Utilization of angiotensin converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB) in patients diagnosed with diabetes: Analysis from the National Ambulatory Medical Care Survey

AbstractObjectiveThe objective of this study was to determine if a difference exists in the proportion of visits for the prescribing of angiotensin converting enzyme inhibitors (ACEI), or angiotensin receptor blockers (ARBs) in diabetic patients during 2007–2010.MethodsThis retrospective, cross-sectional, observational study included adults diagnosed with diabetes mellitus from the National Ambulatory Medical Care Survey (NAMCS) during 2007–2010. Weighted chi-square tests and a multivariable logistic regression model were used to analyze associations between ACEI/ARB prescriptions and predictors of interest. Odds ratios and 95% confidence intervals were reported.ResultsAn unweighted total of 13,590 outpatient ambulatory care visits were identified for adult patients with diabetes without contraindications to ACEIs or ARBs in the NAMCS for the years studied. No statistically significant increase in the proportion of visits with an ACEI/ARB prescription was identified for years 2007–2010 (28.1% in 2007 to 32.2% in 2010). Females (OR 0.78, 95% CI 0.69- 0.89), patients 18–39 years old (OR 0.56, 95% CI 0.43- 0.75), and Medicare users (OR 0.81, 95% CI 0.70- 0.94) were significantly less likely to receive an ACEI/ARB prescription. Patients with hypertension (OR 2.80, 95% CI 2.39-3.29), hyperlipidemia (OR 1.42, 95% CI 1.22-1.65), and ischemic heart disease (OR 1.36, 95% CI 1.10-1.70) were significantly more likely to receive an ACEI/ARB prescription.ConclusionsDespite extensive evidence showing the benefits of ACEI/ARB medications in diabetic patients, disparities of treatment remain evident

An evaluation of Gout visits in the United States for the years 2007 to 2011

Abstract Background This study analyzed visits for and factors associated with gout and gout medication treatment trends for the years 2007–2011 in the United States given the introduction of febuxostat, the first new treatment option for gout in over 40 years, which was introduced to the market in 2009. Methods This study was a retrospective, cross-sectional, observational study of patients age 20 and older seen by providers who participated in the National Ambulatory Medical Care Survey (NAMCS), the National Hospital Ambulatory Medical Care Survey Outpatient Department (NHAMCS-OPD) or Emergency Department (NHAMCS-ED) in the United States. The outcome of interest was visits for gout diagnosis and visits where a gout medication was prescribed. Results Approximately 1.2% of visits had a diagnosis of gout. There was a significant increase in the percentage of visits with a diagnosis of gout in years 2009–2011 compared to 2007–2008, which remained after adjusting for covariates of interest. Groups more likely to have a visit with gout included those ≥65 and 45–64 (both as compared to those 20–44), the African-American and ‘Other’ race groups (as compared to Caucasians) and those on a diuretic. Groups less likely to have a visit with gout included females, Hispanic/Latinos, those with insurance type of ‘Other’ and Medicaid (both as compared to private insurance) and visits to a hospital emergency setting (as compared to physician’s office visits). Conclusion Although there was a significant increase in visits where gout is diagnosed across study years, the overall percentage of visits with a gout diagnosis is low in the US population. Treatment trends over the study years has remained consistent, with the introduction of febuxostat appearing to have little impact for the study years through 2011

Factors of interrupting chemotherapy in patients with Advanced Non-Small-Cell Lung Cancer

<p>Abstract</p> <p>Background</p> <p>Little is known about prognosis of metastatic patients after receiving a first-line treatment and failure. Our group already showed in pre-treated patients enrolled in phase I clinical trials that a performance status (PS) > 2 and an LDH > 600 UI/L were independent prognostic factors. In this prospective study, which included 45 patients, we identified clinical and biological variables as outcome predictors in metastatic Non-Small Cell lung cancer after first line chemotherapy were identified.</p> <p>Findings</p> <p>Forty-five patients that were previously treated for metastatic disease from 12/2000 to 11/2005 in the comprehensive cancer centre (Centre Léon Bérard). Clinical assessment and blood parameters were recorded and considered. Patient prognostic factors for overall survival (OS) with a 0.05-significance level in univariate analysis were entered in a multivariate Cox model for further analysis.</p> <p>Patients' median age was 58.5 years (range: 37 - 76). Sixty two percent of the patients were PS = 0 or 1. After inclusion, nine patients received second-line (22.5%), and two received third-line chemotherapy (5%). Univariate analysis showed that the factors associated with reduced OS were: PS > 2, weight loss >10%, more than one line of chemotherapy treatment and abnormal blood parameters (hemoglobin (Hb), platelet and neutrophils counts). Multiple regression analysis confirmed that PS > 2 and abnormal hemoglobin were independent predictors for low overall survival. According to the presence of none (33%), 1 (37%) and 2 (30%) prognostic factors, median OS were 12, 5 and 2 months respectively.</p> <p>Conclusion</p> <p>From this prospective study, both PS and anemia were found as independent determinants of survival, we found that both PS and anemia were independent determinants of survival. The combination of poor PS and anemia is an effective strategy to predict survival in the case of patients with metastatic NSCLC receiving further treatment after the first line.</p

Predicting sample size required for classification performance

<p>Abstract</p> <p>Background</p> <p>Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.</p> <p>Methods</p> <p>We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.</p> <p>Results</p> <p>A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).</p> <p>Conclusions</p> <p>This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.</p