Predictors of red blood cell transfusion after cardiac surgery: a prospective cohort study

Abstract OBJECTIVE To identify predictors of red blood cell transfusion (RBCT) after cardiac surgery. METHOD A prospective cohort study performed with 323 adults after cardiac surgery, from April to December of 2013. A data collection instrument was constructed by the researchers containing factors associated with excessive bleeding after cardiac surgery, as found in the literature, for investigation in the immediate postoperative period. The relationship between risk factors and the outcome was assessed by univariate analysis and logistic regression. RESULTS The factors associated with RBCT in the immediate postoperative period included lower height and weight, decreased platelet count, lower hemoglobin level, higher prevalence of platelet count <150x10 3/mm3, lower volume of protamine, longer duration of anesthesia, higher prevalence of intraoperative RBCT, lower body temperature, higher heart rate and higher positive end-expiratory pressure. The independent predictor was weight <66.5Kg. CONCLUSION Factors associated with RBCT in the immediate postoperative period of cardiac surgery were found. The independent predictor was weight.

TBS (trabecular bone score) and diabetes-related fracture risk.

CONTEXT: Type 2 diabetes is associated with increased fracture risk but paradoxically greater bone mineral density (BMD). Trabecular bone score (TBS) is derived from the texture of the spine dual x-ray absorptiometry (DXA) image and is related to bone microarchitecture and fracture risk, providing information independent of BMD. OBJECTIVE: This study evaluated the ability of lumbar spine TBS to account for increased fracture risk in diabetes. DESIGN AND SETTING: We performed a retrospective cohort study using BMD results from a large clinical registry for the province of Manitoba, Canada. Patients: We included 29,407 women 50 years old and older with baseline DXA examinations, among whom 2356 had diagnosed diabetes. MAIN OUTCOME MEASURES: Lumbar spine TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Health service records were assessed for incident nontraumatic major osteoporotic fractures (mean follow-up 4.7 years). RESULTS: Diabetes was associated with higher BMD at all sites but lower lumbar spine TBS in unadjusted and adjusted models (all P < .001). The adjusted odds ratio (aOR) for a measurement in the lowest vs the highest tertile was less than 1 for BMD (all P < .001) but was increased for lumbar spine TBS [aOR 2.61, 95% confidence interval (CI) 2.30-2.97]. Major osteoporotic fractures were identified in 175 women (7.4%) with and 1493 (5.5%) without diabetes (P < .001). Lumbar spine TBS was a BMD-independent predictor of fracture and predicted fractures in those with diabetes (adjusted hazard ratio 1.27, 95% CI 1.10-1.46) and without diabetes (hazard ratio 1.31, 95% CI 1.24-1.38). The effect of diabetes on fracture was reduced when lumbar spine TBS was added to a prediction model but was paradoxically increased from adding BMD measurements. CONCLUSIONS: Lumbar spine TBS predicts osteoporotic fractures in those with diabetes, and captures a larger portion of the diabetes-associated fracture risk than BMD.

Strategies for sequential prediction of stationary time series

We present simple procedures for the prediction of a real valued sequence. The algorithms are based on a combinationof several simple predictors. We show that if the sequence is a realization of a bounded stationary and ergodic random process then the average of squared errors converges, almost surely, to that of the optimum, given by the Bayes predictor. We offer an analog result for the prediction of stationary gaussian processes.

On prediction of individual sequences

Sequential randomized prediction of an arbitrary binary sequence isinvestigated. No assumption is made on the mechanism of generating the bit sequence. The goal of the predictor is to minimize its relative loss, i.e., to make (almost) as few mistakes as the best ``expert'' in a fixed, possibly infinite, set of experts. We point out a surprising connection between this prediction problem and empirical process theory. First, in the special case of static (memoryless) experts, we completely characterize the minimax relative loss in terms of the maximum of an associated Rademacher process. Then we show general upper and lower bounds on the minimaxrelative loss in terms of the geometry of the class of experts. As main examples, we determine the exact order of magnitude of the minimax relative loss for the class of autoregressive linear predictors and for the class of Markov experts.

Persecution perpetuated: The medieval origins of anti-semitic violence in Nazi Germany

How persistent are cultural traits? This paper uses data on anti-Semitism in Germany and finds continuity at the local level over more than half a millennium. When the Black Death hit Europe in 1348-50, killing between one third and one half of the population, its cause was unknown. Many contemporaries blamed the Jews. Cities all over Germany witnessed mass killings of their Jewish population. At the same time, numerous Jewish communities were spared these horrors. We use plague pogroms as an indicator for medieval anti-Semitism. Pogroms during the Black Death are a strong and robust predictor of violence against Jews in the 1920s, and of votes for the Nazi Party. In addition, cities that saw medieval anti-Semitic violence also had higher deportation rates for Jews after 1933, were more likely to see synagogues damaged or destroyed in the Night of Broken Glass in 1938, and their inhabitants wrote more anti-Jewish letters to the editor of the Nazi newspaper Der Stürmer.

Continuous electroencephalography in the medical intensive care unit.

OBJECTIVES: To examine predictors and the prognostic value of electrographic seizures (ESZs) and periodic epileptiform discharges (PEDs) in medical intensive care unit (MICU) patients without a primary acute neurologic condition. DESIGN: Retrospective study. SETTING: MICU in a university hospital. PATIENTS: A total of 201 consecutive patients admitted to the MICU between July 2004 and January 2007 without known acute neurologic injury and who underwent continuous electroencephalography monitoring (cEEG) for investigation of possible seizures or changes in mental status. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Median time from intensive care unit (ICU) admission to cEEG was 1 day (interquartile range 1-4). The majority of patients (60%) had sepsis as the primary admission diagnosis and 48% were comatose at the time of cEEG. Ten percent (n = 21) of patients had ESZs, 17% (n = 34) had PEDs, 5% (n = 10) had both, and 22% (n = 45) had either ESZs or PEDs. Seizures during cEEG were purely electrographic (no detectable clinical correlate) in the majority (67%) of patients. Patients with sepsis had a higher rate of ESZs or PEDs than those without sepsis (32% vs. 9%, p < 0.001). On multivariable analysis, sepsis at ICU admission was the only significant predictor of ESZs or PEDs (odds ratio 4.6, 95% confidence interval 1.9-12.7, p = 0.002). After controlling for age, coma, and organ dysfunction, the presence of ESZs or PEDs was associated with death or severe disability at hospital discharge (89% with ESZs or PEDs, vs. 39% if not; odds ratio 19.1, 95% confidence interval 6.3-74.6, p < 0.001). CONCLUSION: In this retrospective study of MICU patients monitored with cEEG, ESZs and PEDs were frequent, predominantly in patients with sepsis. Seizures were mainly nonconvulsive. Both seizures and periodic discharges were associated with poor outcome. Prospective studies are warranted to determine more precisely the frequency and clinical impact of nonconvulsive seizures and periodic discharges, particularly in septic patients.

Clinical and ultrasonographic predictors of joint replacement for knee osteoarthritis: results from a large, 3-year, prospective EULAR study.

OBJECTIVES: To determine clinical and ultrasonographic predictors of joint replacement surgery across Europe in primary osteoarthritis (OA) of the knee. METHODS: This was a 3-year prospective study of a painful OA knee cohort (from a EULAR-sponsored, multicentre study). All subjects had clinical evaluation, radiographs and ultrasonography (US) at study entry. The rate of knee replacement surgery over the 3-year follow-up period was determined using Kaplan-Meier survival data analyses. Predictive factors for joint replacement were identified by univariate log-rank test then multivariate analysis using a Cox proportional-hazards regression model. Potential baseline predictors included demographic, clinical, radiographic and US features. RESULTS: Of the 600 original patients, 531 (88.5%), mean age 67+/-10 years, mean disease duration 6.1+/-6.9 years, had follow-up data and were analysed. During follow-up (median 3 years; range 0-4 years), knee replacement was done or required for 94 patients (estimated event rate of 17.7%). In the multivariate analysis, predictors of joint replacement were as follows: Kellgren and Lawrence radiographic grade (grade &gt; or =III vs &lt;III, hazards ratio (HR) = 4.08 (95% CI 2.34 to 7.12), p&lt;0.0001); ultrasonographic knee effusion (&gt; or =4 mm vs &lt;4 mm) (HR = 2.63 (95% CI 1.70 to 4.06), p&lt;0.0001); knee pain intensity on a 0-100 mm visual analogue scale (&gt; or =60 vs &lt;60) (HR = 1.81 (95% CI 1.15 to 2.83), p=0.01) and disease duration (&gt; or =5 years vs &lt;5 years) (HR=1.63 (95% CI 1.08 to 2.47), p=0.02). Clinically detected effusion and US synovitis were not associated with joint replacement in the univariate analysis. CONCLUSION: Longitudinal evaluation of this OA cohort demonstrated significant progression to joint replacement. In addition to severity of radiographic damage and pain, US-detected effusion was a predictor of subsequent joint replacement.

BIOMOD - a platform for ensemble forecasting of species distributions

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package

Credit cycles in theory and experiment

We test in the laboratory the potential of evolutionary dynamics as predictor of actual behavior. To this end, we propose an asymmetricgame -which we interpret as a borrowerlender relation-, study itsevolutionary dynamics in a random matching set-up, and tests itspredictions. The model provides conditions for the existence ofcredit markets and credit cycles. The theoretical predictions seemto be good approximations of the experimental results.

Individual responses to converting enzyme inhibitors and calcium antagonists.

This study was designed to assess whether the acute blood pressure response of an individual hypertensive patient to a calcium antagonist or an angiotensin converting enzyme (ACE) inhibitor is a good predictor of the long-term efficacy of these drug classes in this particular patient. The concept that good responses to ACE inhibitors and calcium antagonists may be mutually exclusive was also tested. Sixteen patients were included in a randomized crossover trial of enalapril, 20 mg daily, and diltiazem, 120 mg daily, for 6 weeks each. Blood pressure was measured by ambulatory blood pressure recording. During the washout phase, the acute effect of nifedipine, 10 mg p.o., and enalaprilat, 5 mg i.v., was evaluated. Nifedipine and enalaprilat reduced blood pressure equally well. The long-term blood pressure reduction induced by enalapril and diltiazem was similar. The acute blood pressure response to a given drug was not a good predictor of the result obtained with long-term therapy. No age dependency of the antihypertensive effect of either drug class was apparent. There was no evidence that a good response to one drug excluded a similarly good response to the other.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

Association between the A-2518G polymorphism in the monocyte chemoattractant protein-1 gene and insulin resistance and Type 2 diabetes mellitus.

AIMS/HYPOTHESIS: The molecular mechanisms of obesity-related insulin resistance are incompletely understood. Macrophages accumulate in adipose tissue of obese individuals. In obesity, monocyte chemoattractant protein-1 (MCP-1), a key chemokine in the process of macrophage accumulation, is overexpressed in adipose tissue. MCP-1 is an insulin-responsive gene that continues to respond to exogenous insulin in insulin-resistant adipocytes and mice. MCP-1 decreases insulin-stimulated glucose uptake into adipocytes. The A-2518G polymorphism in the distal regulatory region of MCP-1 may regulate gene expression. The aim of this study was to investigate the impact of this gene polymorphism on insulin resistance. METHODS: We genotyped the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort ( n=3307). Insulin resistance, estimated by homeostasis model assessment, and Type 2 diabetes were diagnosed in 803 and 635 patients respectively. RESULTS: Univariate analysis revealed that plasma MCP-1 levels were significantly and positively correlated with WHR ( p=0.011), insulin resistance ( p=0.0097) and diabetes ( p<0.0001). Presence of the MCP-1 G-2518 allele was associated with decreased plasma MCP-1 ( p=0.017), a decreased prevalence of insulin resistance (odds ratio [OR]=0.82, 95% CI: 0.70-0.97, p=0.021) and a decreased prevalence of diabetes (OR=0.80, 95% CI: 0.67-0.96, p=0.014). In multivariate analysis, the G allele retained statistical significance as a negative predictor of insulin resistance (OR=0.78, 95% CI: 0.65-0.93, p=0.0060) and diabetes (OR=0.80, 95% CI: 0.66-0.96, p=0.018). CONCLUSIONS/INTERPRETATION: In a large cohort of Caucasians, the MCP-1 G-2518 gene variant was significantly and negatively correlated with plasma MCP-1 levels and the prevalence of insulin resistance and Type 2 diabetes. These results add to recent evidence supporting a role for MCP-1 in pathologies associated with hyperinsulinaemia.

Forecasting with missing data: Application to a real case

This paper presents a comparative analysis of linear and mixed modelsfor short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay.The series is interpolated with a linear predictor which minimizes theforecast mean square error. The linear models are seasonal ARIMA models and themixed models have a linear component and a non linear seasonal component.The non linear component is estimated by a non parametric regression of dataversus time. Short term forecasts, no more than two days ahead, are of interestbecause they can be used by the port authorities to notice the fleet.Several models are fitted and compared by their forecasting behavior.

Resting metabolic rate in six- to ten-year-old obese and nonobese children.

The resting metabolic rate (RMR) and body composition of 130 obese and nonobese prepubertal children, aged 6 to 10 years, were assessed by indirect calorimetry and skin-fold thickness, respectively. The mean (+/- SD) RMR was 4619 +/- 449 kJ.day-1 (164 +/- 31 kJ.kg body weight-1 x day-1) in the 62 boys and 4449 +/- 520 kJ.day-1 (147 +/- 32 kJ.kg body weight-1 x day-1) in the 68 girls. Fat-free mass was the best single predictor of RMR (R2 = 0.64; p < 0.001). Step-down multiple regression analysis, with independent variables such as age, gender, weight, and height, allowed several RMR predictive equations to be developed. An equation for boys is as follows: RMR (kJ.day-1) = 1287 + 28.6 x Weight(kg) + 23.6 x Height(cm) - 69.1 x Age(yr) (R2 = 0.58; p < 0.001). An equation for girls is as follows: RMR (kJ.day-1 = 1552 + 35.8 x Weight (kg) + 15.6 x Height (cm) - 36.3 x Age (yr) (R2 = 0.69; p < 0.001). Comparison between the measured RMR and that predicted by currently used formulas showed that most of these equations tended to overestimate the RMR of both genders, especially in overweight children.

The influence of environmental conditions on immune responses, morphology and recapture probability of nestling house martins (Delichon urbica)

Nestling birds produced later in the season are hypothesized to be of poor quality with a low probability of survival and recruitment. In a Spanish population of house martins (Delichon urbica), we first compared reproductive success, immune responses and morphological traits between the first and the second broods. Second, we investigated the effects of an ectoparasite treatment and breeding date on the recapture rate the following year. Due probably to a reverse situation in weather conditions during the experiment, with more rain during rearing of the first brood, nestlings reared during the second brood were in better condition and had stronger immune responses compared with nestlings from the first brood. Contrary to other findings on house martins, we found a similar recapture rate for chicks reared during the first and the second brood. Furthermore, ectoparasitic house martin bugs had no significant effect on the recapture rate. Recaptured birds had similar morphology but higher immunoglobulin levels when nestlings compared with non-recaptured birds. This result implies that a measure of immune function is a better predictor of survival than body condition per se.