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.

Temporal lobe white matter asymmetry and language laterality in epilepsy patients.

Recent studies using diffusion tensor imaging (DTI) have advanced our knowledge of the organization of white matter subserving language function. It remains unclear, however, how DTI may be used to predict accurately a key feature of language organization: its asymmetric representation in one cerebral hemisphere. In this study of epilepsy patients with unambiguous lateralization on Wada testing (19 left and 4 right lateralized subjects; no bilateral subjects), the predictive value of DTI for classifying the dominant hemisphere for language was assessed relative to the existing standard-the intra-carotid Amytal (Wada) procedure. Our specific hypothesis is that language laterality in both unilateral left- and right-hemisphere language dominant subjects may be predicted by hemispheric asymmetry in the relative density of three white matter pathways terminating in the temporal lobe implicated in different aspects of language function: the arcuate (AF), uncinate (UF), and inferior longitudinal fasciculi (ILF). Laterality indices computed from asymmetry of high anisotropy AF pathways, but not the other pathways, classified the majority (19 of 23) of patients using the Wada results as the standard. A logistic regression model incorporating information from DTI of the AF, fMRI activity in Broca's area, and handedness was able to classify 22 of 23 (95.6%) patients correctly according to their Wada score. We conclude that evaluation of highly anisotropic components of the AF alone has significant predictive power for determining language laterality, and that this markedly asymmetric distribution in the dominant hemisphere may reflect enhanced connectivity between frontal and temporal sites to support fluent language processes. Given the small sample reported in this preliminary study, future research should assess this method on a larger group of patients, including subjects with bi-hemispheric dominance.

Association of alcohol craving and proximal outcomes of a residential treatment program for patients with alcohol use disorders

Background: Alcohol craving is an essential construct in research and treatment of alcohol use disorders (AUD). Craving is mostly investigated in association with concurrent variables or distal treatment outcomes at follow-up. Objectives: The aim of this study is to examine craving at admission and its relevance for essential proximal outcomes at discharge from AUD treatment such as positive alcohol expectancy, abstinent-related self-efficacy, and substance-related coping, as well as patients’ demographic and AUD characteristics. Methods: In total, 36 patients were recruited within an inpatient treatment AUD program. Results: An association between craving and positive alcohol expectancies at discharge was found in the regression model even when the respective expectancies, age, gender, and severity of alcohol dependence at admission were controlled for (F(2,29)1⁄432.71, p50.001). Craving explained 2.3% of the variance of change in positive alcohol expectancy. Conclusion: The results suggest a low predictive value of craving for positive alcohol expectancy. In addition, we found significant associations between the craving and the severity of AUD and alcohol consumption before admission. Future studies should include proximal outcomes related to treatment efficacy as well as distal outcomes.

Attitudes, stress and use of force in selected areas of Texas law enforcement

This research aimed to explore the extent to which police use of force was related to attitudes towards violence, agency type, and racism. Previous studies have found a culture of honor in the psychology of violence in the Southern United States. Were similar attitudes measurable among Texas professional line officers? Are there predictors of use of force?^ A self reported anonymous survey was administered to Texas patrol officers in the cities of Austin and Houston, and the Counties of Harris and Travis. A total of seventy-four questionnaires were used in the statistical analyses. Scales were developed measuring use of force, attitudes towards violence, and feelings on racism. Their relationship was examined.^ A regression model shows a strong and significant relationship between the officers' attitudes towards violence and the self-reported use of force. Further, agency type, municipal versus sheriff, also predicts use of force. Attitudes regarding race or racism, as measured by this study, were not predictive of use of force. ^

Biodiversity effects on nitrate concentrations in soil solution: a Bayesian model

Ecosystems are faced with high rates of species loss which has consequences for their functions and services. To assess the effects of plant species diversity on the nitrogen (N) cycle, we developed a model for monthly mean nitrate (NO3-N) concentrations in soil solution in 0-30 cm mineral soil depth using plant species and functional group richness and functional composition as drivers and assessing the effects of conversion of arable land to grassland, spatially heterogeneous soil properties, and climate. We used monthly mean NO3-N concentrations from 62 plots of a grassland plant diversity experiment from 2003 to 2006. Plant species richness (1-60) and functional group composition (1-4 functional groups: legumes, grasses, non-leguminous tall herbs, non-leguminous small herbs) were manipulated in a factorial design. Plant community composition, time since conversion from arable land to grassland, soil texture, and climate data (precipitation, soil moisture, air and soil temperature) were used to develop one general Bayesian multiple regression model for the 62 plots to allow an in-depth evaluation using the experimental design. The model simulated NO3-N concentrations with an overall Bayesian coefficient of determination of 0.48. The temporal course of NO3-N concentrations was simulated differently well for the individual plots with a maximum plot-specific Nash-Sutcliffe Efficiency of 0.57. The model shows that NO3-N concentrations decrease with species richness, but this relation reverses if more than approx. 25 % of legume species are included in the mixture. Presence of legumes increases and presence of grasses decreases NO3-N concentrations compared to mixtures containing only small and tall herbs. Altogether, our model shows that there is a strong influence of plant community composition on NO3-N concentrations.

A novel clinical scoring system for outcome prediction in dogs with acute kidney injury managed by hemodialysis.

BACKGROUND No reliable tool to predict outcome of acute kidney injury (AKI) exists. HYPOTHESIS A statistically derived scoring system can accurately predict outcome in dogs with AKI managed with hemodialysis. ANIMALS One hundred and eighty-two client-owned dogs with AKI. METHODS Logistic regression analyses were performed initially on clinical variables available on the 1st day of hospitalization for relevance to outcome. Variables with P< or = .1 were considered for further analyses. Continuous variables outside the reference range were divided into quartiles to yield quartile-specific odds ratios (ORs) for survival. Models were developed by incorporating weighting factors assigned to each quartile based on the OR, using either the integer value of the OR (Model A) or the exact OR (Models B or C, when the etiology was known). A predictive score for each model was calculated for each dog by summing all weighting factors. In Model D, actual values for continuous variables were used in a logistic regression model. Receiver-operating curve analyses were performed to assess sensitivities, specificities, and optimal cutoff points for all models. RESULTS Higher scores were associated with decreased probability of survival (P < .001). Models A, B, C, and D correctly classified outcomes in 81, 83, 87, and 76% of cases, respectively, and optimal sensitivities/specificities were 77/85, 81/85, 83/90 and 92/61%, respectively. CONCLUSIONS AND CLINICAL RELEVANCE The models allowed outcome prediction that corresponded with actual outcome in our cohort. However, each model should be validated further in independent cohorts. The models may also be useful to assess AKI severity.

Efficiency Bounds for Estimating Linear Functionals of Nonparametric Regression Models with Endogenous Regressors

Consider a nonparametric regression model Y=mu*(X) + e, where the explanatory variables X are endogenous and e satisfies the conditional moment restriction E[e|W]=0 w.p.1 for instrumental variables W. It is well known that in these models the structural parameter mu* is 'ill-posed' in the sense that the function mapping the data to mu* is not continuous. In this paper, we derive the efficiency bounds for estimating linear functionals E[p(X)mu*(X)] and int_{supp(X)}p(x)mu*(x)dx, where p is a known weight function and supp(X) the support of X, without assuming mu* to be well-posed or even identified.

The diagnostic value of a 12-lead electrocardiogram in patients with cardiac tamponade due to a malignant pericardial effusion

Background. Cardiac tamponade can occur when a large amount of fluid, gas, singly or in combination, accumulating within the pericardium, compresses the heart causing circulatory compromise. Although previous investigators have found the 12-lead ECG to have a poor predictive value in diagnosing cardiac tamponade, very few studies have evaluated it as a follow up tool for ruling in or ruling out tamponade in patients with previously diagnosed malignant pericardial effusions. ^ Methods. 127 patients with malignant pericardial effusions at the MD Anderson Cancer Center were included in this retrospective study. While 83 of these patients had a cardiac tamponade diagnosed by echocardiographic criteria (Gold standard), 44 did not. We computed the sensitivity (Se), specificity (Sp), positive (PPV) and negative predictive values (NPV) for individual and combinations of ECG abnormalities. Individual ECG abnormalities were also entered singly into a univariate logistic regression model to predict tamponade. ^ Results. For patients with effusions of all sizes, electrical alternans had a Se, Sp, PPV and NPV of 22.61%, 97.61%, 95% and 39.25% respectively. These parameters for low voltage complexes were 55.95%, 74.44%, 81.03%, 46.37% respectively. The presence of all three ECG abnormalities had a Se = 8.33%, Sp = 100%, PPV = 100% and NPV = 35.83% while the presence of at least one of the three ECG abnormalities had a Se = 89.28%, Sp = 46.51%, PPV = 76.53%, NPV = 68.96%. For patients with effusions of all sizes electrical alternans had an OR of 12.28 (1.58–95.17, p = 0.016), while the presence of at least one ECG abnormality had an OR of 7.25 (2.9–18.1, p = 0.000) in predicting tamponade. ^ Conclusions. Although individual ECG abnormalities had low sensitivities, specificities, NPVs and PPVs with the exception of electrical alternans, the presence of at least one of the three ECG abnormalities had a high sensitivity in diagnosing cardiac tamponade. This could point to its potential use as a screening test with a correspondingly high NPV to rule out a diagnosis of tamponade in patients with malignant pericardial effusions. This could save expensive echocardiographic assessments in patients with previously diagnosed pericardial effusions. ^

Mean corpuscular volume as a marker for adherence to antiretroviral therapy among HIV infected children and adolescents in Uganda

Mean corpuscular volume, which is an inexpensive and widely available measure to assess, increases in HIV infected individuals receiving zidovudine and stavudine raising the hypothesis that it could be used as a surrogate for adherence.^ The aim of this study was to examine the association between mean corpuscular volume and adherence to antiretroviral therapy among HIV infected children and adolescents aged 0–19 years in Uganda as well as the extent to which changes in mean corpuscular volume predict adherence as determined by virologic suppression.^ The investigator retrospectively reviewed and analyzed secondary data of 158 HIV infected children and adolescents aged 0–19 years who initiated antiretroviral therapy under an observational cohort at the Baylor College of Medicine Children's Foundation - Uganda. Viral suppression was used as the gold standard for monitoring adherence and defined as viral load of < 400 copies/ml at 24 and 48 weeks. ^ Patients were at least 48 weeks on therapy, age 0.2–18.4 years, 54.4% female, 82.3% on zidovudine based regimen, 92% WHO stage III at initiation of therapy, median pre therapy MCV 80.6 fl (70.3–98.3 fl), median CD4% 10.2% (0.3%–28.0%), and mean pre therapy viral load 407,712.9 ± 270,413.9 copies/ml. For both 24 and 48 weeks of antiretroviral therapy, patients with viral suppression had a greater mean percentage change in mean corpuscular volume (15.1% ± 8.4 vs. 11.1% ± 7.8 and 2.3% ± 13.2 vs. -2.7% ± 10.5 respectively). The mean percentage change in mean corpuscular volume was greater in the first 24 weeks of therapy for patients with and without viral suppression (15.1% ± 8.4 vs. 2.3% ± 13.2 and 11.1% ± 7.8 vs. -2.7% ± 10.5 respectively). In the multivariate logistic regression model, percentage change in mean corpuscular volume ≥ 20% was significantly associated with viral suppression (adjusted OR 4.0; CI 1.2–13.3; p value 0.02). The ability of percentage changes in MCV to correctly identify children and adolescents with viral suppression was higher at a cut off of ≥ 20% (90.7%; sensitivity, 31.7%) than at ≥ 9% (82.9%; sensitivity, 78.9%). Negative predictive value was lower at ≥ 20% change (25%; specificity, 84.8%) than at ≥ 9% change (33.3%; specificity, 39.4%).^ Mean corpuscular volume is a useful marker of adherence among children and adolescents with viral suppression. ^

SEVERE HEAD TRAUMA, EMERGENCY TRANSPORT, AND PATIENT OUTCOME

Trauma and severe head injuries are important issues because they are prevalent, because they occur predominantly in the young, and because variations in clinical management may matter. Trauma is the leading cause of death for those under age 40. The focus of this head injury study is to determine if variations in time from the scene of accident to a trauma center hospital makes a difference in patient outcomes.^ A trauma registry is maintained in the Houston-Galveston area and includes all patients admitted to any one of three trauma center hospitals with mild or severe head injuries. A study cohort, derived from the Registry, includes 254 severe head injury cases, for 1980, with a Glasgow Coma Score of 8 or less.^ Multiple influences relate to patient outcomes from severe head injury. Two primary variables and four confounding variables are identified, including time to emergency room, time to intubation, patient age, severity of injury, type of injury and mode of transport to the emergency room. Regression analysis, analysis of variance, and chi-square analysis were the principal statistical methods utilized.^ Analysis indicates that within an urban setting, with a four-hour time span, variations in time to emergency room do not provide any strong influence or predictive value to patient outcome. However, data are suggestive that at longer time periods there is a negative influence on outcomes. Age is influential only when the older group (55-64) is included. Mode of transport (helicopter or ambulance) did not indicate any significant difference in outcome.^ In a multivariate regression model, outcomes are influenced primarily by severity of injury and age which explain 36% (R('2)) of variance. Inclusion of time to emergency room, time to intubation, transport mode and type injury add only 4% (R('2)) additional contribution to explaining variation in patient outcome.^ The research concludes that since the group most at risk to head trauma is the young adult male involved in automobile/motorcycle accidents, more may be gained by modifying driving habits and other preventive measures. Continuous clinical and evaluative research are required to provide updated clinical wisdom in patient management and trauma treatment protocols. A National Institute of Trauma may be required to develop a national public policy and evaluate the many medical, behavioral and social changes required to cope with the country's number 3 killer and the primary killer of young adults.^

REGRESSION ANALYSIS FOR STANDARDIZED MORTALITY RATIOS WITH APPLICATION TO OCCUPATIONAL FOLLOW-UP STUDIES

Traditional comparison of standardized mortality ratios (SMRs) can be misleading if the age-specific mortality ratios are not homogeneous. For this reason, a regression model has been developed which incorporates the mortality ratio as a function of age. This model is then applied to mortality data from an occupational cohort study. The nature of the occupational data necessitates the investigation of mortality ratios which increase with age. These occupational data are used primarily to illustrate and develop the statistical methodology.^ The age-specific mortality ratio (MR) for the covariates of interest can be written as MR(,ij...m) = ((mu)(,ij...m)/(theta)(,ij...m)) = r(.)exp (Z('')(,ij...m)(beta)) where (mu)(,ij...m) and (theta)(,ij...m) denote the force of mortality in the study and chosen standard populations in the ij...m('th) stratum, respectively, r is the intercept, Z(,ij...m) is the vector of covariables associated with the i('th) age interval, and (beta) is a vector of regression coefficients associated with these covariables. A Newton-Raphson iterative procedure has been used for determining the maximum likelihood estimates of the regression coefficients.^ This model provides a statistical method for a logical and easily interpretable explanation of an occupational cohort mortality experience. Since it gives a reasonable fit to the mortality data, it can also be concluded that the model is fairly realistic. The traditional statistical method for the analysis of occupational cohort mortality data is to present a summary index such as the SMR under the assumption of constant (homogeneous) age-specific mortality ratios. Since the mortality ratios for occupational groups usually increase with age, the homogeneity assumption of the age-specific mortality ratios is often untenable. The traditional method of comparing SMRs under the homogeneity assumption is a special case of this model, without age as a covariate.^ This model also provides a statistical technique to evaluate the relative risk between two SMRs or a dose-response relationship among several SMRs. The model presented has application in the medical, demographic and epidemiologic areas. The methods developed in this thesis are suitable for future analyses of mortality or morbidity data when the age-specific mortality/morbidity experience is a function of age or when there is an interaction effect between confounding variables needs to be evaluated. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^

Frequencies and risk factors for bisphosphonate-related osteonecrosis of the jaw in cancer patients: A matched case-control study

Bisphosphonates represent a unique class of drugs that effectively treat and prevent a variety of bone-related disorders including metastatic bone disease and osteoporosis. High tolerance and high efficacy rates quickly ranked bisphosphonates as the standard of care for bone-related diseases. However, in the early 2000s, case reports began to surface that linked bisphosphonates with osteonecrosis of the jaw (ONJ). Since that time, studies conducted have corroborated the linkage. However, as with most disease states, many factors can contribute to the onset of disease. The aim of this study was to determine which comorbid factors presented an increased risk for developing ONJ in cancer patients.^ Using a case-control study design, investigators used a combination of ICD-9 codes and chart review to identify confirmed cases of ONJ at The University of Texas M. D. Anderson Cancer Center (MDACC). Each case was then matched to five controls based on age, gender, race/ethnicity, and primary cancer diagnosis. Data querying and chart review provided information on variables of interest. These variables included bisphosphonate exposure, glucocorticoids exposure, smoking history, obesity, and diabetes. Statistical analysis was conducted using PASW (Predictive Analytics Software) Statistics, Version 18 (SPSS Inc., Chicago, Illinois).^ One hundred twelve (112) cases were identified as confirmed cases of ONJ. Variables were run using univariate logistic regression to determine significance (p < .05); significant variables were included in the final conditional logistic regression model. Concurrent use of bisphosphonates and glucocorticoids (OR, 18.60; CI, 8.85 to 39.12; p < .001), current smokers (OR, 2.52; CI, 1.21 to 5.25; p = .014), and presence of diabetes (OR, 1.84; CI, 1.06 to 3.20; p = .030) were found to increase the risk for developing ONJ. Obesity was not associated significantly with ONJ development.^ In this study, cancer patients that received bisphosphonates as part of their therapeutic regimen were found to have an 18-fold increase in their risk of developing ONJ. Other factors included smoking and diabetes. More studies examining the concurrent use of glucocorticoids and bisphosphonates may be able to strengthen any correlations.^

Developing the follow-up schedules for women with breast cancer

Background: The follow-up care for women with breast cancer requires an understanding of disease recurrence patterns and the follow-up visit schedule should be determined according to the times when the recurrence are most likely to occur, so that preventive measure can be taken to avoid or minimize the recurrence. Objective: To model breast cancer recurrence through stochastic process with an aim to generate a hazard function for determining a follow-up schedule. Methods: We modeled the process of disease progression as the time transformed Weiner process and the first-hitting-time was used as an approximation of the true failure time. The women's "recurrence-free survival time" or a "not having the recurrence event" is modeled by the time it takes Weiner process to cross a threshold value which represents a woman experiences breast cancer recurrence event. We explored threshold regression model which takes account of covariates that contributed to the prognosis of breast cancer following development of the first-hitting time model. Using real data from SEER-Medicare, we proposed models of follow-up visits schedule on the basis of constant probability of disease recurrence between consecutive visits. Results: We demonstrated that the threshold regression based on first-hitting-time modeling approach can provide useful predictive information about breast cancer recurrence. Our results suggest the surveillance and follow-up schedule can be determined for women based on their prognostic factors such as tumor stage and others. Women with early stage of disease may be seen less frequently for follow-up visits than those women with locally advanced stages. Our results from SEER-Medicare data support the idea of risk-controlled follow-up strategies for groups of women. Conclusion: The methodology we proposed in this study allows one to determine individual follow-up scheduling based on a parametric hazard function that incorporates known prognostic factors.^