A virtual patient model for mechanical ventilation. (October 2018)

• Record Type: Journal Article
• Authors: Morton, S.E.; Dickson, J.; Chase, J.G.; Docherty, P.; Desaive, T.; Howe, S.L.; Shaw, G.M.; Tawhai, M.
• Is Part Of: Computer methods and programs in biomedicine. Volume 165(2018)
• Page Start: 77
• Page End: 87
• Publication Date: 2018-10
• Subjects: In-silico -- Virtual patient -- Mechanical ventilation -- Prediction -- Intensive care -- PEEP; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.08.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Titrating PEEP to minimum elastance is a lung protective strategy in mechanical ventilation. Clinical studies have shown excessive PIP can be dangerous. The ability of this model to predict PIP could reduce the risk of titrating PEEP in VCV. Development and validation of predictive in-silico models is presented. Future use in virtual patients could improve clinician confidence and patient safety in delivering care. Abstract: Background and Objectives: Mechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies. Methods: This study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms. Results: Results show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmH2 O. Conclusions: The results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV.