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PURPOSE OF REVIEW: The Centers for Disease Control and Prevention (CDC) recently transitioned from ventilator-associated pneumonia (VAP) surveillance to ventilator-associated event (VAE) surveillance in adult inpatient settings. Since the transition, several modifications have been made to improve surveillance methods, and there is a growing body of data regarding the epidemiology, risk factors, and preventability of VAEs. RECENT FINDINGS: The VAE surveillance definition algorithm is based on objective criteria and includes three tiers: ventilator-associated conditions, infection-related ventilator-associated complications, and possible and probable VAP. VAE surveillance expands the purview of surveillance beyond pneumonia alone to include additional complications of mechanical ventilation. Most VAEs are caused by pneumonia, pulmonary edema, atelectasis, or acute respiratory distress syndrome. VAEs are associated with adverse outcomes including prolonged mechanical ventilation, longer intensive care and hospital length-of-stay, and higher mortality rates. Studies to date suggest that minimizing sedation and optimizing fluid management can reduce VAE rates. SUMMARY: We review the CDC's recent updates on VAE surveillance definitions, methods, and tools, and provide an overview of the growing evidence base for VAE as a patient safety measure. Further work is needed to affirm and extend the current knowledge about how best to prevent VAEs.
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Neumonía Asociada al Ventilador , Vigilancia en Salud Pública , Cuidados Críticos , Humanos , Respiración ArtificialRESUMEN
BACKGROUND: BioSense is the US national automated biosurveillance system. Data regarding chief complaints and diagnoses are automatically pre-processed into 11 broader syndromes (e.g., respiratory) and 78 narrower sub-syndromes (e.g., asthma). The objectives of this report are to present the types of illness and injury that can be studied using these data and the frequency of visits for the syndromes and sub-syndromes in the various data types; this information will facilitate use of the system and comparison with other systems. METHODS: For each major data source, we summarized information on the facilities, timeliness, patient demographics, and rates of visits for each syndrome and sub-syndrome. RESULTS: In 2008, the primary data sources were the 333 US Department of Defense, 770 US Veterans Affairs, and 532 civilian hospital emergency department facilities. Median times from patient visit to record receipt at CDC were 2.2 days, 2.0 days, and 4 hours for these sources respectively. Among sub-syndromes, we summarize mean 2008 visit rates in 45 infectious disease categories, 11 injury categories, 7 chronic disease categories, and 15 other categories. CONCLUSIONS: We present a systematic summary of data that is automatically available to public health departments for monitoring and responding to emergencies.
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Biovigilancia/métodos , Recolección de Datos/instrumentación , Brotes de Enfermedades/estadística & datos numéricos , Centers for Disease Control and Prevention, U.S. , Procesamiento Automatizado de Datos , Hospitales , Humanos , Administración en Salud Pública , Estados Unidos/epidemiología , Heridas y Lesiones/epidemiologíaRESUMEN
Biosurveillance and disease surveillance systems serve different purposes. However, the richness and quality of an existing data stream and infrastructure used in biosurveillance may prove beneficial for any state-based electronic disease surveillance system, especially if an electronic laboratory data feed does not exist between a hospital and state-based system. The use of an Enterprise Application Integration(EAI) engine, such as the BioSense Integrator,will be necessary to map heterogeneous messages into standard representations, then validate and route them [1] to a disparate system. This poster illustrates the use of an existing BioSense Integrator in order to create a unified message to support the exchange of electronic lab messages necessary for reportable disease notification. An evaluation of the infrastructure for data messaging will be examined and presented, along with a cost and benefit analysis between hospital and state-based system.
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Redes de Comunicación de Computadores/normas , Brotes de Enfermedades , Vigilancia de la Población/métodos , HumanosRESUMEN
The beaks of Darwin's finches and other birds are among the best known examples of adaptive evolution. Beak morphology is usually interpreted in relation to its critical role in feeding. However, the beak also plays an important role in preening, which is the first line of defence against harmful ectoparasites such as feather lice, fleas, bugs, flies, ticks and feather mites. Here, we show a feature of the beak specifically adapted for ectoparasite control. Experimental trimming of the tiny (1-2 mm) maxillary overhang of rock pigeons (Columba livia) had no effect on feeding efficiency, yet triggered a dramatic increase in feather lice and the feather damage they cause. The overhang functions by generating a shearing force against the tip of the lower mandible, which moves forward remarkably quickly during preening, at up to 31 timesper second. This force damages parasite exoskeletons, significantly enhancing the efficiency of preening for parasite control. Overhangs longer than the natural mean of 1.6mm break significantly more often than short overhangs. Hence, stabilizing selection will favour overhangs of intermediate length. The adaptive radiation of beak morphology should be re-assessed with both feeding and preening in mind.