High-molecular weight adiponectin is independently associated with the extent of coronary artery disease in men

OBJECTIVE: Adiponectin has anti-atherogenic properties and low circulating adiponectin has been linked to coronary atherosclerosis. Yet, there is considerable evidence that the high-molecular weight (HMW) complex of adiponectin is the major active form of this adipokine. We therefore investigated whether HMW adiponectin is associated with the extent of coronary artery disease (CAD) in men. RESEARCH DESIGN AND METHODS: Associations among CAD, HMW adiponectin and the HMW/total-adiponectin ratio were assessed in 240 male patients undergoing elective coronary angiography. Total adiponectin and HMW adiponectin was measured by enzyme-linked immunosorbent assay and serum levels were correlated with defined coronary scores and established cardiovascular risk factors. RESULTS: We found significant inverse correlations between angiographic scores and HMW adiponectin [Extent Score (ES): r=-0.39; Gensini Score (GS): r=-0.35; and Severity Score (SS): r=-0.40, all P<0.001], and the HMW/total-adiponectin ratio (ES: r=-0.49; GS: r=-0.46; SS: r=-0.46; all P<0.001). Multivariable regression analyses revealed that HMW adiponectin and the HMW/total-adiponectin ratio were significantly associated with the extent of CAD (both P<0.001). ROC analyses demonstrated that the predictive value of HMW adiponectin and the HMW/total-adiponectin ratio for the extent of coronary atherosclerosis significantly exceeded that of total adiponectin (P<0.001, P=0.010, respectively). CONCLUSIONS: HMW adiponectin and the HMW/total-adiponectin ratio inversely correlate with the extent of CAD. HMW adiponectin in particular seems to be a better marker for CAD extent than total adiponectin.

Osteoporosis case finding in the general practice: phalangeal radiographic absorptiometry with and without risk factors for osteoporosis to select postmenopausal women eligible for lumbar spine and hip densitometry

Mass screening for osteoporosis using DXA measurements at the spine and hip is presently not recommended by health authorities. Instead, risk factor questionnaires and peripheral bone measurements may facilitate the selection of women eligible for axial bone densitometry. The aim of this study was to validate a case finding strategy for postmenopausal women who would benefit most from subsequent DXA measurement by using phalangeal radiographic absorptiometry (RA) alone or in combination with risk factors in a general practice setting. The sensitivity and specificity of this strategy in detecting osteoporosis (T-score < or =2.5 SD at the spine and/or the hip) were compared with those of the current reimbursement criteria for DXA measurements in Switzerland. Four hundred and twenty-three postmenopausal women with one or more risk factors for osteoporosis were recruited by 90 primary care physicians who also performed the phalangeal RA measurements. All women underwent subsequent DXA measurement of the spine and the hip at the Osteoporosis Policlinic of the University Hospital of Berne. They were allocated to one of two groups depending on whether they matched with the Swiss reimbursement conditions for DXA measurement or not. Logistic regression models were used to predict the likelihood of osteoporosis versus "no osteoporosis" and to derive ROC curves for the various strategies. Differences in the areas under the ROC curves (AUC) were tested for significance. In women lacking reimbursement criteria, RA achieved a significantly larger AUC (0.81; 95% CI 0.72-0.89) than the risk factors associated with patients' age, height and weight (0.71; 95% C.I. 0.62-0.80). Furthermore, in this study, RA provided a better sensitivity and specificity in identifying women with underlying osteoporosis than the currently accepted criteria for reimbursement of DXA measurement. In the Swiss environment, RA is a valid case finding tool for patients with risk factors for osteoporosis, especially for those who do not qualify for DXA reimbursement.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

Arterial blood pressure during early sepsis and outcome

OBJECTIVE: To evaluate the association between arterial blood pressure (ABP) during the first 24 h and mortality in sepsis. DESIGN: Retrospective cohort study. SETTING: Multidisciplinary intensive care unit (ICU). PATIENTS AND PARTICIPANTS: A total of 274 septic patients. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Hemodynamic, and laboratory parameters were extracted from a PDMS database. The hourly time integral of ABP drops below clinically relevant systolic arterial pressure (SAP), mean arterial pressure (MAP), and mean perfusion pressure (MPP = MAP - central venous pressure) levels was calculated for the first 24 h after ICU admission and compared with 28-day-mortality. Binary and linear regression models (adjusted for SAPS II as a measure of disease severity), and a receiver operating characteristic (ROC) analysis were applied. The areas under the ROC curve were largest for the hourly time integrals of ABP drops below MAP 60 mmHg (0.779 vs. 0.764 for ABP drops below MAP 55 mmHg; P < or = 0.01) and MPP 45 mmHg. No association between the hourly time integrals of ABP drops below certain SAP levels and mortality was detected. One or more episodes of MAP < 60 mmHg increased the risk of death by 2.96 (CI 95%, 1.06-10.36, P = 0.04). The area under the ROC curve to predict the need for renal replacement therapy was highest for the hourly time integral of ABP drops below MAP 75 mmHg. CONCLUSIONS: A MAP level > or = 60 mmHg may be as safe as higher MAP levels during the first 24 h of ICU therapy in septic patients. A higher MAP may be required to maintain kidney function.

Validation of the estimation of physiologic ability and surgical stress (E-PASS) score in liver surgery

BACKGROUND: The estimation of physiologic ability and surgical stress (E-PASS) has been used to produce a numerical estimate of expected mortality and morbidity after elective gastrointestinal surgery. The aim of this study was to validate E-PASS in a selected cohort of patients requiring liver resections (LR). METHODS: In this retrospective study, E-PASS predictor equations for morbidity and mortality were applied to the prospective data from 243 patients requiring LR. The observed rates were compared with predicted rates using Fisher's exact test. The discriminative capability of E-PASS was evaluated using receiver-operating characteristic (ROC) curve analysis. RESULTS: The observed and predicted overall mortality rates were both 3.3% and the morbidity rates were 31.3 and 26.9%, respectively. There was a significant difference in the comprehensive risk scores for deceased and surviving patients (p = 0.043). However, the scores for patients with or without complications were not significantly different (p = 0.120). Subsequent ROC curve analysis revealed a poor predictive accuracy for morbidity. CONCLUSIONS: The E-PASS score seems to effectively predict mortality in this specific group of patients but is a poor predictor of complications. A new modified logistic regression might be required for LR in order to better predict the postoperative outcome.

Stochastic search for semiparametric linear regression models

This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar [Ann. Statist. 15(3) (1987) 1131–1154]. The idea is to generate a random finite subset of a parameter space which will automatically contain points which are very close to an unknown true parameter. The motivation for this procedure comes from recent work of Dümbgen et al. [Ann. Statist. 39(2) (2011) 702–730] on regression models with log-concave error distributions.

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.