The evidence behind the level of a random urine protein:creatinine in the diagnosis of preeclampsia: A systematic review

Objective. To determine the accuracy of the urine protein:creatinine ratio (pr:cr) in predicting 300 mg of protein in 24-hour urine collection in pregnant patients with suspected preeclampsia. ^ Methods. A systematic review was performed. Articles were identified through electronic databases and the relevant citations were hand searching of textbooks and review articles. Included studies evaluated patients for suspected preeclampsia with a 24-hour urine sample and a pr:cr. Only English language articles were included. The studies that had patients with chronic illness such as chronic hypertension, diabetes mellitus or renal impairment were excluded from the review. Two researchers extracted accuracy data for pr:cr relative to a gold standard of 300 mg of protein in 24-hour sample as well as population and study characteristics. The data was analyzed and summarized in tabular and graphical form. ^ Results. Sixteen studies were identified and only three studies met our inclusion criteria with 510 total patients. The studies evaluated different cut-points for positivity of pr:cr from 130 mg/g to 700 mg/g. Sensitivities and specificities for pr:cr of 130mg/g -150 mg/g were 90-93% and 33-65%, respectively; for a pr:cr of 300 mg/g were 81-95% and 52-80%, respectively; for a pr:cr of 600-700mg/g were 85-87% and 96-97%, respectively. ^ Conclusion. The value of a random pr:cr to exclude pre-eclampsia is limited because even low levels of pr:cr (130-150 mg/g) may miss up to 10% of patients with significant proteinuria. A pr:cr of more than 600 mg/g may obviate a 24-hour collection.^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

Prevalence of gastro esophageal reflux disease following the Montreal Consensus and its association with Helicobacter pylori infection and obesity in a random sample of adults of Ciudad Juarez, Mexico, 2004.

Gastroesophageal reflux disease is a common condition affecting 25 to 40% of the population and causes significant morbidity in the U.S., accounting for at least 9 million office visits to physicians with estimated annual costs of $10 billion. Previous research has not clearly established whether infection with Helicobacter pylori, a known cause of peptic ulcer, atrophic gastritis and non cardia adenocarcinoma of the stomach, is associated with gastroesophageal reflux disease. This study is a secondary analysis of data collected in a cross-sectional study of a random sample of adult residents of Ciudad Juarez, Mexico, that was conducted in 2004 (Prevalence and Determinants of Chronic Atrophic Gastritis Study or CAG study, Dr. Victor M. Cardenas, Principal Investigator). In this study, the presence of gastroesophageal reflux disease was based on responses to the previously validated Spanish Language Dyspepsia Questionnaire. Responses to this questionnaire indicating the presence of gastroesophageal reflux symptoms and disease were compared with the presence of H. pylori infection as measured by culture, histology and rapid urease test, and with findings of upper endoscopy (i.e., hiatus hernia and erosive and atrophic esophagitis). The prevalence ratio was calculated using bivariate, stratified and multivariate negative binomial logistic regression analyses in order to assess the relation between active H. pylori infection and the prevalence of gastroesophageal reflux typical syndrome and disease, while controlling for known risk factors of gastroesophageal reflux disease such as obesity. In a random sample of 174 adults 48 (27.6%) of the study participants had typical reflux syndrome and only 5% (or 9/174) had gastroesophageal reflux disease per se according to the Montreal consensus, which defines reflux syndromes and disease based on whether the symptoms are perceived as troublesome by the subject. There was no association between H. pylori infection and typical reflux syndrome or gastroesophageal reflux disease. However, we found that in this Northern Mexican population, there was a moderate association (Prevalence Ratio=2.5; 95% CI=1.3, 4.7) between obesity (≥30 kg/m2) and typical reflux syndrome. Management and prevention of obesity will significantly curb the growing numbers of persons affected by gastroesophageal reflux symptoms and disease in Northern Mexico. ^

Phosphorus partition and acumulation rates in sediments of ODP Legs 130 and 138

Understanding phosphorus (P) geochemistry and burial in oceanic sediments is important because of the role of P for modulating oceanic productivity on long timescales. We investigated P geochemistry in seven equatorial Pacific sites over the last 53 Ma, using a sequential extraction technique to elucidate sedimentary P composition and P diagenesis within the sediments. The dominant P-bearing component in these sediments is authigenic P (61-86% of total P), followed in order of relative dominance by iron-bound P (7-17%), organic P (3-12%), adsorbed P (2-9%), and detrital P (0-1%). Clear temporal trends in P component composition exist. Organic P decreases rapidly in younger sediments in the eastern Pacific (the only sites with high sample resolution in the younger intervals), from a mean concentration of 2.3 µmol P/g sediment in the 0-1 Ma interval to 0.4 µmol/g in the 5- 6 Ma interval. Over this same time interval, decreases are also observed for iron-bound P (from 2.1 to 1.1 µmol P/g) and adsorbed P (from 1.5 to 0.7 µmol P/g). These decreases are in contrast to increases in authigenic P (from 6.0-9.6 µmol P/g) and no significant changes in detrital P (0.1 µmol P/g) and total P (12 µmol P/g). These temporal trends in P geochemistry suggest that (1) organic matter, the principal shuttle of P to the seafloor, is regenerated in sediments and releases associated P to interstitial waters, (2) P associated with iron-rich oxyhydroxides is released to interstitial waters upon microbial iron reduction, (3) the decrease in adsorbed P with age and depth probably indicates a similar decrease in interstitial water P concentrations, and (4) carbonate fluorapatite (CFA), or another authigenic P-bearing phase, precipitates due to the release of P from organic matter and iron oxyhydroxides and becomes an increasingly significant P sink with age and depth. The reorganization of P between various sedimentary pools, and its eventual incorporation in CFA, has been recognized in a variety of continental margin environments, but this is the first time these processes have been revealed in deep-sea sediments. Phosphorus accumulation rate data from this study and others indicates that the global pre-anthropogenic input rate of P to the ocean (20x10**10 mol P/yr) is about a factor of four times higher than previously thought, supporting recent suggestions that the residence time of P in the oceans may be as short as 10000-20000 years.

Sulphur contents and partition of iron in ODP Leg 112 sediments

Porewaters in site 680 Peru Margin sediments contain dissolved sulfide over a depth of approximately 70 m which, at a sedimentation rate of 0.005 cm/yr, gives a sediment exposure time to dissolved sulfide of about 1.4 Myr. Reactions with dissolved sulfide cause the site 680 sediments to show a progressive decrease in a poorly-reactive silicate iron fraction, defined as the difference between iron extracted by dithionite (FeD) at room temperature and that extracted by boiling concentrated HCl (FeH), normalised to the total iron content (FeT). Straight line plots are obtained for ln[(FeH - FeD)/FeT] against time of burial, from which a first order rate constant of 0.29 1/Myr (equivalent to a half-life of 2.4 Myr) can be derived for the sulfidation of this silicate iron. Comparable half-lives are also found for the same poorly-reactive iron fraction in the nearby site 681 and 684 sediments. This silicate Fe fraction comprises 0.8-1.0% Fe, only 30-60% of which reacts even with 1.5-3 million years exposure to dissolved sulfide. Diagenetic models based on porewater concentrations of sulfate and sulfide, and solid phase iron contents, at site 680 are consistent in indicating that this poorly-reactive iron fraction is only sulfidized on a million year time scale. Silicate iron not extracted by HCl can be regarded as unreactive towards dissolved sulfide on the time scales encountered in marine sediments.