Pulmonary response prediction through personalized basis functions in a virtual patient model.

Background And Objective: Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings.

Methods: This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance.

Virtual patients for mechanical ventilation in the intensive care unit.

Background: Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost.

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation.

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage.

Accuracy of end-tidal CO2 capnometers in post-cardiac surgery patients during controlled mechanical ventilation.

Background: The determination of end-tidal carbon dioxide (etCO2) is very helpful in cardiac resuscitation for confirmation and monitoring of endotracheal tube placement and as an indicator of return of circulation and effectiveness of chest compressions. There is now also widespread use of capnometry on-site at emergency and trauma fields.

Objective: We studied the accuracy and correlation of three capnometers (EMMA, Medtronic, and Evita) with partial pressure of arterial CO2 (PaCO2) measurements.