Cong Zhou1, J Geoffrey Chase2, Jennifer Knopp3, Qianhui Sun3, Merryn Tawhai4, Knut Möller5, Serge J Heines6, Dennis C Bergmans6, Geoffrey M Shaw7, Thomas Desaive8. 1. School of Civil Aviation, Northwestern Polytechnical University, China; Department of Mechanical Engineering, University of Canterbury, New Zealand. 2. Department of Mechanical Engineering, University of Canterbury, New Zealand. Electronic address: Geoff.Chase@canterbury.ac.nz. 3. Department of Mechanical Engineering, University of Canterbury, New Zealand. 4. Auckland Bio-Engineering Institute (ABI), University of Auckland, New Zealand. 5. Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany. 6. Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, the Netherlands. 7. Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand. 8. GIGA-In Silico Medicine, Institute of Physics, University of Liege, Liege, Belgium.
Abstract
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 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc 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.
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 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc 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.
Authors: Xinxiu Li; Eun Jung Lee; Sandra Lilja; Danuta R Gawel; Barbara Bohle; Mikael Benson; Joseph Loscalzo; Samuel Schäfer; Martin Smelik; Maria Regina Strobl; Oleg Sysoev; Hui Wang; Huan Zhang; Yelin Zhao Journal: Genome Med Date: 2022-05-06 Impact factor: 15.266