5 resultados para Time-dependent Analysis


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Aquaporin-1 (AQP1) is a water channel that is highly expressed in tissues with rapid O(2) transport. It has been reported that this protein contributes to gas permeation (CO(2), NO and O(2)) through the plasma membrane. We show that hypoxia increases Aqp1 mRNA and protein levels in tissues, namely mouse brain and lung, and in cultured cells, the 9L glioma cell line. Stopped-flow light-scattering experiments confirmed an increase in the water permeability of 9L cells exposed to hypoxia, supporting the view that hypoxic Aqp1 up-regulation has a functional role. To investigate the molecular mechanisms underlying this regulatory process, transcriptional regulation was studied by transient transfections of mouse endothelial cells with a 1297 bp 5' proximal Aqp1 promoter-luciferase construct. Incubation in hypoxia produced a dose- and time-dependent induction of luciferase activity that was also obtained after treatments with hypoxia mimetics (DMOG and CoCl(2)) and by overexpressing stabilized mutated forms of HIF-1α. Single mutations or full deletions of the three putative HIF binding domains present in the Aqp1 promoter partially reduced its responsiveness to hypoxia, and transfection with Hif-1α siRNA decreased the in vitro hypoxia induction of Aqp1 mRNA and protein levels. Our results indicate that HIF-1α participates in the hypoxic induction of AQP1. However, we also demonstrate that the activation of Aqp1 promoter by hypoxia is complex and multifactorial and suggest that besides HIF-1α other transcription factors might contribute to this regulatory process. These data provide a conceptual framework to support future research on the involvement of AQP1 in a range of pathophysiological conditions, including edema, tumor growth, and respiratory diseases.

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The impact of antimicrobial resistance on clinical outcomes is the subject of ongoing investigations, although uncertainty remains about its contribution to mortality. We investigated the impact of carbapenem resistance on mortality in Pseudomonas aeruginosa bacteremia in a prospective multicenter (10 teaching hospitals) observational study of patients with monomicrobial bacteremia followed up for 30 days after the onset of bacteremia. The adjusted influence of carbapenem resistance on mortality was studied by using Cox regression analysis. Of 632 episodes, 487 (77%) were caused by carbapenem-susceptible P. aeruginosa (CSPA) isolates, and 145 (23%) were caused by carbapenem-resistant P. aeruginosa (CRPA) isolates. The median incidence density of nosocomial CRPA bacteremia was 2.3 episodes per 100,000 patient-days (95% confidence interval [CI], 1.9 to 2.8). The regression demonstrated a time-dependent effect of carbapenem resistance on mortality as well as a significant interaction with the Charlson index: the deleterious effect of carbapenem resistance on mortality decreased with higher Charlson index scores. The impact of resistance on mortality was statistically significant only from the fifth day after the onset of the bacteremia, reaching its peak values at day 30 (adjusted hazard ratio for a Charlson score of 0 at day 30, 9.9 [95% CI, 3.3 to 29.4]; adjusted hazard ratio for a Charlson score of 5 at day 30, 2.6 [95% CI, 0.8 to 8]). This study clarifies the relationship between carbapenem resistance and mortality in patients with P. aeruginosa bacteremia. Although resistance was associated with a higher risk of mortality, the study suggested that this deleterious effect may not be as great during the first days of the bacteremia or in the presence of comorbidities.

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The impact of antimicrobial resistance on clinical outcomes is the subject of ongoing investigations, although uncertainty remains about its contribution to mortality. We investigated the impact of carbapenem resistance on mortality in Pseudomonas aeruginosa bacteremia in a prospective multicenter (10 teaching hospitals) observational study of patients with monomicrobial bacteremia followed up for 30 days after the onset of bacteremia. The adjusted influence of carbapenem resistance on mortality was studied by using Cox regression analysis. Of 632 episodes, 487 (77%) were caused by carbapenem-susceptible P. aeruginosa (CSPA) isolates, and 145 (23%) were caused by carbapenem-resistant P. aeruginosa (CRPA) isolates. The median incidence density of nosocomial CRPA bacteremia was 2.3 episodes per 100,000 patient-days (95% confidence interval [CI], 1.9 to 2.8). The regression demonstrated a time-dependent effect of carbapenem resistance on mortality as well as a significant interaction with the Charlson index: the deleterious effect of carbapenem resistance on mortality decreased with higher Charlson index scores. The impact of resistance on mortality was statistically significant only from the fifth day after the onset of the bacteremia, reaching its peak values at day 30 (adjusted hazard ratio for a Charlson score of 0 at day 30, 9.9 [95% CI, 3.3 to 29.4]; adjusted hazard ratio for a Charlson score of 5 at day 30, 2.6 [95% CI, 0.8 to 8]). This study clarifies the relationship between carbapenem resistance and mortality in patients with P. aeruginosa bacteremia. Although resistance was associated with a higher risk of mortality, the study suggested that this deleterious effect may not be as great during the first days of the bacteremia or in the presence of comorbidities.

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To evaluate the long-term impact of successive interventions on rates of methicillin-resistant Staphylococcus aureus (MRSA) colonization or infection and MRSA bacteremia in an endemic hospital-wide situation. DESIGN:Quasi-experimental, interrupted time-series analysis. The impact of the interventions was analyzed by use of segmented regression. Representative MRSA isolates were typed by use of pulsed-field gel electrophoresis. SETTING:A 950-bed teaching hospital in Seville, Spain. PATIENTS:All patients admitted to the hospital during the period from 1995 through 2008. METHODS:Three successive interventions were studied: (1) contact precautions, with no active surveillance for MRSA; (2) targeted active surveillance for MRSA in patients and healthcare workers in specific wards, prioritized according to clinical epidemiology data; and (3) targeted active surveillance for MRSA in patients admitted from other medical centers. RESULTS:Neither the preintervention rate of MRSA colonization or infection (0.56 cases per 1,000 patient-days [95% confidence interval {CI}, 0.49-0.62 cases per 1,000 patient-days]) nor the slope for the rate of MRSA colonization or infection changed significantly after the first intervention. The rate decreased significantly to 0.28 cases per 1,000 patient-days (95% CI, 0.17-0.40 cases per 1,000 patient-days) after the second intervention and to 0.07 cases per 1,000 patient-days (95% CI, 0.06-0.08 cases per 1,000 patient-days) after the third intervention, and the rate remained at a similar level for 8 years. The MRSA bacteremia rate decreased by 80%, whereas the rate of bacteremia due to methicillin-susceptible S. aureus did not change. Eighty-three percent of the MRSA isolates identified were clonally related. All MRSA isolates obtained from healthcare workers were clonally related to those recovered from patients who were in their care. CONCLUSION:Our data indicate that long-term control of endemic MRSA is feasible in tertiary care centers. The use of targeted active surveillance for MRSA in patients and healthcare workers in specific wards (identified by means of analysis of clinical epidemiology data) and the use of decolonization were key to the success of the program.

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BACKGROUND Only multifaceted hospital wide interventions have been successful in achieving sustained improvements in hand hygiene (HH) compliance. METHODOLOGY/PRINCIPAL FINDINGS Pre-post intervention study of HH performance at baseline (October 2007-December 2009) and during intervention, which included two phases. Phase 1 (2010) included multimodal WHO approach. Phase 2 (2011) added Continuous Quality Improvement (CQI) tools and was based on: a) Increase of alcohol hand rub (AHR) solution placement (from 0.57 dispensers/bed to 1.56); b) Increase in frequency of audits (three days every three weeks: "3/3 strategy"); c) Implementation of a standardized register form of HH corrective actions; d) Statistical Process Control (SPC) as time series analysis methodology through appropriate control charts. During the intervention period we performed 819 scheduled direct observation audits which provided data from 11,714 HH opportunities. The most remarkable findings were: a) significant improvements in HH compliance with respect to baseline (25% mean increase); b) sustained high level (82%) of HH compliance during intervention; c) significant increase in AHRs consumption over time; c) significant decrease in the rate of healthcare-acquired MRSA; d) small but significant improvements in HH compliance when comparing phase 2 to phase 1 [79.5% (95% CI: 78.2-80.7) vs 84.6% (95% CI:83.8-85.4), p<0.05]; e) successful use of control charts to identify significant negative and positive deviations (special causes) related to the HH compliance process over time ("positive": 90.1% as highest HH compliance coinciding with the "World hygiene day"; and "negative":73.7% as lowest HH compliance coinciding with a statutory lay-off proceeding). CONCLUSIONS/SIGNIFICANCE CQI tools may be a key addition to WHO strategy to maintain a good HH performance over time. In addition, SPC has shown to be a powerful methodology to detect special causes in HH performance (positive and negative) and to help establishing adequate feedback to healthcare workers.