Recent advances in respiratory monitory in nonoperating room anesthesia

Mandel, Jeff E.

doi:10.1097/aco.0000000000000606

Cited by 8 publications

(9 citation statements)

References 28 publications

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“…These devices utilize a sensor placed on the neck to detect air flow in the pharynx produced during inhalation and exhalation. 29 In general, these devices are quite effective compared to exhaled CO 2 monitoring. [30][31][32][33] In the first study comparing the newer acoustic technology to capnometry, Mimoz et al 30 reported that it correlated well with capnometry in the evaluation of f in extubated postsurgical subjects.…”

Section: Acoustic Breathing Frequency Monitoringmentioning

confidence: 99%

“…Pulse oximetry-derived f monitoring, or a photoplethysmographic respiratory monitor, detects changes in intrapleural pressures that are transmitted to the cardiovascular system. 29 Eisenberg et al 38 evaluated this technology in healthy subjects and compared it to acoustic f monitoring; both methods were compared to capnography as well. The researchers reported that the pulse oximetry-derived f was more likely to detect bradypnea and may be more reliable than acoustic f monitoring in the presence of routine patient activities.…”

Section: Other Technology For Breathing Frequency Monitoringmentioning

confidence: 99%

“…Although promising, more studies are needed because this technology has not yet shown clinical superiority over capnography or acoustic f monitoring. 29 Thoracic impedance devices have also been evaluated and compared to acoustic f monitoring and capnography. Guechi et al 39 noted that acoustic f monitoring was more accurate than thoracic impedance in subjects hospitalized for drug or alcohol poisoning.…”

Section: Other Technology For Breathing Frequency Monitoringmentioning

confidence: 99%

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Monitoring Breathing Frequency, Pattern, and Effort

Scott

Kaur

2020

Respir Care

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Monitoring respiratory values such as breathing frequency, minute ventilation, breathing effort, and dyspnea are common in acute care. There is evidence that accurate monitoring and interpretation of these values leads to early identification and treatment of impending respiratory failure. Despite this evidence, some values, such as breathing frequency, are largely undervalued in the clinical setting. The undervaluation of breathing frequency is complex and will require a multifaceted approach, including education and improved technology, to reestablish its clinical potential. Many questions remain regarding how to most efficiently and effectively monitor other respiratory values, like noninvasive minute volume and breathing effort, as well. As technology continues to improve alongside the understanding of respiratory physiology, clinicians are able to apply basic clinical assessment skills and technology together to improve patient safety and outcomes.

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Section: Acoustic Breathing Frequency Monitoringmentioning

confidence: 99%

Section: Other Technology For Breathing Frequency Monitoringmentioning

confidence: 99%

Section: Other Technology For Breathing Frequency Monitoringmentioning

confidence: 99%

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Monitoring Breathing Frequency, Pattern, and Effort

Scott

Kaur

2020

Respir Care

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“…Capnography is a waveform display of CO2 concentration in a gas mixture. It is often measured in intubated patients to confirm tracheal tube position and also to evaluate patient ventilation [6][7][8]. Capnography can also be measured in non-intubated patients using side-stream capnography; while the patient is breathing into a cannula, exhaled air is sampled and transferred through a long tube for processing.…”

Section: Introductionmentioning

confidence: 99%

Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients

et al. 2021

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Background Capnography waveform contains essential information regarding physiological characteristics of the airway and thus indicative of the level of airway obstruction. Our aim was to develop a capnography-based, point-of-care tool that can estimate the level of obstruction in patients with asthma and COPD. Methods Two prospective observational studies conducted between September 2016 and May 2018 at Rabin Medical Center, Israel, included healthy, asthma and COPD patient groups. Each patient underwent spirometry test and continuous capnography, as part of, either methacholine challenge test for asthma diagnosis or bronchodilator reversibility test for asthma and COPD routine evaluation. Continuous capnography signal, divided into single breaths waveforms, were analyzed to identify waveform features, to create a predictive model for FEV1 using an artificial neural network. The gold standard for comparison was FEV1 measured with spirometry. Measurements and main results Overall 160 patients analyzed. Model prediction included 32/88 waveform features and three demographic features (age, gender and height). The model showed excellent correlation with FEV1 (R = 0.84), R2 achieved was 0.7 with mean square error of 0.13. Conclusion In this study we have developed a model to evaluate FEV1 in asthma and COPD patients. Using this model, as a point-of-care tool, we can evaluate the airway obstruction level without reliance on patient cooperation. Moreover, continuous FEV1 monitoring can identify disease fluctuations, response to treatment and guide therapy. Trial registration clinical trials.gov, NCT02805114. Registered 17 June 2016, https://clinicaltrials.gov/ct2/show/NCT02805114

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“…Endotracheal intubation or laryngeal mask airway (LMA) placement and mechanical ventilation are recommended for safe anesthesia management as general anesthesia agents can cause upper airway obstruction and/or respiratory depression [5,8]. However, general anesthesia or sedation with natural airways and spontaneous breathing are often employed outside operating theatre [9,10]. It is also known that not many MRI offices are equipped with special MRI compatible (non-magnetic) monitors, suctions, infusion pumps, laryngoscopes and anesthesia stations, especially in low income countries.…”

Section: Introductionmentioning

confidence: 99%