Pre-apneic capnography waveform abnormalities during procedural sedation and analgesia

Conway, Aaron; Collins, Peter; Chang, Kristina; Mafeld, Sebastian; Sutherland, Joanna; Fingleton, James; Parotto, Matteo

doi:10.1007/s10877-019-00391-z

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Capnography is widely considered to be an essential aspect of physiological monitoring during sedation. [25][26][27] The concern about reduced ability to monitor capnography waveforms when HFNO is used potentially increasing the risk of more prolonged, undetected episodes of hypoventilation during sedation has been raised previously in the literaure. 28 However, it should be noted that if undetected episodes of hypoventilation were considerably more frequent and prolonged when HFNO was used in our study, presumably, we would have observed higher TcCO2 concentrations in this group.…”

Section: Discussionmentioning

confidence: 99%

“…Another plausible mechanism is that the ability to monitor capnography waveforms was diminished with HFNO. Capnography is widely considered to be an essential aspect of physiological monitoring during sedation 25–27 . The concern about reduced ability to monitor capnography waveforms when HFNO is used, potentially increasing the risk of more prolonged, undetected episodes of hypoventilation during sedation, has been raised previously 28 .…”

Section: Discussionmentioning

confidence: 99%

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High flow nasal oxygen during procedural sedation for cardiac implantable electronic device procedures

Conway

Collins

Chang

et al. 2021

European Journal of Anaesthesiology

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BackgroundHigh flow nasal oxygen may better support the vulnerable breathing state of patients during procedural sedation. ObjectiveThe objective of this study was to investigate the effects of high flow nasal oxygen in comparison to facemask oxygen on ventilation during cardiac implantable electronic device procedures performed with procedural sedation. DesignRandomized controlled trial. SettingThe study was conducted at one academic teaching hospital in Canada.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

High flow nasal oxygen during procedural sedation for cardiac implantable electronic device procedures

Conway

Collins

Chang

et al. 2021

European Journal of Anaesthesiology

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“…Results from prior studies indicated that the use of information about the history of previous respiratory states may be a promising approach for predicting the duration of apneic events. A study of capnography waveform abnormalities during nurse-administered sedation found a two-fold increase in the risk of apnea (hazard ratio [HR] 2.14; 95% CI 1.75-2.62) when a patient was in a state of hypoventilation (defined as >10% reduction in end-tidal CO 2 from baseline) [ 24 ]. The risk of apnea also increased with each additional sedative dose (HR 2.86; 95% CI 2.15-3.81) [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…A study of capnography waveform abnormalities during nurse-administered sedation found a two-fold increase in the risk of apnea (hazard ratio [HR] 2.14; 95% CI 1.75-2.62) when a patient was in a state of hypoventilation (defined as >10% reduction in end-tidal CO 2 from baseline) [ 24 ]. The risk of apnea also increased with each additional sedative dose (HR 2.86; 95% CI 2.15-3.81) [ 24 ]. Results from an earlier study in a different population also supported the observations that the onset of apneic periods during sedation is associated with a previous history of abnormal respiratory state.…”

Section: Discussionmentioning

confidence: 99%

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

Conway¹,

Jungquist²,

Chang³

et al. 2021

JMIR Perioper Med

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Background Capnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a “smart alarm” that can alert clinicians to apneic events that are predicted to be prolonged. Objective To determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds). Methods A secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds). Results A total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy. Conclusions Decision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds.

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Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study

Conway

Rad

Zhou

et al. 2023

J Clin Monit Comput

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Pre-apneic capnography waveform abnormalities during procedural sedation and analgesia

Cited by 4 publications

References 15 publications

High flow nasal oxygen during procedural sedation for cardiac implantable electronic device procedures

High flow nasal oxygen during procedural sedation for cardiac implantable electronic device procedures

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study

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