Respiratory care
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Clinical alarms, including those for mechanical ventilation, have been one of the leading causes of health technology hazards. It has been reported that < 15% of alarms studied rose to the level of being clinically relevant or actionable. Most alarms in health care, whether by default or intention, are set to a hypothetical average patient, which is essentially a one size fits most approach. ⋯ Observations of human response to stimuli suggest that response to alarms is closely matched to the perceived reliability and value of the alarm system. This paper provides a review examining vulnerabilities in the current management of mechanical ventilation alarms and summarizes best practices identified to help prevent patient injury. This review examines the factors that affect alarm utility and provides recommendations for applying research findings to improve safety for patients, clinician efficiency, and clinician well-being.
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The estimation of pleural pressure with esophageal manometry has been used for decades, and it has been a fertile area of physiology research in healthy subject as well as during mechanical ventilation in patients with lung injury. However, its scarce adoption in clinical practice takes its roots from the (false) ideas that it requires expertise with years of training, that the values obtained are not reliable due to technical challenges or discrepant methods of calculation, and that measurement of esophageal pressure has not proved to benefit patient outcomes. Despites these criticisms, esophageal manometry could contribute to better monitoring, optimization, and personalization of mechanical ventilation from the acute initial phase to the weaning period. This review aims to provide a comprehensive but comprehensible guide addressing the technical aspects of esophageal catheter use, its application in different clinical situations and conditions, and an update on the state of the art with recent studies on this topic and on remaining questions and ways for improvement.
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The diaphragm is vulnerable to injury during mechanical ventilation, and diaphragm dysfunction is both a marker of severity of illness and a predictor of poor patient outcome in the ICU. A combination of factors can result in diaphragm weakness. ⋯ Several monitoring tools can be used to assess diaphragm activity and function during mechanical ventilation, including pressure-derived parameters, electromyography, and ultrasound. This review details these techniques and presents the rationale for a diaphragm-protective ventilation strategy.
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Mechanical ventilation is an indispensable form of life support for patients undergoing general anesthesia or experiencing respiratory failure in the setting of critical illness. These patients are at risk for a number of complications related to both their underlying disease states and the mechanical ventilation itself. Intensive monitoring is required to identify early signs of clinical worsening and to minimize the risk of iatrogenic harm. ⋯ Assessments of driving pressure, transpulmonary pressure, and the pressure-volume loop are performed to ensure that adequate PEEP is applied and excess distending pressure is minimized. Finally, monitoring and frequent adjustment of airway cuff pressures is performed to minimize the risk of airway injury and ventilator-associated pneumonia. We will discuss monitoring during mechanical ventilation with a focus on the accuracy, ease of use, and effectiveness in preventing harm for each of these monitoring modalities.
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The electronic health record allows the assimilation of large amounts of clinical and laboratory data. Big data describes the analysis of large data sets using computational modeling to reveal patterns, trends, and associations. ⋯ First, a general overview is provided for the layperson and introduces key concepts, definitions, best practices, and things to watch out for when reading a paper that incorporates machine learning. Second, recent publications at the intersection of big data, machine learning, and mechanical ventilation are presented.