Respiratory care
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Ventilator graphic monitoring is common in ICUs. The graphic information provides clinicians with immediate clues regarding patient-ventilator interaction and ventilator function. These display tools are aimed at reducing complications associated with mechanical ventilation, such as patient-ventilator asynchrony. ⋯ Ventilator graphics impact mechanical ventilation management through optimizing effectiveness of patient care and enhancing promptness of clinician response. Despite being a valuable asset in providing high-quality patient care, many bedside clinicians do not have a thorough understanding of ventilator graphics. Mastery of ventilator graphics interpretation is key in managing patients who are receiving ventilatory support.
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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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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.
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Respiratory compromise is a common and potentially dangerous complication of patients admitted to general care units of hospitals. There are several distinct and disparate pathophysiologic trajectories of respiratory deterioration that hospitalized patients may suffer. Obstructive sleep apnea and preexisting cardiopulmonary disease increase the risk of respiratory failure after major surgery. ⋯ Early warning systems that utilize analysis of intermittently collected vital signs may result in earlier recognition of clinical deterioration. Continuous monitoring of oximetry and capnography may allow the detection of pathophysiologic abnormalities earlier in patients in general care units, but the evidence for improved clinical outcomes remains weak. Increased monitoring may lead to increased monitor alarms that can have negative effects on patient care.