Usefulness of inducible ventricular tachycardia to predict long-term adverse outcomes in arrhythmogenic right ventricular cardiomyopathy

The role of the electrophysiologic (EP) study for risk stratification in patients with arrhythmogenic right ventricular cardiomyopathy is controversial. We investigated the role of inducible sustained monomorphic ventricular tachycardia (SMVT) for the prediction of an adverse outcome (AO), defined as the occurrence of cardiac death, heart transplantation, sudden cardiac death, ventricular fibrillation, ventricular tachycardia with hemodynamic compromise or syncope. Of 62 patients who fulfilled the 2010 Arrhythmogenic Right Ventricular Cardiomyopathy Task Force criteria and underwent an EP study, 30 (48%) experienced an adverse outcome during a median follow-up of 9.8 years. SMVT was inducible in 34 patients (55%), 22 (65%) of whom had an adverse outcome. In contrast, in 28 patients without inducible SMVT, 8 (29%) had an adverse outcome. Kaplan-Meier analysis showed an event-free survival benefit for patients without inducible SMVT (log-rank p = 0.008) with a cumulative survival free of an adverse outcome of 72% (95% confidence interval [CI] 56% to 92%) in the group without inducible SMVT compared to 26% (95% CI 14% to 50%) in the other group after 10 years. The inducibility of SMVT during the EP study (hazard ratio [HR] 2.99, 95% CI 1.23 to 7.27), nonadherence (HR 2.74, 95% CI 1.3 to 5.77), and heart failure New York Heart Association functional class II and III (HR 2.25, 95% CI 1.04 to 4.87) were associated with an adverse outcome on univariate Cox regression analysis. The inducibility of SMVT (HR 2.52, 95% CI 1.03 to 6.16, p = 0.043) and nonadherence (HR 2.34, 95% CI 1.1 to 4.99, p = 0.028) remained as significant predictors on multivariate analysis. This long-term observational data suggest that SMVT inducibility during EP study might predict an adverse outcome in patients with arrhythmogenic right ventricular cardiomyopathy, advocating a role for EP study in risk stratification.

Comparison of paired serum and lithium heparin plasma samples for the measurement of serum amyloid A in horses using an automated turbidimetric immunoassay

This study aimed to evaluate whether equine serum amyloid A (SAA) concentrations could be reliably measured in plasma with a turbidimetric immunoassay previously validated for equine SAA concentrations in serum. Paired serum and lithium-heparin samples obtained from 40 horses were evaluated. No difference was found in SAA concentrations between serum and plasma using a paired t test (P=0.48). The correlation between paired samples was 0.97 (Spearman's rank P<0.0001; 95% confidence interval 0.95-0.99). Passing-Bablok regression analyses revealed no differences between paired samples. Bland-Altman plots revealed a positive bias in plasma compared to serum but the difference was not considered clinically significant. The results indicate that lithium-heparin plasma samples are suitable for measurement of equine SAA using this method. Use of either serum or plasma allows for greater flexibility when it comes to sample collection although care should be taken when comparing data between measurements from different sample types.

Tumor Regression Grading of Gastrointestinal Carcinomas after Neoadjuvant Treatment

Multimodal therapy concepts have been successfully implemented in the treatment of locally advanced gastrointestinal malignancies. The effects of neoadjuvant chemo- or radiochemotherapy such as scarry fibrosis or resorptive changes and inflammation can be determined by histopathological investigation of the subsequent resection specimen. Tumor regression grading (TRG) systems which aim to categorize the amount of regressive changes after cytotoxic treatment mostly refer onto the amount of therapy induced fibrosis in relation to residual tumor or the estimated percentage of residual tumor in relation to the previous tumor site. Commonly used TRGs for upper gastrointestinal carcinomas are the Mandard grading and the Becker grading system, e.g., and for rectal cancer the Dworak or the Rödel grading system, or other systems which follow similar definitions. Namely for gastro-esophageal carcinomas these TRGs provide important prognostic information since complete or subtotal tumor regression has shown to be associated with better patient's outcome. The prognostic value of TRG may even exceed those of currently used staging systems (e.g., TNM staging) for tumors treated by neoadjuvant therapy. There have been some limitations described regarding interobserver variability especially in borderline cases, which may be improved by standardization of work up of resection specimen and better training of histopathologic determination of regressive changes. It is highly recommended that TRG should be implemented in every histopathological report of neoadjuvant treated gastrointestinal carcinomas. The aim of this review is to disclose the relevance of histomorphological TRG to accomplish an optimal therapy for patients with gastrointestinal carcinomas.

Tumor regression grade of urothelial bladder cancer after neoadjuvant chemotherapy: a novel and successful strategy to predict survival.

Histopathologic tumor regression grades (TRGs) after neoadjuvant chemotherapy predict survival in different cancers. In bladder cancer, corresponding studies have not been conducted. Fifty-six patients with advanced invasive urothelial bladder cancer received neoadjuvant chemotherapy before cystectomy and lymphadenectomy. TRGs were defined as follows: TRG1: complete tumor regression; TRG2: >50% tumor regression; TRG3: 50% or less tumor regression. Separate TRGs were assigned for primary tumors and corresponding lymph nodes. The prognostic impact of these 2 TRGs, the highest (dominant) TRG per patient, and competing tumor features reflecting tumor regression (ypT/ypN stage, maximum diameter of the residual tumor) were determined. Tumor characteristics in initial transurethral resection of the bladder specimens were tested for response prediction. The frequency of TRGs 1, 2, and 3 in the primary tumors were n=16, n=19, and n=21; corresponding data from the lymph nodes were n=31, n=9, and n=16. Interobserver agreement in determination of the TRG was strong (κ=0.8). Univariately, all evaluated parameters were significantly (P≤0.001) related to overall survival; however, the segregation of the Kaplan-Meier curves was best for the dominant TRG. In multivariate analysis, only dominant TRG predicted overall survival independently (P=0.035). In transurethral resection specimens of the chemotherapy-naive bladder cancer, the only tumor feature with significant (P<0.03) predictive value for therapy response was a high proliferation rate. In conclusion, among all parameters reflecting tumor regression, the dominant TRG was the only independent risk factor. A favorable chemotherapy response is associated with a high proliferation rate in the initial chemotherapy-naive bladder cancer. This feature might help personalize neoadjuvant chemotherapy.

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression

Reconstruction of shape and intensity from 2D x-ray images has drawn more and more attentions. Previously introduced work suffers from the long computing time due to its iterative optimization characteristics and the requirement of generating digitally reconstructed radiographs within each iteration. In this paper, we propose a novel method which uses a patient-specific 3D surface model reconstructed from 2D x-ray images as a surrogate to get a patient-specific volumetric intensity reconstruction via partial least squares regression. No DRR generation is needed. The method was validated on 20 cadaveric proximal femurs by performing a leave-one-out study. Qualitative and quantitative results demonstrated the efficacy of the present method. Compared to the existing work, the present method has the advantage of much shorter computing time and can be applied to both DXA images as well as conventional x-ray images, which may hold the potentials to be applied to clinical routine task such as total hip arthroplasty (THA).

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.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Real-life results of balloon kyphoplasty for vertebral compression fractures from the SWISSspine registry

BACKGROUND CONTEXT The Swiss Federal Office of Public Health mandated a nationwide health technology assessment-registry for balloon kyphoplasty (BKP) for decision making on reimbursement of these interventions. The early results of the registry led to a permanent coverage of BKP by basic health insurance. The documentation was continued for further evidence generation. PURPOSE This analysis reports on the 1 year results of patients after BKP treatment. STUDY DESIGN Prospective multicenter observational case series. PATIENT SAMPLE The data on 625 cases with 819 treated vertebrae were documented from March 2005 to May 2012. OUTCOME MEASURES Surgeon-administered outcome instruments were primary intervention form for BKP and the follow-up form; patient self-reported measures were EuroQol-5D questionnaire, North American Spine Society outcome instrument /Core Outcome Measures Index (including visual analog scale), and a comorbidity questionnaire. Outcome measures were back pain, medication, quality of life (QoL), cement extrusions, and new fractures within the first postoperative year. METHODS Data were recorded preoperatively and at 3 to 6-month and 1-year follow-ups. Wilcoxon signed-rank test was used for comparison of pre- with postoperative measurements. Multivariate logistic regression was used to identify factors with a significant influence on the outcome. RESULTS Seventy percent of patients were women with mean age of 71 years (range, 18-91 years); mean age of men was 65 years (range, 15-93 years). Significant and clinically relevant reduction of back pain, improvement of QoL, and reduction of pain killer consumption was seen within the first postoperative year. Preoperative back pain decreased from 69.3 to 29.0 at 3 to 6-month and remained unchanged at 1-year follow-ups. Consequently, QoL improved from 0.23 to 0.71 and 0.75 at the same follow-up intervals. The overall vertebra-based cement extrusion rates with and without extrusions into intervertebral discs were 22.1% and 15.3%, respectively. Symptomatic cement extrusions with radiculopathy were five (0.8%). A new vertebral fracture within a year from the BKP surgery was observed in 18.4% of the patients. CONCLUSIONS The results of the largest observational study for BKP so far are consistent with published randomized trials and systematic reviews. In this routine health care setting, BKP is safe and effective in reducing pain, improving QoL, and lowering pain_killer consumption and has an acceptable rate of cement extrusions. Postoperative outcome results show clear and significant clinical improvement at early follow-up that remain stable during the first postoperative year.