An RTNI promoting a best-practice glycemic control order set was successful in modestly lowering mean glucose levels and substantially reducing the use of LCI and PIOHMs.
In the advent of increased use of technology in diabetes care, the management of CSII and CGM devices in the inpatient setting has not been well described. We conducted a non-incentivized survey among inpatient licensed independent practitioners (LIP) at 4 large US hospital systems to assess their knowledge, comfort, and behaviors related to the use of CSII and CGM in the inpatient setting. Of 128 respondents, 83% were day and night hospitalists, 10% advanced practice providers, and 7% primary care physicians. Most LIPs worked in academic centers (68%), 18% at community teaching hospitals, with 93% having diabetes consult service, and 55% having dedicated teams to assist with use of CSII and CGM. Most respondents (96%) rated inpatient hyperglycemia treatment as important, and 93% of LIPs agreed that CSII should be continued in the inpatient setting in the absence of contraindication. However, only half (49%) reported reviewing CSII settings on admission, and of those, only 50% were confident with interpreting those parameters to guide therapy. Almost two-thirds (64% ) of providers reported knowledge of a CSII policy at their institution. The most common barriers to continuing CSII included lack of LIP knowledge and perceived lack of nursing knowledge regarding CSII. LIPs were less familiar with the use of CGM, with 72% being unaware or not at all familiar with their institution’s policy. Of 64 respondents who cared for a patient with CGM in the preceding 6 months, 56% reported not reviewing CGM data to guide therapy. Most inpatient providers value glycemic control and use of DM technology; however, many reported limited in-depth knowledge of CSII and CGM devices for optimal use to guide clinical care in the inpatient setting. Educational programs and collaboration between LIPs and endocrinologists are needed to safely optimize the use of CSII and CGM in the hospital setting. Disclosure N. Z. Madhun: None. R. J. Galindo: Consultant; Self; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk, Sanofi US, Valeritas, Inc., Research Support; Self; Dexcom, Inc., Novo Nordisk. J. Donato: None. P. Hwang: None. H. F. Shabbir: None. M. Fowler: Other Relationship; Self; AstraZeneca. E. Molitch-hou: None. G. E. Umpierrez: Research Support; Self; AstraZeneca, Dexcom, Inc., Novo Nordisk. M. Lansang: Consultant; Self; Sanofi, Research Support; Self; Dexcom, Inc.
In microencapsulation, composition of wall materials defines the characteristics of microcapsules, their targeted use, and bioavailability of the core material. Sodium alginate (SA) is a good candidate as a cold method to produce microcapsules, but its high cost, sodium foot prints, and digestive intolerance are the constrains. In the present study, SA was partially substituted with starch (S), pectin (P), and whey (W) to encapsulate wheat germ oil (WGO). Encapsulation efficiency was highest at 20% and 30% replacement of SA. The angle of repose was highest for S. Oil release studies were conducted in hexane, simulated gastric and intestinal fluid (SGF and SIF) without enzymes. SA with S had highest mean release in hexane (60.07%), SFG (21.11%), and SIF (72.25%) followed by P and W. The amount of oil released was lower in lower core to coating ratios and substitution of SA with polymers caused an increase in oil release. Novelty impact statement:• Encapsulation efficiency was highest at 20% and 30% replacement of sodium alginate.• The addition of pectin increased the mean size of microcapsules of wheat germ oil.• Oil released percentage in simulated gastric and intestinal fluid was less in lower core to coating ratios.
Background Peer comparison reduces unnecessary outpatient antibiotic prescribing, but no prescribing metric has been validated for inpatient comparison. We aimed to evaluate if an electronically derived antibiotic prescribing metric correlated with indicated antibiotic days in hospitalized patients. Methods We previously created a hospitalist-specific adjusted antibiotic use metric (observed:expected [O:E]) for National Healthcare Safety Network-defined broad-spectrum antibiotics. From May-Oct 2019 at four Emory Healthcare hospitals, we identified outlier hospitalists prescribing in the top (high O:E) and bottom (low O:E) 15th percentile. We randomly selected 10 days of antibiotic administration from each outlier and reviewed days with > 2 days of consecutive days of antibiotics. For pneumonia, chronic obstructive pulmonary disease (COPD), or urinary tract infection (UTI) we determined if each day of antibiotics was indicated, assuming the diagnosis was accurate. We compared high vs. low O:E providers and used regression modeling to determine if the metric predicted indicated days of antibiotics. Results Among 997 days, 510 (51%) were from high and 487 (49%) from low O:E providers. High O:E providers had a greater proportion of days with > 2 prior days of antibiotics (60%) compared to low O:E providers (54%, p = 0.03). In the subset of days with > 2 prior days of antibiotics (n = 569), high O:E providers had more patient-days with longer hospital stays, diabetes and Charlson comorbidity index (CCI) >3, and fewer days supervising (resident/advanced practice provider, Table 1). The primary diagnosis was pneumonia, COPD exacerbation or UTI in 260 (25%) days; 91% were indicated based on duration with no difference between high and low O:E providers (88% vs. 94%, p = 0.1). After controlling for days of hospitalization, CCI, immunocompromised status, and supervisory role, a high O:E was not associated with indicated antibiotic use (OR 0.5, 95% CI 0.2 – 1.3). Description of days with a patient on greater than two days of antibiotics, comparing high- versus low-metric providers Conclusion A high hospitalist antibiotic prescribing metric correlated with patients receiving > 2 consecutive days of antibiotics on any given day but did not predict unindicated antibiotic use for a subset of diagnoses. Evaluating indicated use by validating diagnoses may improve metric performance. Disclosures Jessica Howard-Anderson, MD, Antibacterial Resistance Leadership Group (ARLG) (Other Financial or Material Support, The ARLG fellowship provides salary support for ID fellowship and mentored research training)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.