Experimental dataset: inspiratory pressure and flow optimization for reduction of ventilator-induced lung injury risk

This dataset accompanies the article: Darowski M., Pasledni R., Urbankowski T., Stankiewicz B., Pałko K.J., Kozarski M., Zieliński K.Reducing the risk of ventilator-induced lung injury through inspiratory pressure and flow adaptation: A proof-of-concept study. The dataset contains experimental measurements obtained from a physical lung model (SmartLung 2000, IMT Analytics, Switzerland) connected to a Servo 900B ventilator and monitored using the NICO device. The experiments were performed to compare conventional constant-flow ventilation (NoOpt) with an optimized inspiratory pressure and flow pattern (Opt). Nine resistance–compliance (R–C) combinations were tested:R5, R20, and R50 resistance settings combined with C25, C60, and C75 compliance settings. Each folder corresponds to one R–C combination and contains CSV files for optimized and non-optimized ventilation. The CSV files contain the following variables:u_Q,mV – flow signal values in millivolts [mV] from the analog output of the NICO deviceu_P,mV – pressure signal values in millivolts [mV] from the analog output of the NICO deviceQ,L/min – calculated airflow values expressed in liters per minute [L/min]P,cmH2O – calculated airway pressure values expressed in centimeters of water [cmH2O]N,J/min – calculated ventilatory mechanical power expressed in joules per minute [J/min], computed as pressure × flow × 0.098 This dataset supports the findings reported in the associated publication.Funding: National Science Centre, Poland, Grant No. 2023/50/A/ST7/00498.