3 resultados para ORAL HYGIENE

em Duke University


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BACKGROUND: Hand hygiene noncompliance is a major cause of nosocomial infection. Nosocomial infection cost data exist, but the effect of hand hygiene noncompliance is unknown. OBJECTIVE: To estimate methicillin-resistant Staphylococcus aureus (MRSA)-related cost of an incident of hand hygiene noncompliance by a healthcare worker during patient care. DESIGN: Two models were created to simulate sequential patient contacts by a hand hygiene-noncompliant healthcare worker. Model 1 involved encounters with patients of unknown MRSA status. Model 2 involved an encounter with an MRSA-colonized patient followed by an encounter with a patient of unknown MRSA status. The probability of new MRSA infection for the second patient was calculated using published data. A simulation of 1 million noncompliant events was performed. Total costs of resulting infections were aggregated and amortized over all events. SETTING: Duke University Medical Center, a 750-bed tertiary medical center in Durham, North Carolina. RESULTS: Model 1 was associated with 42 MRSA infections (infection rate, 0.0042%). Mean infection cost was $47,092 (95% confidence interval [CI], $26,040-$68,146); mean cost per noncompliant event was $1.98 (95% CI, $0.91-$3.04). Model 2 was associated with 980 MRSA infections (0.098%). Mean infection cost was $53,598 (95% CI, $50,098-$57,097); mean cost per noncompliant event was $52.53 (95% CI, $47.73-$57.32). A 200-bed hospital incurs $1,779,283 in annual MRSA infection-related expenses attributable to hand hygiene noncompliance. A 1.0% increase in hand hygiene compliance resulted in annual savings of $39,650 to a 200-bed hospital. CONCLUSIONS: Hand hygiene noncompliance is associated with significant attributable hospital costs. Minimal improvements in compliance lead to substantial savings.

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BACKGROUND: West Virginia has the worst oral health in the United States, but the reasons for this are unclear. This pilot study explored the etiology of this disparity using culture-independent analyses to identify bacterial species associated with oral disease. METHODS: Bacteria in subgingival plaque samples from twelve participants in two independent West Virginia dental-related studies were characterized using 16S rRNA gene sequencing and Human Oral Microbe Identification Microarray (HOMIM) analysis. Unifrac analysis was used to characterize phylogenetic differences between bacterial communities obtained from plaque of participants with low or high oral disease, which was further evaluated using clustering and Principal Coordinate Analysis. RESULTS: Statistically different bacterial signatures (P<0.001) were identified in subgingival plaque of individuals with low or high oral disease in West Virginia based on 16S rRNA gene sequencing. Low disease contained a high frequency of Veillonella and Streptococcus, with a moderate number of Capnocytophaga. High disease exhibited substantially increased bacterial diversity and included a large proportion of Clostridiales cluster bacteria (Selenomonas, Eubacterium, Dialister). Phylogenetic trees constructed using 16S rRNA gene sequencing revealed that Clostridiales were repeated colonizers in plaque associated with high oral disease, providing evidence that the oral environment is somehow influencing the bacterial signature linked to disease. CONCLUSIONS: Culture-independent analyses identified an atypical bacterial signature associated with high oral disease in West Virginians and provided evidence that the oral environment influenced this signature. Both findings provide insight into the etiology of the oral disparity in West Virginia.

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BACKGROUND: Edoxaban, an oral direct factor Xa inhibitor, is in development for thromboprophylaxis, including prevention of stroke and systemic embolism in patients with atrial fibrillation (AF). P-glycoprotein (P-gp), an efflux transporter, modulates absorption and excretion of xenobiotics. Edoxaban is a P-gp substrate, and several cardiovascular (CV) drugs have the potential to inhibit P-gp and increase drug exposure. OBJECTIVE: To assess the potential pharmacokinetic interactions of edoxaban and 6 cardiovascular drugs used in the management of AF and known P-gp substrates/inhibitors. METHODS: Drug-drug interaction studies with edoxaban and CV drugs with known P-gp substrate/inhibitor potential were conducted in healthy subjects. In 4 crossover, 2-period, 2-treatment studies, subjects received edoxaban 60 mg alone and coadministered with quinidine 300 mg (n = 42), verapamil 240 mg (n = 34), atorvastatin 80 mg (n = 32), or dronedarone 400 mg (n = 34). Additionally, edoxaban 60 mg alone and coadministered with amiodarone 400 mg (n = 30) or digoxin 0.25 mg (n = 48) was evaluated in a single-sequence study and 2-cohort study, respectively. RESULTS: Edoxaban exposure measured as area under the curve increased for concomitant administration of edoxaban with quinidine (76.7 %), verapamil (52.7 %), amiodarone (39.8 %), and dronedarone (84.5 %), and exposure measured as 24-h concentrations for quinidine (11.8 %), verapamil (29.1 %), and dronedarone (157.6 %) also increased. Administration of edoxaban with amiodarone decreased the 24-h concentration for edoxaban by 25.7 %. Concomitant administration with digoxin or atorvastatin had minimal effects on edoxaban exposure. CONCLUSION: Coadministration of the P-gp inhibitors quinidine, verapamil, and dronedarone increased edoxaban exposure. Modest/minimal effects were observed for amiodarone, atorvastatin, and digoxin.