122,036 research outputs found

    Lung Rest During Extracorporeal Membrane Oxygenation for Neonatal Respiratory Failure-Practice Variations and Outcomes.

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    OBJECTIVE: Describe practice variations in ventilator strategies used for lung rest during extracorporeal membrane oxygenation for respiratory failure in neonates, and assess the potential impact of various lung rest strategies on the duration of extracorporeal membrane oxygenation and the duration of mechanical ventilation after decannulation. DATA SOURCES: Retrospective cohort analysis from the Extracorporeal Life Support Organization registry database during the years 2008-2013. STUDY SELECTION: All extracorporeal membrane oxygenation runs for infants less than or equal to 30 days of life for pulmonary reasons were included. DATA EXTRACTION: Ventilator type and ventilator settings used for lung rest at 24 hours after extracorporeal membrane oxygenation initiation were obtained. DATA SYNTHESIS: A total of 3,040 cases met inclusion criteria. Conventional mechanical ventilation was used for lung rest in 88% of cases and high frequency ventilation was used in 12%. In the conventional mechanical ventilation group, 32% used positive end-expiratory pressure strategy of 4-6 cm H2O (low), 22% used 7-9 cm H2O (mid), and 43% used 10-12 cm H2O (high). High frequency ventilation was associated with an increased mean (SEM) hours of extracorporeal membrane oxygenation (150.2 [0.05] vs 125 [0.02]; p \u3c 0.001) and an increased mean (SEM) hours of mechanical ventilation after decannulation (135 [0.09] vs 100.2 [0.03]; p = 0.002), compared with conventional mechanical ventilation among survivors. Within the conventional mechanical ventilation group, use of higher positive end-expiratory pressure was associated with a decreased mean (SEM) hours of extracorporeal membrane oxygenation (high vs low: 136 [1.06] vs 156 [1.06], p = 0.001; mid vs low: 141 [1.06] vs 156 [1.06]; p = 0.04) but increased duration of mechanical ventilation after decannulation in the high positive end-expiratory pressure group compared with low positive end-expiratory pressure (p = 0.04) among survivors. CONCLUSIONS: Wide practice variation exists with regard to ventilator settings used for lung rest during neonatal respiratory extracorporeal membrane oxygenation. Use of high frequency ventilation when compared with conventional mechanical ventilation and use of low positive end-expiratory pressure strategy when compared with mid positive end-expiratory pressure and high positive end-expiratory pressure strategy is associated with longer duration of extracorporeal membrane oxygenation. Further research to provide evidence to drive optimization of pulmonary management during neonatal respiratory extracorporeal membrane oxygenation is warranted

    Randomized crossover comparison of proportional assist ventilation and patient-triggered ventilation in extremely low birth weight infants with evolving chronic lung disease

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    Background: Refinement of ventilatory techniques remains a challenge given the persistence of chronic lung disease of preterm infants. Objective: To test the hypothesis that proportional assist ventilation ( PAV) will allow to lower the ventilator pressure at equivalent fractions of inspiratory oxygen (FiO(2)) and arterial hemoglobin oxygen saturation in ventilator-dependent extremely low birth weight infants in comparison with standard patient-triggered ventilation ( PTV). Methods: Design: Randomized crossover design. Setting: Two level-3 university perinatal centers. Patients: 22 infants ( mean (SD): birth weight, 705 g ( 215); gestational age, 25.6 weeks ( 2.0); age at study, 22.9 days ( 15.6)). Interventions: One 4- hour period of PAV was applied on each of 2 consecutive days and compared with epochs of standard PTV. Results: Mean airway pressure was 5.64 ( SD, 0.81) cm H2O during PAV and 6.59 ( SD, 1.26) cm H2O during PTV ( p < 0.0001), the mean peak inspiratory pressure was 10.3 ( SD, 2.48) cm H2O and 15.1 ( SD, 3.64) cm H2O ( p < 0.001), respectively. The FiO(2) ( 0.34 (0.13) vs. 0.34 ( 0.14)) and pulse oximetry readings were not significantly different. The incidence of arterial oxygen desaturations was not different ( 3.48 ( 3.2) vs. 3.34 ( 3.0) episodes/ h) but desaturations lasted longer during PAV ( 2.60 ( 2.8) vs. 1.85 ( 2.2) min of desaturation/ h, p = 0.049). PaCO2 measured transcutaneously in a subgroup of 12 infants was similar. One infant met prespecified PAV failure criteria. No adverse events occurred during the 164 cumulative hours of PAV application. Conclusions: PAV safely maintains gas exchange at lower mean airway pressures compared with PTV without adverse effects in this population. Backup conventional ventilation breaths must be provided to prevent apnea-related desaturations. Copyright (c) 2007 S. Karger AG, Base

    The Real World of Ventilation Troubleshooting: A Swine Case Study

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    Swine finishing facility ventilation has become relatively complex and is often mismanaged as a system. One of the few ways to truly understand these systems is to spend time systematically going through the many components of the building. To learn to help producers better, a team of university Extension specialists that included agricultural engineers and animal scientists spent an extended period carefully documenting conditions in a deep-pit swine finishing building with two 1,000-head rooms. Exhaust fans in the pit and walls operated at various stages throughout the year as a negative-pressure ventilation system. A computerized controller activated exhaust fans, a ventilation curtain actuator, and heaters. Gravity baffled ceiling inlets were evenly spaced in the building to provide good air distribution during cold and mild weather conditions. Following the review of current conditions and operating parameters, performance deficiencies were identified and recommendations were given regarding controller settings, inlet settings, and curtain management. The overall operating characteristics of the ventilation system and air quality in the animal space were documented ventilation and related management changes were discussed with the owner/operator

    Microscopic-observation drug-susceptibility assay for the diagnosis of TB.

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    BACKGROUND: New diagnostic tools are urgently needed to interrupt the transmission of tuberculosis and multidrug-resistant tuberculosis. Rapid, sensitive detection of tuberculosis and multidrug-resistant tuberculosis in sputum has been demonstrated in proof-of-principle studies of the microscopic-observation drug-susceptibility (MODS) assay, in which broth cultures are examined microscopically to detect characteristic growth. METHODS: In an operational setting in Peru, we investigated the performance of the MODS assay for culture and drug-susceptibility testing in three target groups: unselected patients with suspected tuberculosis, prescreened patients at high risk for tuberculosis or multidrug-resistant tuberculosis, and unselected hospitalized patients infected with the human immunodeficiency virus. We compared the MODS assay head-to-head with two reference methods: automated mycobacterial culture and culture on Löwenstein-Jensen medium with the proportion method. RESULTS: Of 3760 sputum samples, 401 (10.7%) yielded cultures positive for Mycobacterium tuberculosis. Sensitivity of detection was 97.8% for MODS culture, 89.0% for automated mycobacterial culture, and 84.0% for Löwenstein-Jensen culture (P<0.001); the median time to culture positivity was 7 days, 13 days, and 26 days, respectively (P<0.001), and the median time to the results of susceptibility tests was 7 days, 22 days, and 68 days, respectively. The incremental benefit of a second MODS culture was minimal, particularly in patients at high risk for tuberculosis or multidrug-resistant tuberculosis. Agreement between MODS and the reference standard for susceptibility was 100% for rifampin, 97% for isoniazid, 99% for rifampin and isoniazid (combined results for multidrug resistance), 95% for ethambutol, and 92% for streptomycin (kappa values, 1.0, 0.89, 0.93, 0.71, and 0.72, respectively). CONCLUSIONS: A single MODS culture of a sputum sample offers more rapid and sensitive detection of tuberculosis and multidrug-resistant tuberculosis than the existing gold-standard methods used

    Bayesian State Space Modeling of Physical Processes in Industrial Hygiene

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    Exposure assessment models are deterministic models derived from physical-chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters such as generation and ventilation rates are usually of interest since they are difficult to obtain. In this paper we outline a flexible Bayesian framework for parameter inference and exposure prediction. In particular, we propose using Bayesian state space models by discretizing the differential equation models and incorporating information from observed measurements and expert prior knowledge. At each time point, a new measurement is available that contains some noise, so using the physical model and the available measurements, we try to obtain a more accurate state estimate, which can be called filtering. We consider Monte Carlo sampling methods for parameter estimation and inference under nonlinear and non-Gaussian assumptions. The performance of the different methods is studied on computer-simulated and controlled laboratory-generated data. We consider some commonly used exposure models representing different physical hypotheses

    Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode

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    Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can limit the input data available to CDSS. This is especially true in the use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS to make accurate recommendations related to ventilator mode, we developed a highly performant ma-chine learning model that is able to perform per-breath clas-sification of 5 of the most widely used ventilation modes in the USA with an average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and that it is highly robust to missing data caused by software/sensor error
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