Abstract:A significant percentage of mechanically ventilated patients in Intensive Care Units (ICUs) show some type of patient-ventilator asynchrony (PVA). The presence of PVA is associated with complications that affect the clinical outcome and the goals for which mechanical ventilation is used in critically ill patients. Currently, mechanical ventilators are able to show different types of waveforms that allow to identify the different types of PVA in a noninvasive and reliable way. However, in order to perform an ad… Show more
“…14 a, whilst it could still be identified as flow asynchrony with 3 segments for cases with a clear pressure drop in the beginning of the inspiration. However, these 7 types of asynchronies cover the most typical asynchronies reported in other research [ 14 , 20 , 21 , 24 , 38 ].…”
Section: Methodsmentioning
confidence: 74%
“…Figure 14 shows the typical PV curves of the seven types of asynchronies using the real patient data collected from the clinical trial. In particular, identification of the seven most common types of asynchronies [ 14 , 20 , 21 , 24 , 38 ] based on the shape of the approximated PV loop and the calculated model parameters are implemented as follows: Fig. 14 Hysteretic characteristic for asynchrony …”
Section: Methodsmentioning
confidence: 99%
“…However, subjective bedside waveform analysis lacks accuracy and robustness, and is not continuous. In particular, research indicates less than 25% of ICU health professionals could identify all typical types of patient asynchronies [ 14 , 41 ], where each asynchrony type merits a specific therapeutic strategy to reduce asynchrony and prolonged weaning [ 24 ]. Therefore, automated, computer algorithm-based recognition of asynchrony types has been evaluated to overcome subjective bias and the lack of a continuous measurement, and thus improve care and outcomes [ 7 , 22 , 37 , 39 , 40 , 55 ].…”
Section: Introductionmentioning
confidence: 99%
“…Whilst patient asynchrony may occur in various forms, seven types of patient–ventilator interactions have been recognized as the most common asynchronies, in specific, flow asynchrony, ineffective effort, reverse triggering, auto triggering, double triggering, premature cycling, and delayed cycling [ 14 , 20 , 21 , 24 , 38 ]. However, automated algorithms in the literature focused on identification of only one or a few types of asynchronies, and thus did not generalize well over a wide range of asynchronous scenarios [ 7 , 11 , 22 , 37 , 39 , 40 , 55 ].…”
Background
Patient–ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure–volume (PV) loop.
Methods
Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.
Results
The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.
Conclusions
The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.
“…14 a, whilst it could still be identified as flow asynchrony with 3 segments for cases with a clear pressure drop in the beginning of the inspiration. However, these 7 types of asynchronies cover the most typical asynchronies reported in other research [ 14 , 20 , 21 , 24 , 38 ].…”
Section: Methodsmentioning
confidence: 74%
“…Figure 14 shows the typical PV curves of the seven types of asynchronies using the real patient data collected from the clinical trial. In particular, identification of the seven most common types of asynchronies [ 14 , 20 , 21 , 24 , 38 ] based on the shape of the approximated PV loop and the calculated model parameters are implemented as follows: Fig. 14 Hysteretic characteristic for asynchrony …”
Section: Methodsmentioning
confidence: 99%
“…However, subjective bedside waveform analysis lacks accuracy and robustness, and is not continuous. In particular, research indicates less than 25% of ICU health professionals could identify all typical types of patient asynchronies [ 14 , 41 ], where each asynchrony type merits a specific therapeutic strategy to reduce asynchrony and prolonged weaning [ 24 ]. Therefore, automated, computer algorithm-based recognition of asynchrony types has been evaluated to overcome subjective bias and the lack of a continuous measurement, and thus improve care and outcomes [ 7 , 22 , 37 , 39 , 40 , 55 ].…”
Section: Introductionmentioning
confidence: 99%
“…Whilst patient asynchrony may occur in various forms, seven types of patient–ventilator interactions have been recognized as the most common asynchronies, in specific, flow asynchrony, ineffective effort, reverse triggering, auto triggering, double triggering, premature cycling, and delayed cycling [ 14 , 20 , 21 , 24 , 38 ]. However, automated algorithms in the literature focused on identification of only one or a few types of asynchronies, and thus did not generalize well over a wide range of asynchronous scenarios [ 7 , 11 , 22 , 37 , 39 , 40 , 55 ].…”
Background
Patient–ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure–volume (PV) loop.
Methods
Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.
Results
The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.
Conclusions
The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.
“…In clinical settings, the primary way to detect VD is by manually examining ventilator waveforms such as pressuretime, volume-time, and flow-time signals at the bedside [12][13][14][15]. This process is labor-intensive and full of diagnostic inaccuracies because the recognition ability of the individual clinician drives the outcome [16].…”
Section: (Which Was Not Certified By Peer Review)mentioning
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