Analysis of modern approaches and formalization of parameters for the management of mechanical ventilation modes

The relevance of this study is determined by the high need for respiratory support in intensive care unit patients (up to 50 % of patients) and the significant risk of ventilator-associated lung injury (VALI) due to suboptimal ventilator settings. Modern mechanical ventilators offer dozens of modes and over 50 adjustable parameters, creating a high cognitive load on the physician and increasing the likelihood of errors. The aim of this work is to systematize knowledge about modern mechanical ventilation modes and formalize key parameters of respiratory support for the subsequent development of intelligent clinical decision support systems (CDSS). The study employs methods of analytical review, classification, mathematical modeling of respiratory mechanics, and formalization of clinical criteria. An analysis of factors justifying the need for CDSS was performed: the complexity of interpreting respiratory mechanics (compliance, resistance, driving pressure), the high incidence of complications due to incorrect settings (barotrauma in 10–15 % of patients with plateau pressure >30 cm H₂O), time constraints for ICU physicians, and non-standardized nomenclature of modes across different manufacturers. A classification of ventilation modes by level of intelligence (from mandatory to fully automated) is provided, and key ventilation parameters (tidal volume, rate, pressures, flow, PEEP) are described in detail. Four groups of parameters for mode selection are formalized: lung mechanics (static compliance, resistance, plateau pressure, P0.1, driving pressure), gas exchange (PaO₂/FiO₂, PaCO₂, SpO₂), patient activity (respiratory rate, asynchrony signs), and hemodynamics (blood pressure, central venous pressure). Specific criteria for each parameter are proposed. A logical algorithm for mode selection based on formalized parameters is developed. The obtained results provide a foundation for building a production rule base for CDSS, enabling physicians in time-critical situations to receive justified recommendations. Further research should focus on clinical validation of the proposed criteria and the development of explainable artificial intelligence algorithms for personalizing respiratory support.

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