Publications

[No abstract available]

JTD Keywords: Breathing, Cost, Developed country, Developing countries, Developing country, Health care facility, Home monitoring, Human, Humans, Internet, Internet of things, Letter, Lowest income group, Lung function, Lung mechanics, Lung resistance, Mathematical model, Middle income country, Mobile applications, Non invasive procedure, Open source technology, Oscillometry, Pneumotachygraphy, Telemedicine

Bioimpedance has been widely studied as alternative to respiratory monitoring methods because of its linear relationship with respiratory volume during normal breathing. However, other body tissues and fluids contribute to the bioimpedance measurement. The objective of this study is to investigate the relevance of chest movement in thoracic bioimpedance contributions to evaluate the applicability of bioimpedance for respiratory monitoring. We measured airflow, bioimpedance at four electrode configurations and thoracic accelerometer data in 10 healthy subjects during inspiratory loading. This protocol permitted us to study the contributions during different levels of inspiratory muscle activity. We used chest movement and volume signals to characterize the bioimpedance signal using linear mixed-effect models and neural networks for each subject and level of muscle activity. The performance was evaluated using the Mean Average Percentage Errors for each respiratory cycle. The lowest errors corresponded to the combination of chest movement and volume for both linear models and neural networks. Particularly, neural networks presented lower errors (median below 4.29%). At high levels of muscle activity, the differences in model performance indicated an increased contribution of chest movement to the bioimpedance signal. Accordingly, chest movement contributed substantially to bioimpedance measurement and more notably at high muscle activity levels.

JTD Keywords: Diagnosis, Health care

Even if the performance of a given ventilator has been evaluated in the laboratory under very well controlled conditions, inappropriate maintenance and lack of long-term stability and accuracy of the ventilator sensors may lead to ventilation errors in actual clinical practice. The aim of this study was to evaluate the actual performances of ventilators during clinical routines. A resistance (7.69 cmH(2)O/L/s) - elastance (100 mL/cmH(2)O) test lung equipped with pressure, flow, and oxygen concentration sensors was connected to the Y-piece of all the mechanical ventilators available for patients in four intensive care units (ICUs; n = 66). Ventilators were set to volume-controlled ventilation with tidal volume = 600 mL, respiratory rate = 20 breaths/minute, positive end-expiratory pressure (PEEP) = 8 cmH(2)O, and oxygen fraction = 0.5. The signals from the sensors were recorded to compute the ventilation parameters. The average standard deviation and range (min-max) of the ventilatory parameters were the following: inspired tidal volume = 607 36 (530-723) mL, expired tidal volume = 608 36 (530-728) mL, peak pressure = 20.8 2.3 (17.2-25.9) cmH(2)O, respiratory rate = 20.09 0.35 (19.5-21.6) breaths/minute, PEEP = 8.43 0.57 (7.26-10.8) cmH(2)O, oxygen fraction = 0.49 0.014 (0.41-0.53). The more error-prone parameters were the ones related to the measure of flow. In several cases, the actual delivered mechanical ventilation was considerably different from the set one, suggesting the need for improving quality control procedures for these machines.

JTD Keywords: Equipment and supplies, Medical devices, Intravenous, Quality assurance, Health care quality assessment, Ventilator accuracy, Ventilation error