The role of admission blood glucose in outcome prediction of surviving patients with multiple injuries

BACKGROUND: Recent literature demonstrates hyperglycemia to be common in patients with trauma and associated with poor outcome in patients with traumatic brain injury and critically ill patients. The goal of this study was to analyze the impact of admission blood glucose on the outcome of surviving patients with multiple injuries. METHODS: Patients' charts (age >16) admitted to the emergency room of the University Hospital of Berne, Switzerland, between January 1, 2002, and December 31, 2004, with an Injury Severity Score >or=17 and more than one severely injured organ system were reviewed retrospectively. Outcome measurements included morbidity, intensive care unit, and hospital length of stay. RESULTS: The inclusion criteria were met by 555 patients, of which 108 (19.5%) patients died. After multiple regression analysis, admission blood glucose proved to be an independent predictor of posttraumatic morbidity (p < 0.0001), intensive care unit, and hospital length of stay (p < 0.0001), despite intensified insulin therapy on the intensive care unit. CONCLUSIONS: In this population of patients with multiple injuries, hyperglycemia on admission was strongly associated with increased morbidity, especially infections, prolonged intensive care unit, and hospital length of stay independent of injury severity, gender, age, and various biochemical parameters.

Kinetics of maternally acquired anti-hepatitis A antibodies: prediction of waning based on maternal or cord blood antibody levels

BACKGROUND Timing is critical for efficient hepatitis A vaccination in high endemic areas as high levels of maternal IgG antibodies against the hepatitis A virus (HAV) present in the first year of life may impede the vaccine response. OBJECTIVES To describe the kinetics of the decline of anti-HAV maternal antibodies, and to estimate the time of complete loss of maternal antibodies in infants in León, Nicaragua, a region in which almost all mothers are anti-HAV seropositive. METHODS We collected cord blood samples from 99 healthy newborns together with 49 corresponding maternal blood samples, as well as further blood samples at 2 and 7 months of age. Anti-HAV IgG antibody levels were measured by enzyme immunoassay (EIA). We predicted the time when antibodies would fall below 10 mIU/ml, the presumed lowest level of seroprotection. RESULTS Seroprevalence was 100% at birth (GMC 8392 mIU/ml); maternal and cord blood antibody concentrations were similar. The maternal antibody levels of the infants decreased exponentially with age and the half-life of the maternal antibody was estimated to be 40 days. The relationship between the antibody concentration at birth and time until full waning was described as: critical age (months)=3.355+1.969 × log(10)(Ab-level at birth). The survival model estimated that loss of passive immunity will have occurred in 95% of infants by the age of 13.2 months. CONCLUSIONS Complete waning of maternal anti-HAV antibodies may take until early in the second year of life. The here-derived formula relating maternal or cord blood antibody concentrations to the age at which passive immunity is lost may be used to determine the optimal age of childhood HAV vaccination.

Development and validation of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children

Background: Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children. Methods: Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model. Results: All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches. Conclusions: The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Prediction of cardiovascular disease risk factors in Mexican-American children from parental risk factors, and social and demographic characteristics in the HHANES

Objectives. Cardiovascular disease (CVD) including CVD secondary to diabetes type II, a significant health problem among Mexican American populations, originates in early childhood. This study seeks to determine risk factors available to the health practitioner that can identify the child at potential risk of developing CVD, thereby enabling early intervention. ^ Design. This is a secondary analysis of cross-sectional data of matched Mexican American parents and children selected from the HHANES, 1982–1984. ^ Methods. Parents at high risk for CVD were identified based on medical history, and clinical and physical findings. Factor analysis was performed on children's skinfold thicknesses, height, weight, and systolic and diastolic blood pressures, in order to produce a limited number of uncorrelated child CVD risk factors. Multiple regression analyses were then performed to determine other CVD markers associated with these Factors, independently for mothers and fathers. ^ Results. Factor analysis of children's measurements revealed three uncorrelated latent variables summarizing the children's CVD risk: Factor1: ‘Fatness’, Factor2: ‘Size and Maturity’, and Factor3: ‘Blood Pressure’, together accounting for the bulk of variation in children's measurements (86–89%). Univariate analyses showed that children from high CVD risk families did not differ from children of low risk families in occurrence of high blood pressure, overweight, biological maturity, acculturation score, or social and economic indicators. However, multiple regression using the factor scores (from factor analysis) as dependent variables, revealed that higher CVD risk in parents, was significantly associated with increased fatness and increased blood pressure in the children. Father's CVD risk status was associated with higher levels of body fat in his children and higher levels of blood pressure in sons. Mother's CVD risk status was associated with higher blood pressure levels in children, and occurrence of obesity in the mother associated with higher fatness levels in her children. ^ Conclusion. Occurrence of cardiovascular disease and its risk factors in parents of Mexican American children, may be used to identify children at potentially higher risk for developing CV disease in the future. Obesity in mothers appears to be an important marker for the development of higher levels of body fatness in children. ^

Therapeutic drug monitoring of once daily aminoglycoside dosing: comparison of two methods and investigation of the optimal blood sampling strategy.

PURPOSE Therapeutic drug monitoring of patients receiving once daily aminoglycoside therapy can be performed using pharmacokinetic (PK) formulas or Bayesian calculations. While these methods produced comparable results, their performance has never been checked against full PK profiles. We performed a PK study in order to compare both methods and to determine the best time-points to estimate AUC0-24 and peak concentrations (C max). METHODS We obtained full PK profiles in 14 patients receiving a once daily aminoglycoside therapy. PK parameters were calculated with PKSolver using non-compartmental methods. The calculated PK parameters were then compared with parameters estimated using an algorithm based on two serum concentrations (two-point method) or the software TCIWorks (Bayesian method). RESULTS For tobramycin and gentamicin, AUC0-24 and C max could be reliably estimated using a first serum concentration obtained at 1 h and a second one between 8 and 10 h after start of the infusion. The two-point and the Bayesian method produced similar results. For amikacin, AUC0-24 could reliably be estimated by both methods. C max was underestimated by 10-20% by the two-point method and by up to 30% with a large variation by the Bayesian method. CONCLUSIONS The ideal time-points for therapeutic drug monitoring of once daily administered aminoglycosides are 1 h after start of a 30-min infusion for the first time-point and 8-10 h after start of the infusion for the second time-point. Duration of the infusion and accurate registration of the time-points of blood drawing are essential for obtaining precise predictions.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Prediction of sepsis in the burn intensive care unit patient

Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^

Use of prediction methods for patent and trademark applications in Spain

Patent and trademark offices which run according to principles of new management have an inherent need for dependable forecasting data in planning capacity and service levels. The ability of the Spanish Office of Patents and Trademarks to carry out efficient planning of its resource needs requires the use of methods which allow it to predict the changes in the number of patent and trademark applications at different time horizons. The approach for the prediction of time series of Spanish patents and trademarks applications (1979e2009) was based on the use of different techniques of time series prediction in a short-term horizon. The methods used can be grouped into two specifics areas: regression models of trends and time series models. The results of this study show that it is possible to model the series of patents and trademarks applications with different models, especially ARIMA, with satisfactory model adjustment and relatively low error.

Blood flow and oxygen delivery to human brain during functional activity: Theoretical modeling and experimental data

Coupling of cerebral blood flow (CBF) and cerebral metabolic rate for oxygen (CMRO2) in physiologically activated brain states remains the subject of debates. Recently it was suggested that CBF is tightly coupled to oxidative metabolism in a nonlinear fashion. As part of this hypothesis, mathematical models of oxygen delivery to the brain have been described in which disproportionately large increases in CBF are necessary to sustain even small increases in CMRO2 during activation. We have explored the coupling of CBF and oxygen delivery by using two complementary methods. First, a more complex mathematical model was tested that differs from those recently described in that no assumptions were made regarding tissue oxygen level. Second, [15O] water CBF positron emission tomography (PET) studies in nine healthy subjects were conducted during states of visual activation and hypoxia to examine the relationship of CBF and oxygen delivery. In contrast to previous reports, our model showed adequate tissue levels of oxygen could be maintained without the need for increased CBF or oxygen delivery. Similarly, the PET studies demonstrated that the regional increase in CBF during visual activation was not affected by hypoxia. These findings strongly indicate that the increase in CBF associated with physiological activation is regulated by factors other than local requirements in oxygen.

Direct observation of covalent adducts with Cys34 of human serum albumin using mass spectrometry

The interactions of the unpaired thiol residue (Cys34) of human serum albumin (HSA) with low-molecular-weight thiols and an Au(I)-based antiarthritic drug have been examined using electrospray ionization mass spectrometry. Early measurements of the amount of HSA containing Cys34 as the free thiol suggested that up to 30% of circulating HSA bound cysteine as a mixed disulfide. It has also been suggested that reaction of HSA with cysteine, occurs only on handling and storage of plasma. In our experiments, there were three components of HSA in freshly collected plasma from normal volunteers, HSA, HSA + cysteine, and HSA + glucose in the ratio similar to50:25:25. We addressed this controversy by using iodoacetamide to block the free thiol of HSA in fresh plasma, preventing its reaction with plasma cysteine. When iodoacetamide was injected into a vacutaner tube as blood was collected, the HSA was modified by iodoacetamide, with 20-30% present as the mixed disulfide with cysteine (HSA + cys). These data provide strong evidence that 20-30% of HSA in normal plasma contains one bound cysteine. Reaction of HSA with [Au(S2O3)(2)](3-) resulted in formation of the adducts HSA + Au(S2O3) and HSA + Au. Reaction of HSA with iodoacetamide prior to treatment with [Au(S2O3)(2)](3-) blocked the formation of gold adducts. (C) 2003 Elsevier Inc. All rights reserved.

Smoking and elevated blood pressure are the most important risk factors for subarachnoid hemorrhage in the Asia-Pacific region - An overview of 26 cohorts involving 306,620 participants

Background and Purpose - The cause of subarachnoid hemorrhage ( SAH) is poorly understood and there are few large cohort studies of risk factors for SAH. We investigated the risk of SAH mortality and morbidity associated with common cardiovascular risk factors in the Asia-Pacific region and examined whether the strengths of these associations were different in Asian and Australasian ( predominantly white) populations. Methods - Cohort studies were identified from Internet electronic databases, searches of proceedings of meetings, and personal communication. Hazard ratios (HRs) for systolic blood pressure (SBP), current smoking, total serum cholesterol, body mass index (BMI), and alcohol drinking were calculated from Cox models that were stratified by sex and cohort and adjusted for age at risk. Results - Individual participant data from 26 prospective cohort studies ( total number of participants 306 620) that reported incident cases of SAH ( fatal and/or nonfatal) were available for analysis. During the median follow-up period of 8.2 years, a total of 236 incident cases of SAH were observed. Current smoking (HR, 2.4; 95% CI, 1.8 to 3.4) and SBP > 140 mm Hg ( HR, 2.0; 95% CI, 1.5 to 2.7) were significant and independent risk factors for SAH. Attributable risks of SAH associated with current smoking and elevated SBP ( similar to 140 mm Hg) were 29% and 19%, respectively. There were no significant associations between the risk of SAH and cholesterol, BMI, or drinking alcohol. The strength of the associations of the common cardiovascular risk factors with the risk of SAH did not differ much between Asian and Australasian regions. Conclusions - Cigarette smoking and SBP are the most important risk factors for SAH in the Asia-Pacific region.

Evaluation of brief dietary questions to estimate vegetable and fruit consumption - using serum carotenoids and red-cell folate

Objective: To evaluate responses to self-administered brief questions regarding consumption of vegetables and fruit by comparison with blood levels of serum carotenoids and red-cell folate. Design: A cross-sectional study in which participants reported their usual intake of fruit and vegetables in servings per day, and serum levels of five carotenoids (alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein/zeaxanthin and lycopene) and red-cell folate were measured. Serum carotenoid levels were determined by high-performance liquid chromatography, and red-cell folate by an automated immunoassay system. Settings and subjects: Between October and December 2000, a sample of 1598 adults aged 25 years and over, from six randomly selected urban centres in Queensland, Australia, were examined as part of a national study conducted to determine the prevalence of diabetes and associated cardiovascular risk factors. Results: Statistically significant (P < 0.01) associations with vegetable and fruit intake ( categorised into groups: = 4 servings per day) were observed for alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein/zeaxanthin and red-cell folate. The mean level of these carotenoids and of red-cell folate increased with increasing frequency of reported servings of vegetables and fruit, both before and after adjusting for potential confounding factors. A significant association with lycopene was observed only for vegetable intake before adjusting for confounders. Conclusions: These data indicate that brief questions may be a simple and valuable tool for monitoring vegetable and fruit intake in this population.

White blood cell count predicts reduction in coronary heart disease mortality with pravastatin

Background-Elevated serum inflammatory marker levels are associated with a greater long-term risk of cardiovascular events. Because 3-hydroxy-3-methylglutaryl coenzyme-A reductase inhibitors (statins) may have an antiinflammatory action, it has been suggested that patients with elevated inflammatory marker levels may have a greater reduction in cardiovascular risk with statin treatment. Methods and Results-We evaluated the association between the white blood cell count (WBC) and coronary heart disease mortality during a mean follow-up of 6.0 years in the Long-Term Intervention With Pravastatin in Ischemic Disease (LIPID) Study, a clinical trial comparing pravastatin (40 mg/d) with a placebo in 9014 stable patients with previous myocardial infarction or unstable angina. An increase in baseline WBC was associated with greater coronary heart disease mortality in patients randomized to placebo (hazard ratio for 1 X 10(9)/L increase in WBC, 1.18; 95% CI, 1.12 to 1.25; P<0.001) but not pravastatin (hazard ratio, 1.02; 95% CI, 0.96 to 1.09; P=0.56; P for interaction=0.004). The numbers of coronary heart disease deaths prevented per 1000 patients treated with pravastatin were 0, 9, 30, and 38 for baseline WBC quartiles of <5.9, 6.0 to 6.9, 7.0 to 8.1, and >8.2X10(9)/L, respectively. WBC was a stronger predictor of this treatment benefit than the ratio of total to high-density lipoprotein cholesterol and a global measure of cardiac risk. There was also a greater reduction (P=0.052) in the combined incidence of cardiovascular mortality, nonfatal myocardial infarction, and stroke with pravastatin as baseline WBC increased ( by quartile: 3, 41, 61, and 60 events prevented per 1000 patients treated, respectively). Conclusions-These data support the hypothesis that individuals with evidence of inflammation may obtain a greater benefit from statin therapy.

Evaluation and comparison of mammalian subcellular localization prediction methods

Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.