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Background: Patient–ventilator asynchrony (PVA) is a condition that commonly affects patients who are mechanically ventilated. PVA happens when the patient's own breathing effort and the ventilator preset settings are out of sync. Ventilator waveform monitoring is viewed as a difficult undertaking, even for experienced practitioners, despite being a non‐invasive and reliable tool for diagnosing PVA. Aim: To assess the knowledge levels and attitudes of critical care nurses (CCNs) regarding the use of ventilator waveform monitoring to detect PVA. Study Design: A cross‐sectional online survey was conducted in three intensive care units (ICUs) in Alexandria, Egypt. The questionnaire consisted of four parts to evaluate CCNs' level of knowledge and attitude regarding ventilator waveform monitoring and assess their ability to detect PVA. Results: Of the 137 CCNs approached, 101 CCNs completed the survey, resulting in a 73.7% response rate. Most nurses (88.1%) demonstrated poor knowledge levels and negative attitudes (93.1%) towards using waveform monitoring to detect PVA. A significant relationship was found between nurses' knowledge of ventilator waveform monitoring and their participation in previous training programmes on mechanical ventilation (MV; p =.031). Additionally, nurses' attitudes towards ventilator waveform monitoring were significantly associated with their level of education (p =.002) and attendance in previous courses on waveform analysis (p =.020). Conclusions: A majority of CCNs have poor knowledge and negative attitudes regarding ventilator waveform monitoring. Previous training in MV and attendance courses on ventilator waveform analysis showed a significant correlation between nurses' level of knowledge and attitudes regarding ventilator waveform monitoring. Relevance to Clinical Practice: Assessment of CCNs' knowledge and attitudes regarding ventilator waveform monitoring for detecting patient–ventilator asynchrony (PVA) informs the development of future educational programmes, ultimately aiding in the delivery of prompt and high‐quality care. [ABSTRACT FROM AUTHOR]

Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome. [ABSTRACT FROM AUTHOR]

Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.

Background: Ventilator dyssynchrony (VD) can worsen lung injury and is challenging to detect and quantify due to the complex variability in the dyssynchronous breaths. While machine learning (ML) approaches are useful for automating VD detection from the ventilator waveform data, scalable severity quantification and its association with pathogenesis and ventilator mechanics remain challenging., Objective: We develop a systematic framework to quantify pathophysiological features observed in ventilator waveform signals such that they can be used to create feature-based severity stratification of VD breaths., Methods: A mathematical model was developed to represent the pressure and volume waveforms of individual breaths in a feature-based parametric form. Model estimates of respiratory effort strength were used to assess the severity of flow-limited (FL)-VD breaths compared to normal breaths. A total of 93,007 breath waveforms from 13 patients were analyzed., Results: A novel model-defined continuous severity marker was developed and used to estimate breath phenotypes of FL-VD breaths. The phenotypes had a predictive accuracy of over 97% with respect to the previously developed ML-VD identification algorithm. To understand the incidence of FL-VD breaths and their association with the patient state, these phenotypes were further successfully correlated with ventilator-measured parameters and electronic health records., Conclusion: This work provides a computational pipeline to identify and quantify the severity of FL-VD breaths and paves the way for a large-scale study of VD causes and effects. This approach has direct application to clinical practice and in meaningful knowledge extraction from the ventilator waveform data., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024. Published by Elsevier Ltd.)