall increase in the mean relative abundance of Staphylococcaceae in infants with cystic fibrosis compared with healthy infants (multivariable linear regression model stratified by age and adjusted for season; second month: coefficient 16·2 [95% CI 0·6-31·9]; p=0·04; third month: 17·9 [3·3-32·5]; p=0·02; fourth month: 21·1 [7·8-34·3]; p=0·002). Oligotyping analysis enabled differentiation between Staphylococcus aureus and coagulase-negative Staphylococci. Whereas the analysis showed a decrease in S aureus at and after antibiotic treatment, coagulase-negative Staphylococci increased. INTERPRETATION Our study describes compositional differences in the microbiota of infants with cystic fibrosis compared with healthy controls, and disordering of the microbiota on antibiotic administration. Besides S aureus, coagulase-negative Staphylococci also contributed to the disordering identified in these infants. These findings are clinically important in view of the crucial role that bacterial pathogens have in the disease progression of cystic fibrosis in early life. Our findings could be used to inform future studies of the effect of antibiotic treatment on the microbiota in infants with cystic fibrosis, and could assist in the prevention of early disease progression in infants with this disorder. FUNDING Swiss National Science Foundation, Fondation Botnar, the Swiss Society for Cystic Fibrosis, and the Swiss Lung Association Bern.

Comparison of two scoring systems for evaluation of treatment outcome in patients with complete bilateral cleft lip and palate

Objective : To compare two scoring systems: the Huddart/Bodenham system (HB system) and the Bauru-BCLP yardstick (BCLP yardstick), which classify treatment outcome in terms of dental arch relationships in patients with complete bilateral cleft lip and palate (CBCLP). The predictive value of these scoring systems for treatment outcome was also evaluated. Design : Retrospective longitudinal study. Patients : Dental arch relationships of 43 CBCLP patients were evaluated at 6, 9, and 12 years. Setting : Treatment outcome in BCLP patients using two scoring systems. Main Outcome Measures : For each age group, the HB scores were correlated with the BCLP yardstick scores using Spearman's correlation coefficient. The predictive value of the two scoring systems was evaluated by backward regression analysis. Results : Intraobserver Kappa values for the BCLP yardstick scoring for the two observers were .506 and .627, respectively, and the interobserver reliability ranged from .427 and .581. The intraobserver reliability for the HB system ranged from .92 to .97 and the interobserver reliability from .88 to .96. The BCLP yardstick scores of 6 and 9 years together were predictors for the outcome at 12 years (explained variance 41.3%). Adding the incisor and lateral HB scores in the regression model increased the explained variance to 67%. Conclusions : The BCLP yardstick and the HB system are reliable scoring systems for evaluation of dental arch relationships of CBCLP patients. The HB system categorizes treatment outcome into similar categories as the BCLP yardstick. In case a more sensitive measure of treatment outcome is needed, selectively both scoring systems should be used.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

Adjudication-related processes are underreported and lack standardization in clinical trials of venous thromboembolism: a systematic review

OBJECTIVES Although the use of an adjudication committee (AC) for outcomes is recommended in randomized controlled trials, there are limited data on the process of adjudication. We therefore aimed to assess whether the reporting of the adjudication process in venous thromboembolism (VTE) trials meets existing quality standards and which characteristics of trials influence the use of an AC. STUDY DESIGN AND SETTING We systematically searched MEDLINE and the Cochrane Library from January 1, 2003, to June 1, 2012, for randomized controlled trials on VTE. We abstracted information about characteristics and quality of trials and reporting of adjudication processes. We used stepwise backward logistic regression model to identify trial characteristics independently associated with the use of an AC. RESULTS We included 161 trials. Of these, 68.9% (111 of 161) reported the use of an AC. Overall, 99.1% (110 of 111) of trials with an AC used independent or blinded ACs, 14.4% (16 of 111) reported how the adjudication decision was reached within the AC, and 4.5% (5 of 111) reported on whether the reliability of adjudication was assessed. In multivariate analyses, multicenter trials [odds ratio (OR), 8.6; 95% confidence interval (CI): 2.7, 27.8], use of a data safety-monitoring board (OR, 3.7; 95% CI: 1.2, 11.6), and VTE as the primary outcome (OR, 5.7; 95% CI: 1.7, 19.4) were associated with the use of an AC. Trials without random allocation concealment (OR, 0.3; 95% CI: 0.1, 0.8) and open-label trials (OR, 0.3; 95% CI: 0.1, 1.0) were less likely to report an AC. CONCLUSION Recommended processes of adjudication are underreported and lack standardization in VTE-related clinical trials. The use of an AC varies substantially by trial characteristics.

Hypofibrinolysis in type 2 diabetes: the role of the inflammatory pathway and complement C3

AIMS/HYPOTHESIS Plasminogen activator inhibitor-1 (PAI-1) has been regarded as the main antifibrinolytic protein in diabetes, but recent work indicates that complement C3 (C3), an inflammatory protein, directly compromises fibrinolysis in type 1 diabetes. The aim of the current project was to investigate associations between C3 and fibrinolysis in a large cohort of individuals with type 2 diabetes. METHODS Plasma levels of C3, C-reactive protein (CRP), PAI-1 and fibrinogen were analysed by ELISA in 837 patients enrolled in the Edinburgh Type 2 Diabetes Study. Fibrin clot lysis was analysed using a validated turbidimetric assay. RESULTS Clot lysis time correlated with C3 and PAI-1 plasma levels (r = 0.24, p < 0.001 and r = 0.22, p < 0.001, respectively). In a multivariable regression model involving age, sex, BMI, C3, PAI-1, CRP and fibrinogen, and using log-transformed data as appropriate, C3 was associated with clot lysis time (regression coefficient 0.227 [95% CI 0.161, 0.292], p < 0.001), as was PAI-1 (regression coefficient 0.033 [95% CI 0.020, 0.064], p < 0.05) but not fibrinogen (regression coefficient 0.003 [95% CI -0.046, 0.051], p = 0.92) or CRP (regression coefficient 0.024 [95% CI -0.008, 0.056], p = 0.14). No correlation was demonstrated between plasma levels of C3 and PAI-1 (r = -0.03, p = 0.44), consistent with previous observations that the two proteins affect different pathways in the fibrinolytic system. CONCLUSIONS/INTERPRETATION Similarly to PAI-1, C3 plasma levels are independently associated with fibrin clot lysis in individuals with type 2 diabetes. Therefore, future studies should analyse C3 plasma levels as a surrogate marker of fibrinolysis potential in this population.

WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.

Predicting adverse events in children with fever and chemotherapy-induced neutropenia: the prospective multicenter SPOG 2003 FN study

PURPOSE To develop a score predicting the risk of adverse events (AEs) in pediatric patients with cancer who experience fever and neutropenia (FN) and to evaluate its performance. PATIENTS AND METHODS Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of future AEs (ie, serious medical complication, microbiologically defined infection, radiologically confirmed pneumonia) was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. Results An AE was reported in 122 (29%) of 423 FN episodes. In 57 episodes (13%), the first AE was known only after reassessment after 8 to 24 hours of inpatient management. Predicting AE at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The score predicting future AE in 358 episodes without known AE at reassessment used the following four variables: preceding chemotherapy more intensive than acute lymphoblastic leukemia maintenance (weight = 4), hemoglobin > or = 90 g/L (weight = 5), leukocyte count less than 0.3 G/L (weight = 3), and platelet count less than 50 G/L (weight = 3). A score (sum of weights) > or = 9 predicted future AEs. The cross-validated performance of this score exceeded the performance of published risk prediction rules. At an overall sensitivity of 92%, 35% of the episodes were classified as low risk, with a specificity of 45% and a negative predictive value of 93%. CONCLUSION This score, based on four routinely accessible characteristics, accurately identifies pediatric patients with cancer with FN at risk for AEs after reassessment.

Prediction of Psychosis by Mismatch Negativity

BACKGROUND: To develop risk-adapted prevention of psychosis, an accurate estimation of the individual risk of psychosis at a given time is needed. Inclusion of biological parameters into multilevel prediction models is thought to improve predictive accuracy of models on the basis of clinical variables. To this aim, mismatch negativity (MMN) was investigated in a sample clinically at high risk, comparing individuals with and without subsequent conversion to psychosis. METHODS: At baseline, an auditory oddball paradigm was used in 62 subjects meeting criteria of a late risk at-state who remained antipsychotic-naive throughout the study. Median follow-up period was 32 months (minimum of 24 months in nonconverters, n = 37). Repeated-measures analysis of covariance was employed to analyze the MMN recorded at frontocentral electrodes; additional comparisons with healthy controls (HC, n = 67) and first-episode schizophrenia patients (FES, n = 33) were performed. Predictive value was evaluated by a Cox regression model. RESULTS: Compared with nonconverters, duration MMN in converters (n = 25) showed significantly reduced amplitudes across the six frontocentral electrodes; the same applied in comparison with HC, but not FES, whereas the duration MMN in in nonconverters was comparable to HC and larger than in FES. A prognostic score was calculated based on a Cox regression model and stratified into two risk classes, which showed significantly different survival curves. CONCLUSIONS: Our findings demonstrate the duration MMN is significantly reduced in at-risk subjects converting to first-episode psychosis compared with nonconverters and may contribute not only to the prediction of conversion but also to a more individualized risk estimation and thus risk-adapted prevention.

Safety, effectiveness and predictors for early reoperation in therapeutic and prophylactic vertebroplasty: short-term results of a prospective case series of patients with osteoporotic vertebral fractures

Abstract Introduction Vertebroplasty (VP) is a cost-efficient alternative to kyphoplasty; however, regarding safety and vertebral body (VB) height restoration, it is considered inferior. We assessed the safety and efficacy of VP in alleviating pain, improving quality of life (QoL) and restoring alignment. Methods In a prospective monocenter case series from May 2007 until July 2008, there were 1,408 vertebroplasties performed during 319 interventions in 306 patients with traumatic, lytic and osteoporotic fractures. The 249 interventions in 233 patients performed because of osteoporotic vertebral fractures were analyzed regarding demographics, treatment and radiographic details, pain alleviation (VAS), QoL improvement (NASS and EQ-5D), complications and predictors for new fractures requiring a reoperation. Results The osteoporotic patient sample consisted of 76.7% (179) females with a median age of 80 years. A total of 54 males had a median age of 77 years. On average, there were 1.8 VBs fractured and 5 VBs treated. The preoperative pain was assessed by the visual analog scale (VAS) and decreased from 54.9 to 40.4 pts after 2 months and 31.2 pts after 6 months. Accordingly, the QoL on the EQ-5D measure (−0.6 to 1) improved from 0.35 pts before surgery to 0.56 pts after 2 and to 0.68 pts after 6 months. The preoperative Beck Index (anterior height/posterior height) improved from a mean of 0.64 preoperative to 0.76 postoperative, remained stable at 2 months and slightly deteriorated to 0.72 at 6 months postoperatively. There were cement leakages in 26% of the fractured VBs and in 1.4% of the prophylactically cemented VBs; there were symptoms in 4.3%, and most of them were temporary hypotension and one pulmonary cement embolism that remained asymptomatic. The univariate regression model revealed a tendency for a reduced risk for new or refractures on radiographs (OR = 2.61, 95% CI 0.92–7.38, p = 0.12) and reoperations (OR = 2.9, 95% CI 0.94–8.949, p = 0.1) when prophylactic augmentation was performed. The final multivariate regression model revealed male patients to have an about three times higher refracture risk (radiographic) (OR = 2.78, p = 0.02) at 6 months after surgery. Patients with a lumbar index fracture had an about three to five times higher refracture/reoperation risk than patients with a thoracic (OR = 0.33/0.35, p = 0.009/0.01) or thoracolumbar (OR = 0.32/0.22, p = 0.099/0.01) index fracture. Conclusion If routinely used, VP is a safe and efficacious treatment option for osteoporotic vertebral fractures with regard to pain relief and improvement of the QoL. Even segmental realignment can be partially achieved with proper patient positioning. Certain patient or fracture characteristics increase the risk for early radiographic refractures or new fractures, or a reoperation; a consequent prophylactic augmentation showed protective tendencies, but the study was underpowered for a final conclusion.

Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

Meta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests.

Impact of left ventricular hypertrophy late after aortic valve replacement for aortic stenosis on cardiovascular morbidity and mortality

AIMS: The goal of this study was to assess the prevalence of left ventricular (LV) hypertrophy in patients with aortic stenosis late (>6 months) after aortic valve replacement and its impact on cardiac-related morbidity and mortality. METHODS AND RESULTS: In a single tertiary centre, echocardiographic data of LV muscle mass were collected. Detailed information of medical history and angiographic data were gathered. Ninety-nine of 213 patients (46%) had LV hypertrophy late (mean 5.8 +/- 5.4 years) after aortic valve replacement. LV hypertrophy was associated with impaired exercise capacity, higher New York Heart Association dyspnoea class, a tendency for more frequent chest pain expressed as higher Canadian Cardiovascular Society class, and more rehospitalizations. 24% of patients with normal LV mass vs. 39% of patients with LV hypertrophy reported cardiac-related morbidity (p = 0.04). In a multivariate logistic regression model, LV hypertrophy was an independent predictor of cardiac-related morbidity (odds ratio 2.31, 95% CI 1.08 to 5.41), after correction for gender, baseline ejection fraction, and coronary artery disease and its risk factors. Thirty seven deaths occurred during a total of 1959 patient years of follow-up (mean follow-up 9.6 years). Age at aortic valve replacement (hazard ratio 1.85, 95% CI 1.39 to 2.47, for every 5 years increase in age), coexisting coronary artery disease at the time of surgery (hazard ratio 3.36, 95% CI 1.31 to 8.62), and smoking (hazard ratio 4.82, 95% CI 1.72 to 13.45) were independent predictors of overall mortality late after surgery, but not LV hypertrophy. CONCLUSIONS: In patients with aortic valve replacement for isolated aortic stenosis, LV hypertrophy late after surgery is associated with increased morbidity.