Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function

Abstract

Mechanical ventilation is a life-support therapy for intensive care patients suffering from respiratory failure. To reduce the current rate of ventilator-induced lung injury requires ventilator settings that are patient-, time-, and disease-specific. A common lung protective strategy is to optimise the level of positive end-expiratory pressure (PEEP) through a recruitment manoeuvre to prevent alveolar collapse at the end of expiration and to improve gas exchange through recruitment of additional alveoli. However, this process can subject parts of the lung to excessively high pressures or volumes. This research significantly extends and more robustly validates a previously developed pulmonary mechanics model to predict lung mechanics throughout recruitment manoeuvres. In particular, the process of recruitment is more thoroughly investigated and the impact of the inclusion of expiratory data when estimating peak inspiratory pressure is assessed. Data from the McREM trial and CURE pilot trial were used to test model predictive capability and assumptions. For PEEP changes of up to and including 14 cmH 2 O, the parabolic model was shown to improve peak inspiratory pressure prediction resulting in less than 10% absolute error in the CURE cohort and 16% in the McREM cohort. The parabolic model also better captured expiratory mechanics than the exponential model for both cohorts.

Three Alveolar Phenotypes Govern Lung Function in Murine Ventilator-Induced Lung Injury

“…Understanding how these mechanisms are associated across length scales is critical for developing approaches to mitigating them. Structure-function correlations may also improve clinical practice by defining the mechanistic basis for diagnostic parameters such as the driving pressure ( Aoyama et al, 2018 ; Pereira Romano et al, 2020 ) and subject-specific computational models ( Hamlington et al, 2016b ; Mellenthin et al, 2019 ; Morton et al, 2019 ) that seek to optimize ventilation based on patient-specific alveolar mechanics.…”

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

“…2) Retrospective mode: This mode allows analysis of retrospective patient data. Virtual patients can be simulated in this mode for research purposes [10], [19], [20]. It also enables user training or testing.…”

“…As a result, patients ventilated with excessive airway pressure or tidal volume can develop ventilator induced lung injury (VILI), increasing morbidity and mortality [4], [9]. Thus, accurate, predictive, and patientspecific MV strategies could significantly advance MV care, and minimize both VILI and mortality [10], [11].…”

“…Model-based methods are a growing means of personalising care [13]- [16]. While many models can successfully capture lung dynamics [17], very few can accurately predict the pulmonary response over time [10], [18]- [20]. In addition, these model-based works are performed retrospectively, and there is a need of a platform to enable real-time MV data monitoring, processing and to provide decision support information to the clinicians.…”

Oxygen Concentration’s Effects on Respiratory Mechanics During Recruitment Manoeuvre

“…To the editor, We read with great interest the recent work by Morton et al 1 We would like to draw attention to the oxygen concentration used during the recruitment maneuver and its possible detrimental effects on respiratory mechanics. While the pressure values and times that were applied during the recruitment maneuver were given in detail, no information is provided on the gas concentrations used.…”