Estimating patient spontaneous breathing effort in mechanical ventilation using a b-splines function approach

Abstract

Background:Patient work of breathing is a key clinical metric strongly to guide patient care and weaning from mechanical ventilation (MV). Measurement requires added equipment, well-trained clinicians, or/and extra interventions. This study combines a spontaneous breathing effort model using b-spline functions with a nonlinear, predictive MV digital-twin model to monitor patient effort in real-time. Methods:Data from 22 patients for two assisted spontaneous breathing MV modes, NAVA (neurally adjusted ventilatory assist) and PSV (pressure support ventilation), are employed. The patient effort function estimates a pleural pressure Pˆp surrogate of muscular work of breathing induced pressure. To ensure identifiability Pˆp is identified with a negative constraint level of 75%. Estimated patient effort is compared to electrical activity of the diaphragm (EAdi) signals from the NAVA naso-gastric tude, airway pressure, and tidal volume (VT) as well as physiological and clinical expectations. Results:Pˆp generalizes well across the digital twin model and MV modes in comparison to the original single compartment lung model. Strong neuro-muscular correlations are identified with Pˆp compared to EAdi, VT, and airway pressure in NAVA. They are lower in PSV, as expected, as pressure delivery is not a function of EAdi in this MV mode, while the uncontrolled variable VT shows a stronger association with Pˆp than EAdi. Conclusion:The digital twin model relates patient-specific induced breathing effort, modeled as Pˆp, as well as or better than EAdi in both assisted breathing MV modes. Results differ between NAVA and PSV modes due to the poorer patient–ventilator interaction typical in PSV. The ability to estimate patient work of breathing allows non-invasive, real-time quantification of ventilator unloading, heretofore not possible without extra sensors or maneuvers, to help guide weaning or changes in MV settings for assisted spontaneous breathing (ASB) MV modes.