MV-Optimizer is a clinical prognosis software developed to assist physicians in the management of mechanical ventilation (MV), an artificial technique that partially or fully replaces the function of the respiratory muscles of a person who cannot breathe on his own. MV influences natural respiration by improving oxygenation and CO2 elimination while the causes that lead to the need for MV are resolved.
Managing the VM involves the selection and configuration of a significant number of parameters. The high complexity of modern ventilators, the disparity in the nomenclature used between the different brands and the critical situation of the patients who require this therapy make this process a challenging task that mainly falls on the knowledge or know-how of the intensivist physician.
MV-Optimizer facilitates the management and configuration of MV predicting the cardiorespiratory response of ventilated patients. It integrates complex mathematical models that can be dynamically trained and self-adjusted with clinical patient data, thus offering physicians the possibility of testing different ventilator configurations, simulating the patient’s response to these configurations and, therefore, helping the decision making.
MV-Optimizer has been developed by researchers from the BIOART group of the Research Centre for Biomedical Engineering (CREB), in collaboration with the GIBIC group of the University of Antioquia (Medellín, Colombia). Previous validations that involve the use of animal models and real patients under mechanical ventilation have been carried out within the framework of a CIBER intramural collaboration project with the I3PT research group of Parc Taulí.
MV-Optimizer has participated in various technology enhancement programs. It was part of one of the 13 innovative science-based projects of the UPC of the ‘Knowledge Industry’ program and has been selected by IESE in the BTTG technology transfer accelerator program. Currently, it participates in the Barcelona Activa pre-acceleration program, an accompaniment program for entrepreneurial projects in initial phases with a high technological impact.