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BackgroundAmong patients in the United States with chronic kidney disease, black patients are at increased risk for end-stage renal disease, as compared with white patients.MethodsIn two studies, we examined the effects of variants in the gene encoding apolipoprotein L1 (APOL1) on the progression of chronic kidney disease. In the African American Study of Kidney Disease and Hypertension (AASK), we evaluated 693 black patients with chronic kidney disease attributed to hypertension. In the Chronic Renal Insufficiency Cohort (CRIC) study, we evaluated 2955 white patients and black patients with chronic kidney disease (46% of whom had diabetes) according to whether they had 2 copies of high-risk APOL1 variants (APOL1 high-risk group) or 0 or 1 copy (APOL1 low-risk group). In the AASK study, the primary outcome was a composite of end-stage renal disease or a doubling of the serum creatinine level. In the CRIC study, the primary outcomes were the slope in the estimated glomerular filtration rate (eGFR) and the composite of end-stage renal disease or a reduction of 50% in the eGFR from baseline.ResultsIn the AASK study, the primary outcome occurred in 58.1% of the patients in the APOL1 high-risk group and in 36.6% of those in the APOL1 low-risk group (hazard ratio in the high-risk group, 1.88; P

Abstract: Objectives: To compare population-level baseline characteristics, individual-level utilization, and costs between antihypertensive medication users versus nonusers in adults with diabetes and concomitant hypertension. Methods: This longitudinal retrospective observational research used Medical Expenditure Panel Survey household component pooled years 2006 to 2009 to analyze adults 18 years or older with nongestational diabetes and coexistent essential hypertension. Two groups were created: 1) antihypertensive medication users and 2) no antihypertensive pharmacotherapy. We examined average annualized health care costs and emergency department and hospital utilization. Accounting for Medical Expenditure Panel Survey’s complex survey design, all analyses used longitudinal weights. Logistic regressions examined the likelihood of utilization and anytihypertensive medication use, and log-transformed multiple linear regression models assessed costs and antihypertensive medication use. Results: Of the 3261 adults identified with diabetes, 66% (n = 2137) had concomitant hypertension representing 38.7 million individuals during 2006 to 2009. Significantly, the 16% (n = 338) no antihypertensive pharmacotherapy group showed greater mean nights hospitalized (3.6 vs. 1.7, P = 0.0120), greater all-cause hospitalization events per 1000 patient months (41 vs. 24, P = 0.0.007), and lower mean diabetes-related and hypertension-related ambulatory visits. After adjusting for confounders, non-antihypertensive medication users showed 1.64 odds of hospitalization, 29% lower total, and 27% lower average annualized medical expenses compared with antihypertensive medication users. Conclusions: In adults with diabetes and coexistent hypertension, we observed significantly greater hospitalizations and lower costs for the non antihypertensive pharmacotherapy group versus those using antihypertensive medications. The short-term time horizon greater hospitalizations with lower expenses among non-antihypertensive medication users with diabetes and concomitant hypertension warrant further study. [Copyright &y& Elsevier]

Background and Objective: Hypoglycemia is a common safety event when attempting to optimize glycemic control in diabetes (DM). While electronic medical records provide a natural ground for detecting and analyzing hypoglycemia, ICD codes used in the databases may be invalid, insensitive or non-specific in detecting new hypoglycemic events. We developed text preprocessing methods to improve automatic detection of hypoglycemia from analysis of clinical encounter text notes.Methods: We set out to improve hypoglycemia detection from clinical notes by introducing three preprocessing methods: stop word filtering, medication signaling, and ICD narrative enrichment. To test the proposed methods, we selected clinical notes from VA Maryland Healthcare System, based on various combinations of three criteria that are suggestive of hypoglycemia, including ICD-9 code of diabetes and hypoglycemia, laboratory glucose values < 70 md/dL, and text reference to a proximate hypoglycemia event. In addition, we constructed one dataset of 395 clinical notes from year 2009 and another of 460 notes from year 2014 to test the generality of the proposed methods. For each of the datasets, two physician judges manually reviewed individual clinical notes to determine whether hypoglycemia was present or absent. A third physician judge served as a final adjudicator for disagreements.Results: Each of the proposed preprocessing methods contributed to the performance of hypoglycemia detection by significantly increasing the F1 score in the range of 5.3∼7.4% on one dataset (p < .01). Among the methods, stop word filtering contributed most to the performance improvement (7.4%). Combining all the preprocessing methods led to greater performance gain (p < .001) compared with using each method individually. Similar patterns were observed for the other dataset with the F1 score being increased in the range of 7.7%∼9.4% by individual methods (p < .001). Nevertheless, combining the three methods did not yield additional performance gain.Conclusion: The proposed text preprocessing methods improved the performance of hypoglycemia detection from clinical text notes. Stop word filtering achieved the most performance improvement. ICD narrative enrichment boosted the recall of detection. Combining the three preprocessing methods led to additional performance gains. [ABSTRACT FROM AUTHOR]

• Quantifying use-error risk can aid in maximizing system performance and safety. • Heterogeneous user populations present challenges for design validation studies. • An expert-driven methodology for quantifying human performance is proposed. • Method serves as a supplemental alternative to live participant studies. • The method is demonstrated on a diabetes population medical device use case study. Heterogeneous product user populations are common across many safety–critical domains. Catering to variable user needs is critical for designing safe and effective systems. Despite this, it is common for a 1-size-fits-all approach to be applied in design for these populations. Quantifying risk of use error throughout the design process can justify design decisions that maximize system performance and safety. Many regulatory agencies require consideration of user variability in design validation activities. However, there are practical challenges for integrating variable users into these activities. Adequately representing populations requires significant time and monetary commitments for subject recruitment. In addition, population access may be difficult in some cases. In this work, an alternative to traditional human factors design validation efforts is presented. Expert elicitation is proposed as a cost-effective means to quantify heterogenous user performance in the formative product design stages. The approach relies on the generation of generic physical and cognitive tasks that can be applied across use cases. The approach is demonstrated on the diabetes population, specially focusing on medical device use. The output of the demonstration are performance distributions for 27 task-user group pairs that can be integrated into design validation efforts to identify human error risks that require mitigation. [ABSTRACT FROM AUTHOR]