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. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV. Methods: An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers. Results: Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH(2)O for both volume and pressure control cohorts. R-2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, V-frc in VC, are R-2 =0.86 and R-2 =0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and V-frc yield R-2 =0.86 and R-2 =0.83. Absolute PIP, PIV and V-frc errors are relatively small. Conclusions: Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The study develops a digital clone model to predict lung mechanics in response to changes in mechanical ventilation, enabling personalised care. Results show that the virtual patient model accurately predicts patient-specific lung responses, guiding personalised and optimised mechanical ventilation therapy.