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    Nursing Preference for Alcohol-Based Hand Rub Volume
    (Cambridge University Press (CUP), 2019-09) Martinello, Richard A.; Arbogast, James W.; Guercia, Kerri; Parker, Albert E.; Boyce, John M.
    Background:The effectiveness of alcohol-based hand rub (ABHR) is correlated with drying time, which depends on the volume applied. Evidence suggests that there is considerable variation in the amount of ABHR used by healthcare providers. Objective:We sought to identify the volume of ABHR preferred for use by nurses. Methods:A prospective observation study was performed in 8 units at a tertiary-care hospital. Nurses were provided pocket-sized ABHR bottles with caps to record each bottle opening. Nurses were instructed to use the volume of ABHR they felt was best. The average ABHR volume used per hand hygiene event was calculated using cap data and changes in bottle mass. Results:In total, 53 nurses participated and 140 nurse shifts were analyzed. The average ABHR dose was 1.09 mL. This value was greater for non-ICU nurses (1.18 mL) than ICU nurses (0.96 mL), but this difference was not significant. We detected no significant association between hand surface area and preferred average dose volume. The ABHR dose volume was 0.006 mL less per use as the number of applications per shift increased (P = .007).Conclusions:The average dose of ABHR used was similar to the dose provided by the hospital’s automated dispensers, which deliver 1.1 mL per dose. The volume of ABHR dose was inversely correlated with the number of applications of ABHR per shift and was not correlated with hand size. Further research to understand differences and drivers of ABHR volume preferences and whether automated ABHR dosing may create a risk for people with larger hands is warranted.
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    The Chronic Kidney Disease Model: A General Purpose Model of Disease Progression and Treatment.
    (2011-06) Orlando, L. A.; Belasco, Eric J.; Patel, U. D.; Matcher, D. B.
    Background: Chronic kidney disease (CKD) is the focus of recent national policy efforts; however, decision makers must account for multiple therapeutic options, comorbidities and complications. The objective of the Chronic Kidney Disease model is to provide guidance to decision makers. We describe this model and give an example of how it can inform clinical and policy decisions. Methods: Monte Carlo simulation of CKD natural history and treatment. Health states include myocardial infarction, stroke with and without disability, congestive heart failure, CKD stages 1-5, bone disease, dialysis, transplant and death. Each cycle is 1 month. Projections account for race, age, gender, diabetes, proteinuria, hypertension, cardiac disease, and CKD stage. Treatment strategies include hypertension control, diabetes control, use of HMG-CoA reductase inhibitors, use of angiotensin converting enzyme inhibitors, nephrology specialty care, CKD screening, and a combination of these. The model architecture is flexible permitting updates as new data become available. The primary outcome is quality adjusted life years (QALYs). Secondary outcomes include health state events and CKD progression rate. Results: The model was validated for GFR change/year -3.0 ± 1.9 vs. -1.7 ± 3.4 (in the AASK trial), and annual myocardial infarction and mortality rates 3.6 ± 0.9% and 1.6 ± 0.5% vs. 4.4% and 1.6% in the Go study. To illustrate the model's utility we estimated lifetime impact of a hypothetical treatment for primary prevention of vascular disease. As vascular risk declined, QALY improved but risk of dialysis increased. At baseline, 20% and 60% reduction: QALYs = 17.6, 18.2, and 19.0 and dialysis = 7.7%, 8.1%, and 10.4%, respectively. Conclusions: The CKD Model is a valid, general purpose model intended as a resource to inform clinical and policy decisions improving CKD care. Its value as a tool is illustrated in our example which projects a relationship between decreasing cardiac disease and increasing ESRD.
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