Monitoring diabetes in patients with and without rheumatoid arthritis: a Medicare study

Introduction: Diabetes mellitus is a key predictor of mortality in rheumatoid arthritis (RA) patients. Both RA and diabetes increase the risk of cardiovascular disease (CVD), yet understanding of how comorbid RA impacts the receipt of guideline-based diabetes care is limited. The purpose of this study was to examine how the presence of RA affected hemoglobin A1C (A1c) and lipid measurement in older adults with diabetes.Methods: Using a retrospective cohort approach, we identified beneficiaries ≥65 years old with diabetes from a 5% random national sample of 2004 to 2005 Medicare patients (N = 256,331), then examined whether these patients had comorbid RA and whether they received guideline recommended A1c and lipid testing in 2006. Multivariate logistic regression was used to examine the effect of RA on receiving guideline recommended testing, adjusting for baseline sociodemographics, comorbidities and health care utilization.Results: Two percent of diabetes patients had comorbid RA (N = 5,572). Diabetes patients with comorbid RA were more likely than those without RA to have baseline cardiovascular disease (such as 17% more congestive heart failure), diabetes-related complications including kidney disease (19% higher), lower extremity ulcers (77% higher) and peripheral vascular disease (32% higher). In adjusted models, diabetes patients with RA were less likely to receive recommended A1c testing (odds ratio (OR) 0.84, CI 0.80 to 0.89) than those without RA, but were slightly more likely to receive lipid testing (OR 1.08, CI 1.01 to 1.16).Conclusions: In older adults with diabetes, the presence of comorbid RA predicted lower rates of A1c testing but slightly improved lipid testing. Future research should examine strategies to improve A1c testing in patients with diabetes and RA, in light of increased CVD and microvascular risks in patients with both conditions. © 2012 Bartels et al.; licensee BioMed Central Ltd

Alzheimer's disease biomarkers in Black and non-Hispanic White cohorts: A contextualized review of the evidence

Black Americans are disproportionately affected by dementia. To expand our understanding of mechanisms of this disparity, we look to Alzheimer's disease (AD) biomarkers. In this review, we summarize current data, comparing the few studies presenting these findings. Further, we contextualize the data using two influential frameworks: the National Institute on Aging–Alzheimer's Association (NIA-AA) Research Framework and NIA's Health Disparities Research Framework. The NIA-AA Research Framework provides a biological definition of AD that can be measured in vivo. However, current cut-points for determining pathological versus non-pathological status were developed using predominantly White cohorts—a serious limitation. The NIA's Health Disparities Research Framework is used to contextualize findings from studies identifying racial differences in biomarker levels, because studying biomakers in isolation cannot explain or reduce inequities. We offer recommendations to expand study beyond initial reports of racial differences. Specifically, life course experiences associated with racialization and commonly used study enrollment practices may better account for observations than exclusively biological explanations

Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk

Abstract Background This paper explores the importance of electronic medical records (EMR) for predicting 30-day all-cause non-elective readmission risk of patients and presents a comparison of prediction performance of commonly used methods. Methods The data are extracted from eight Advocate Health Care hospitals. Index admissions are excluded from the cohort if they are observation, inpatient admissions for psychiatry, skilled nursing, hospice, rehabilitation, maternal and newborn visits, or if the patient expires during the index admission. Data are randomly and repeatedly divided into fitting and validating sets for cross validations. Approaches including LACE, STEPWISE logistic, LASSO logistic, and AdaBoost, are compared with sample sizes varying from 2,500 to 80,000. Results Our results confirm that LACE has moderate discrimination power with the area under receiver operating characteristic curve (AUC) around 0.65-0.66, which can be improved to 0.73-0.74 when additional variables from EMR are considered. These variables include Inpatient in the last six months, Number of emergency room visits or inpatients in the last year, Braden score, Polypharmacy, Employment status, Discharge disposition, Albumin level, and medical condition variables such as Leukemia, Malignancy, Renal failure with hemodialysis, History of alcohol substance abuse, Dementia and Trauma. When sample size is small (≤5000), LASSO is the best; when sample size is large (≥20,000), the predictive performance is similar. The STEPWISE method has a slightly lower AUC (0.734) comparing to LASSO (0.737) and AdaBoost (0.737). More than one half of the selected predictors can be false positives when using a single method and a single division of fitting/validating data. Conclusions True predictors can be identified by repeatedly dividing data into fitting/validating subsets and referring the final model based on summarizing results. LASSO is a better alternative to the STEPWISE logistic regression, especially when sample size is not large. The evidence for adequate sample size can be explored by fitting models on gradually reduced samples. Our model comparison strategy is not only good for 30-day all-cause non-elective readmission risk predictions, but also applicable to other types of predictive models in clinical studies