In-silico credibility assessment of a computational physiological model for non-invasive monitoring of respiratory effort in critically ill patients

OBJECTIVE: Monitoring respiratory effort in critically ill patients during assisted mechanical ventilation is essential to individualize ventilatory support and prevent over- or underassistance. A non-invasive method to estimate respiratory muscle pressure (P_mus) combines respiratory surface electromyography (sEMG) with ventilator pressure-flow waveforms through the integrated equation of motion (iEqM). Implemented within a computational physiological model (CPM), the iEqM links muscle activation to generated pressure. This study investigates the reliability of an iEqM-based CPM under critical care conditions. Approach: CPM performance to estimate respiratory muscle pressure-time product ((PTP) ̂_mus) was evaluated in-silico using simulated patient profiles. Credibility activities included numerical verification, Monte-Carlo-based uncertainty quantification, and sensitivity analysis, exploring variations in respiratory mechanics, effort variability, sEMG signal quality, and patient-ventilator timing. Model outputs were compared with simulated reference values, with an acceptable clinical error margin set at 20%. Main results: Verification confirmed correct model implementation (errors < 0.3%). Input data uncertainty quantification showed limited variability (SD 1.8%). Sensitivity analysis revealed reduced accuracy under low P_mus variability (< 5.0 cmH₂O), low sEMG signal-to-noise ratios ((SNR) ̂ < 1.4), high P_mus magnitudes (17.5 cmH₂O), and persistent inspiratory efforts during expiration. Calibration using end-expiratory occlusion maneuvers improved accuracy, except at the lowest P_mus magnitude and (SNR) ̂s. Significance: The iEqM performs reliably when calibrated via end-expiratory occlusion maneuvers. Without calibration, accuracy declined with lower effort variability, poorer sEMG quality, or patient-ventilator asynchrony. These findings emphasize the need for context-aware application, accounting for patient-specific mechanics, signal integrity and ventilator interaction, to ensure credible and reliable monitoring of respiratory effort in critically ill patients.