Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model

Abstract Background and objective Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks. Methods This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0–12cmH2O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH2O ahead covering 6x2cmH2O PEEP steps. Results Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH2O, compared with 4.76cmH2O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R2 = 0.93–0.95 if maximum Δ P E E P ≤ 6cmH2O. Predicted dynamic functional residual capacity ( V f r c ) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R2 of 0.84. Conclusions Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.