American Society of Anesthesiologists Score: Still Useful After 60 Years? Results of the EuSOS Study

OBJECTIVE: The European Surgical Outcomes Study described mortality following in-patient surgery. Several factors were identified that were able to predict poor outcomes in a multivariate analysis. These included age, procedure urgency, severity and type and the American Association of Anaesthesia score. This study describes in greater detail the relationship between the American Association of Anaesthesia score and postoperative mortality. METHODS: Patients in this 7-day cohort study were enrolled in April 2011. Consecutive patients aged 16 years and older undergoing inpatient non-cardiac surgery with a recorded American Association of Anaesthesia score in 498 hospitals across 28 European nations were included and followed up for a maximum of 60 days. The primary endpoint was in-hospital mortality. Decision tree analysis with the CHAID (SPSS) system was used to delineate nodes associated with mortality. RESULTS: The study enrolled 46,539 patients. Due to missing values, 873 patients were excluded, resulting in the analysis of 45,666 patients. Increasing American Association of Anaesthesia scores were associated with increased admission rates to intensive care and higher mortality rates. Despite a progressive relationship with mortality, discrimination was poor, with an area under the ROC curve of 0.658 (95% CI 0.642 - 0.6775). Using regression trees (CHAID), we identified four discrete American Association of Anaesthesia nodes associated with mortality, with American Association of Anaesthesia 1 and American Association of Anaesthesia 2 compressed into the same node. CONCLUSION: The American Association of Anaesthesia score can be used to determine higher risk groups of surgical patients, but clinicians cannot use the score to discriminate between grades 1 and 2. Overall, the discriminatory power of the model was less than acceptable for widespread use.

Structural time series models and the Kalman filter: A concise review

The continued increase in availability of economic data in recent years and, more importantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci fications we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman filter algorithm is described taking into account its different stages, from initialisation to parameter s estimation.

The relationship between the education level of successor's and the firm performance in German publicly listed family firms- an empirical analysis

The purpose of this thesis is to investigate how far the education level of the second or third generation of publicly traded German family firms affects the post-succession firm performance. By conducting a correlational and regression design, the aim is to examine how several variables influence the performance of family firms. Performance measures, for example ROA and Tobin’s q and variables, like Education level and succession periods, examine analytically that a positive succession trend will occur. However, with the used model, only a less rigid model shows empirical evidence.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Assessment of Myocardial Infarction by Cardiac Magnetic Resonance Imaging and Long-Term Mortality

Background: Cardiac magnetic resonance imaging provides detailed anatomical information on infarction. However, few studies have investigated the association of these data with mortality after acute myocardial infarction. Objective: To study the association between data regarding infarct size and anatomy, as obtained from cardiac magnetic resonance imaging after acute myocardial infarction, and long-term mortality. Methods: A total of 1959 reports of “infarct size” were identified in 7119 cardiac magnetic resonance imaging studies, of which 420 had clinical and laboratory confirmation of previous myocardial infarction. The variables studied were the classic risk factors – left ventricular ejection fraction, categorized ventricular function, and location of acute myocardial infarction. Infarct size and acute myocardial infarction extent and transmurality were analyzed alone and together, using the variable named “MET-AMI”. The statistical analysis was carried out using the elastic net regularization, with the Cox model and survival trees. Results: The mean age was 62.3 ± 12 years, and 77.3% were males. During the mean follow-up of 6.4 ± 2.9 years, there were 76 deaths (18.1%). Serum creatinine, diabetes mellitus and previous myocardial infarction were independently associated with mortality. Age was the main explanatory factor. The cardiac magnetic resonance imaging variables independently associated with mortality were transmurality of acute myocardial infarction (p = 0.047), ventricular dysfunction (p = 0.0005) and infarcted size (p = 0.0005); the latter was the main explanatory variable for ischemic heart disease death. The MET-AMI variable was the most strongly associated with risk of ischemic heart disease death (HR: 16.04; 95%CI: 2.64-97.5; p = 0.003). Conclusion: The anatomical data of infarction, obtained from cardiac magnetic resonance imaging after acute myocardial infarction, were independently associated with long-term mortality, especially for ischemic heart disease death.

The management of brain metastases

Brain metastases occur in 20-50% of NSCLC and 50-80% of SCLC. In this review, we will look at evidence-based medicine data and give some perspectives on the management of BM. We will address the problems of multiple BM, single BM and prophylactic cranial irradiation. Recursive Partitioning Analysis (RPA) is a powerful prognostic tool to facilitate treatment decisions. Dealing with multiple BM, the use of corticosteroids was established more than 40 years ago by a unique randomized trial (RCT). Palliative effect is high (_80%) as well as side-effects. Whole brain radiotherapy (WBRT) was evaluated in many RCTs with a high (60-90%) response rate; several RT regimes are equivalent, but very high dose per fraction should be avoided. In multiple BM from SCLC, the effect of WBRT is comparable to that in NSCLC but chemotherapy (CXT) although advocated is probably less effective than RT. Single BM from NSCLC occurs in 30% of all BM cases; several prognostic classifications including RPA are very useful. Several options are available in single BM: WBRT, surgery (SX), radiosurgery (RS) or any combination of these. All were studied in RCTs and will be reviewed: the addition of WBRT to SX or RS gives a better neurological tumour control, has little or no impact on survival, and may be more toxic. However omitting WBRT after SX alone gives a higher risk of cerebro-spinal fluid dissemination. Prophylactic cranial irradiation (PCI) has a major role in SCLC. In limited disease, meta-analyses have shown a positive impact of PCI in the decrease of brain relapse and in survival improvement, especially for patients in complete remission. Surprisingly, this has been recently confirmed also in extensive disease. Experience with PCI for NSCLC is still limited, but RCT suggest a reduction of BM with no impact on survival. Toxicity of PCI is a matter of debate, as neurological or neuro-cognitive impairment is already present prior to PCI in almost half of patients. However RT toxicity is probably related to total dose and dose per fraction. Perspectives : Future research should concentrate on : 1) combined modalities in multiple BM. 2) Exploration of treatments in oligo-metastases. 3) Further exploration of PCI in NSCLC. 4) Exploration of new, toxicity-sparing radiotherapy techniques (IMRT, Tomotherapy etc).

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.

Infection of Calomys callosus (Rodentia Cricetidae) with strains of different Trypanosoma cruzi biodemes: pathogenicity, histotropism, and fibrosis induction

The influence of different Trypanosoma cruzi biodemes on the evolution of the infection and on the histopathological lesions of the heart and skeletal muscles, during the experimental infection of Calomys callosus, was investigated. Three groups of C. callosus were infected, respectively, with parasite strains representative of three different Biodemes: Type I (Y strain), Type II (21 SF strain), and Type III (Colombian strain). For each group, normal C. callosus were also used as controls. Marked differences have been detected in the responses of C. callosus to the infection with the three strains in this model. The strains Types I and II (Y and 21 SF) determined moderate lesions, mostly in the myocardium, with low parasitism, a rapid course, and total regression of the lesions by the 60th day of infection. Differently, Type III strain (Colombian), was more pathogenic for C. callosus and induced necrotic-inflammatory lesions in skeletal muscles and myocardium, in correspondence to intracellular parasitism. Proliferation of fibroblasts and amorphous matrix deposits, followed by interstitial fibrosis were present. Progressive regression of the inflammatory changes and collagen deposits occurred spontaneously. The progression and regression of both inflammation and fibrosis induced by the Colombian strain were further submitted to quantitative evaluation by morphometry. Results of the morphometric studies presented good correlation with the histopathological findings. The results confirm the importance of the different biodemes in the determination of tissue lesions and the peculiarities of response of C. callosus to infection with T. cruzi.

Assessing the Relationship between the Baseline Value of a Continuous Variable and Subsequent Change over Time

Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this analysis lies and which statistical method is adequate.With the help of simulated longitudinal data of body mass index in children,we review statistical methods for the analysis of the association between the baseline value and subsequent change, assuming linear growth with time. Key issues in such analyses are mathematical coupling, measurement error, variability of change between individuals, and regression to the mean. Ideally, it is better to rely on multiple repeated measurements at different times and a linear random effects model is a standard approach if more than two waves of data are available. If only two waves of data are available, our simulations show that Blomqvist's method - which consists in adjusting for measurement error variance the estimated regression coefficient of observed change on baseline value - provides accurate estimates. The adequacy of the methods to assess the relationship between the baseline value and subsequent change depends on the number of data waves, the availability of information on measurement error, and the variability of change between individuals.

Disagreement between ultrasound and magnetic resonance imaging in the identification of schistosomal periportal fibrosis

Abdominal ultrasound (US) has been widely used in the evaluation of patients with schistosomiasis mansoni. It represents an important indirect method of diagnosis and classification of the disease, and it has also been used as a tool in the evaluation of therapeutic response and regression of fibrosis. We describe the case of a man in whom US showed solid evidence of schistosomal periportal fibrosis and magnetic resonance imaging revealed that periportal signal alteration corresponded to adipose tissue which entered the liver togheter with the portal vein.

Impact of Prophylactic Cranial Irradiation Timing on Brain Relapse Rates in Patients With Stage IIIB Non-Small-Cell Lung Carcinoma Treated With Two Different Chemoradiotherapy Regimens.

PURPOSE: To retrospectively assess the influence of prophylactic cranial irradiation (PCI) timing on brain relapse rates in patients treated with two different chemoradiotherapy (CRT) regimens for Stage IIIB non-small-cell lung cancer (NSCLC). METHODS AND MATERIALS: A cohort of 134 patients, with Stage IIIB NSCLC in recursive partitioning analysis Group 1, was treated with PCI (30 Gy at 2 Gy/fr) following one of two CRT regimens. Regimen 1 (n = 58) consisted of three cycles of induction chemotherapy (ICT) followed by concurrent CRT (C-CRT). Regimen 2 (n = 76) consisted of immediate C-CRT during thoracic radiotherapy. RESULTS: At a median follow-up of 27.6 months (range, 7.2-40.4), 65 patients were alive. Median, progression-free, and brain metastasis-free survival (BMFS) times for the whole study cohort were 23.4, 15.4, and 23.0 months, respectively. Median survival time and the 3-year survival rate for regimens 1 and 2 were 19.3 vs. 26.1 months (p = 0.001) and 14.4% vs. 34.4% (p < .001), respectively. Median time from the initiation of primary treatment to PCI was 123.2 (range, 97-161) and 63.4 (range, 55-74) days for regimens 1 and 2, respectively (p < 0.001). Overall, 11 (8.2%) patients developed brain metastasis (BM) during the follow-up period: 8 (13.8%) in regimen 1 and 3 (3.9%) in regimen 2 (p = 0.03). Only 3 (2.2%) patients developed BM at the site of first failure, and for 2 of them, it was also the sole site of recurrence. Median BMFS for regimens 1 and 2 were 17.4 (13.5-21.3) vs. 26.0 (22.9-29.1 months), respectively (p < 0.001). CONCLUSION: These results suggest that in Stage IIIB NSCLC patients treated with PCI, lower BM incidence and longer survival rates result from immediate C-CRT rather than ITC-first regimens. This indicates the benefit of earlier PCI use without delay because of induction protocols.

Land use improves spatial predictions of mountain plant abundance but not presence-absence

Question Does a land-use variable improve spatial predictions of plant species presence-absence and abundance models at the regional scale in a mountain landscape? Location Western Swiss Alps. Methods Presence-absence generalized linear models (GLM) and abundance ordinal logistic regression models (LRM) were fitted to data on 78 mountain plant species, with topo-climatic and/or land-use variables available at a 25-m resolution. The additional contribution of land use when added to topo-climatic models was evaluated by: (1) assessing the changes in model fit and (2) predictive power, (3) partitioning the deviance respectively explained by the topo-climatic variables and the land-use variable through variation partitioning, and (5) comparing spatial projections. Results Land use significantly improved the fit of presence-absence models but not their predictive power. In contrast, land use significantly improved both the fit and predictive power of abundance models. Variation partitioning also showed that the individual contribution of land use to the deviance explained by presence-absence models was, on average, weak for both GLM and LRM (3.7% and 4.5%, respectively), but changes in spatial projections could nevertheless be important for some species. Conclusions In this mountain area and at our regional scale, land use is important for predicting abundance, but not presence-absence. The importance of adding land-use information depends on the species considered. Even without a marked effect on model fit and predictive performance, adding land use can affect spatial projections of both presence-absence and abundance models.

A combined molecular clinical predictor of survival validated with the rtog-0525 cohort

For glioblastoma (GBM), survival classification has primarily relied on clinical criteria, exemplified by the Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis (RPA). We sought to improve tumor classification by combining tumor biomarkers with the clinical RPA data. To accomplish this, we first developed 4 molecular biomarkers derived from gene expression profiling, a glioma CpG island methylator phenotype, a novel MGMT promoter methylation assay, and IDH1 mutations. A molecular predictor (MP) model was created with these 4 biomarkers on a training set of 220 retrospectively collected archival GBMtumors. ThisMPwas further combined with RPA classification to develop a molecular-clinical predictor (MCP). The median survivals for the combined, 4-class MCP were 65 months, 31 months, 13 months, and 9 months, which was significantly improved when compared with the RPA alone. The MCP was then applied to 725 samples from the RTOG-0525 cohort, showing median survival for each risk group of NR, 26 months, 16 months, and 11 months. The MCP was significantly improved over the RPA at outcome prediction in the RTOG 0525 cohort with a 33%increase in explained variation with respect to survival, validating the result obtained in the training set. To illustrate the benefit of the MCP for patient stratification, we examined progression-free survival (PFS) for patients receiving standard-dose temozolomide (SD-TMZ) vs. dose-dense TMZ (DD-TMZ) in RPA and MCP risk groups. A significant difference between DD-TMZ and SD-TMZ was observed in the poorest surviving MCP risk group with a median PFS of 6 months vs. 3 months (p ¼ 0.048, log-rank test). This difference was not seen using the RPA classification alone. In summary, we have developed a combined molecular-clinical predictor that appears to improve outcome prediction when compared with clinical variables alone. This MCP may serve to better identify patients requiring intensive treatments beyond the standard of care.

Health-related quality of life assessment in people with multiple sclerosis and their family caregivers. A multicenter study in Catalonia (Southern Europe)

Objectives: To measure the health-related quality of life (HRQoL) of multiple sclerosis (MS) patients and their caregivers, and to assess which factors can best describe HRQoL. Methods: A cross-sectional multicenter study of nine hospitals enrolled MS patients and their caregivers who attended outpatient clinics consecutively. The instruments used were the SF-36 for patients and the SF-12 and GHQ-12 for caregivers. Classification and regression tree analysis was used to analyze the explanatory factors of HRQoL. Results: A total of 705 patients (mean age 40.4 years, median Expanded Disability Status Scale 2.5, 77.8% with relapsing-remitting MS) and 551 caregivers (mean age 45.4 years) participated in the study. MS patients had significantly lower HRQoL than in the general population (physical SF-36: 39.9; 95% confidence interval [CI]: 39.1–40.6; mental SF-36: 44.4; 95% CI: 43.5–45.3). Caregivers also presented lower HRQoL than general population, especially in its mental domain (mental SF-12: 46.4; 95% CI: 45.5–47.3). Moreover, according to GHQ-12, 27% of caregivers presented probable psychological distress. Disability and co-morbidity in patients, and co-morbidity and employment status in caregivers, were the most important explanatory factors of their HRQoL. Conclusions: Not only the HRQoL of patients with MS, but also that of their caregivers, is indeed notably affected. Caregivers’ HRQoL is close to population of chronic illness even that the patients sample has a mild clinical severity and that caregiving role is a usual task in the study context