Relationship between the logistic EuroSCORE and the Society of Thoracic Surgeons Predicted Risk of Mortality score in patients implanted with the CoreValve ReValving system--a Bern-Rotterdam Study

Background Surgical risk scores, such as the logistic EuroSCORE (LES) and Society of Thoracic Surgeons Predicted Risk of Mortality (STS) score, are commonly used to identify high-risk or “inoperable” patients for transcatheter aortic valve implantation (TAVI). In Europe, the LES plays an important role in selecting patients for implantation with the Medtronic CoreValve System. What is less clear, however, is the role of the STS score of these patients and the relationship between the LES and STS. Objective The purpose of this study is to examine the correlation between LES and STS scores and their performance characteristics in high-risk surgical patients implanted with the Medtronic CoreValve System. Methods All consecutive patients (n = 168) in whom a CoreValve bioprosthesis was implanted between November 2005 and June 2009 at 2 centers (Bern University Hospital, Bern, Switzerland, and Erasmus Medical Center, Rotterdam, The Netherlands) were included for analysis. Patient demographics were recorded in a prospective database. Logistic EuroSCORE and STS scores were calculated on a prospective and retrospective basis, respectively. Results Observed mortality was 11.1%. The mean LES was 3 times higher than the mean STS score (LES 20.2% ± 13.9% vs STS 6.7% ± 5.8%). Based on the various LES and STS cutoff values used in previous and ongoing TAVI trials, 53% of patients had an LES ≥15%, 16% had an STS ≥10%, and 40% had an LES ≥20% or STS ≥10%. Pearson correlation coefficient revealed a reasonable (moderate) linear relationship between the LES and STS scores, r = 0.58, P < .001. Although the STS score outperformed the LES, both models had suboptimal discriminatory power (c-statistic, 0.49 for LES and 0.69 for STS) and calibration. Conclusions Clinical judgment and the Heart Team concept should play a key role in selecting patients for TAVI, whereas currently available surgical risk score algorithms should be used to guide clinical decision making.

Social cognition as a mediator variable between neurocognition and functional outcome in schizophrenia: empirical review and new results by structural equation modeling

Cognitive impairments are currently regarded as important determinants of functional domains and are promising treatment goals in schizophrenia. Nevertheless, the exact nature of the interdependent relationship between neurocognition and social cognition as well as the relative contribution of each of these factors to adequate functioning remains unclear. The purpose of this article is to systematically review the findings and methodology of studies that have investigated social cognition as a mediator variable between neurocognitive performance and functional outcome in schizophrenia. Moreover, we carried out a study to evaluate this mediation hypothesis by the means of structural equation modeling in a large sample of 148 schizophrenia patients. The review comprised 15 studies. All but one study provided evidence for the mediating role of social cognition both in cross-sectional and in longitudinal designs. Other variables like motivation and social competence additionally mediated the relationship between social cognition and functional outcome. The mean effect size of the indirect effect was 0.20. However, social cognitive domains were differentially effective mediators. On average, 25% of the variance in functional outcome could be explained in the mediation model. The results of our own statistical analysis are in line with these conclusions: Social cognition mediated a significant indirect relationship between neurocognition and functional outcome. These results suggest that research should focus on differential mediation pathways. Future studies should also consider the interaction with other prognostic factors, additional mediators, and moderators in order to increase the predictive power and to target those factors relevant for optimizing therapy effects.

Combined anatomical and clinical factors for the long-term risk stratification of patients undergoing percutaneous coronary intervention: the Logistic Clinical SYNTAX score

The SYNTAX score (SXscore), an anatomical-based scoring tool reflecting the complexity of coronary anatomy, has established itself as an important long-term prognostic factor in patients undergoing percutaneous coronary intervention (PCI). The incorporation of clinical factors may further augment the utility of the SXscore to longer-term risk stratify the individual patient for clinical outcomes.

Life on the Edge: Patterns of Formal and Informal Help to Older Adults in the United States and Sweden

Objectives Our objective in this study was to compare assistance received by individuals in the United States and Sweden with characteristics associated with low, moderate, or high 1-year placement risk in the United States. Methods We used longitudinal nationally representative data from 4,579 participants aged 75 years and older in the 1992 and 1993 waves of the Medicare Current Beneficiary Survey (MCBS) and cross-sectional data from 1,379 individuals aged 75 years and older in the Swedish Aging at Home (AH) national survey for comparative purposes. We developed a logistic regression equation using U.S. data to identify individuals with 3 levels (low, moderate, or high) of predicted 1-year institutional placement risk. Groups with the same characteristics were identified in the Swedish sample and compared on formal and informal assistance received. Results Formal service utilization was higher in Swedish sample, whereas informal service use is lower overall. Individuals with characteristics associated with high placement risk received more formal and less informal assistance in Sweden relative to the United States. Discussion Differences suggest formal services supplement informal support in the United States and that formal and informal services are complementary in Sweden.

Fitting ACE Structural Equation Models to Case-Control Family Data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. Our likelihood-based approach to fitting involves conditioning on the proband’s disease status, as well as setting prevalence equal to a pre-specified value that can be estimated from the data themselves if necessary. Simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. These assumptions include: the usual assumptions for the classic ACE and liability-threshold models; assumptions about shared family environment for relative pairs; and assumptions about the case-control family sampling, including single ascertainment. When our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

Analysis of nonlinear gain-induced effects on short-pulse amplification in doped fibers by use of an extended power equation

Optical pulse amplification in doped fibers is studied using an extended power transport equation for the coupled pulse spectral components. This equation includes the effects of gain saturation, gain dispersion, fiber dispersion, fiber nonlinearity, and amplified spontaneous emission. The new model is employed to study nonlinear gain-induced effects on the spectrotemporal characteristics of amplified subpicosecond pulses, in both the anomalous and the normal dispersion regimes.