An open source tool to compute measures of inpatient glycemic control: translating from healthcare analytics research to clinical quality improvement.

Objectives: The objective of this study is to facilitate monitoring of the quality of inpatient glycemic control by providing an open-source tool to compute glucometrics. To allay regulatory and privacy concerns, the tool is usable locally; no data are uploaded to the internet.

Materials And Methods: We extended code, initially developed for healthcare analytics research, to serve the clinical need for quality monitoring of diabetes.

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

Background And Aim: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") patients with GIB at the time of presentation.

Using the glucometrics website to benchmark ICU glucose control before and after the NICE-SUGAR study.

Prior to 2009, intensive glycemic control was the standard in main intensive care units (ICUs). Glucose targets have been recalibrated after publication of the NICE-SUGAR study in that year, followed by updated guidelines that endorsed more moderated control. We sought to determine if the prevalence of hyperglycemia in US ICUs had increased after the NICE-SUGAR study's results were reported.

An internet service supporting quality assessment of inpatient glycemic control.

Background: Several studies have linked the maintenance of normoglycemia in acutely ill inpatients with improved clinical outcomes. We previously proposed a few standard definitions for monitoring inpatient glycemic control, or "glucometrics." In clinical practice, limited data management resources for developing and refining measurement protocols can slow quality improvement efforts.

Computing performance measures for inpatient glucose management.

Recent research indicates that inpatients with hyperglycemia suffer poor outcomes. Efforts to improve glycemic control need measures of performance. We proposed candidate measures, but these require analysis of large glucose datasets, a cumbersome task for individual institutions.

Shallow semantic parsing of randomized controlled trial reports.

In this work, we are measuring the performance of Propbank-based Machine Learning (ML) for automatically annotating abstracts of Randomized Controlled Trials (CTRs) with semantically meaningful tags. Propbank is a resource of annotated sentences from the Wall Street Journal (WSJ) corpus, and we were interested in assessing performance issues when porting this resource to the medical domain. We compare intra-domain (WSJ/WSJ) with cross-domain (WSJ/medical abstract) performance.

"Glucometrics"--assessing the quality of inpatient glucose management.

Background: For patients with diabetes, the quality of outpatient glycemic control is readily assessed by hemoglobin A1c. In contrast, standardized measures for assessing the quality of blood glucose (BG) management in hospitalized patients are lacking. Because of recent studies demonstrating the benefits of strict glycemic control in critically ill patients, hospitals nationwide are dedicating resources to address this important issue.

Data presentation for quality improvement.

Efforts to improve quality of medical care often involve large data sets. Reviewing laboratory results over time for a cohort of patients is particularly problematic: traditional statistics conflate case to case variations with day to day variations (within a case). To help solve this problem, we propose using sparklines for case by case review and a modified box-plot for overall data review.

Inhibition of human LDL oxidation by the neuroprotective drug l-deprenyl.

L-deprenyl (Selegiline) used in the treatment of Parkinson's and Alzheimer's disease also enhances longevity. Oxidized low density lipoprotein promotes atherosclerosis and is toxic to both vascular and neural tissue. The reported association between vascular dysfunction and neurodegenerative diseases prompted us to investigate the effect of l-deprenyl, a MAO-B inhibitor, on low density lipoprotein (LDL) oxidation.