moitvivt Моделирование, оптимизация и информационные технологии Modeling, Optimization and Information Technology 2310-6018 Издательство 10.26102/2310-6018/2026.56.5.015 2388 Анализ современных подходов и формализация параметров для управления режимами искусственной вентиляции легких Analysis of modern approaches and formalization of parameters for the management of mechanical ventilation modes 0000-0003-2917-535X Фролов Сергей Владимирович Frolov Sergey Vladimirovich Sergej.frolov@gmail.com aff-1 0009-0003-3188-1269 Судаков Дмитрий Евгеньевич Sudakov Dmitry Evgenyevich sudakov.dima1702@yandex.ru aff-2 Долгов Егор Павлович Dolgov Egor Pavlovich Toveg2@yandex.ru aff-3 Тамбовский государственный технический университет Tambov State Technical University Тамбовский государственный технический университет Tambov State Technical University Тамбовский государственный технический университет Tambov State Technical University 01 01 2026 1 1 10.26102/2310-6018/2026.56.5.015 Copyright © Авторы, 2026 2026 This work is licensed under a Creative Commons Attribution 4.0 International License

Актуальность работы обусловлена высокой потребностью в респираторной поддержке пациентов отделений реанимации и интенсивной терапии (до 50 % больных) и значительным риском вентилятор-ассоциированных повреждений легких (ВАПЛ) при неоптимальных настройках аппарата искусственной вентиляции легких (ИВЛ). Современные аппараты ИВЛ предлагают десятки режимов и более 50 параметров, что создает высокую когнитивную нагрузку на врача и повышает вероятность ошибок. Цель работы – систематизация знаний о современных режимах ИВЛ и формализация ключевых параметров респираторной поддержки для последующего построения интеллектуальных алгоритмов поддержки принятия врачебных решений (СППВР). В работе использованы методы аналитического обзора, классификации, математического моделирования механики дыхания и формализации клинических критериев. Проведен анализ факторов, обосновывающих необходимость СППВР: сложность интерпретации механики дыхания (комплаенс, сопротивление, движущее давление), высокая частота осложнений при ошибках настройки (баротравма у 10–15 % пациентов при давлении плато >30 см H₂O), дефицит времени врача отделения реанимации и интенсивной терапии и нестандартизованная номенклатура режимов у разных производителей. Выполнена классификация режимов ИВЛ по уровню интеллектуализации (от принудительных до полностью автоматизированных) и детально описаны ключевые параметры вентиляции (дыхательный объем, частота, давления, поток, PEEP). Формализованы четыре группы параметров для выбора режима: механика легких (статический комплаенс, сопротивление, давление плато, P0.1, движущее давление), газообмен (PaO₂/FiO₂, PaCO₂, SpO₂), активность пациента (частота дыхания, признаки асинхронии) и гемодинамика (артериальное давление, центральное венозное давление). Предложены конкретные критерии для каждого параметра. Разработана логическая схема выбора режима на основе формализованных параметров. Полученные результаты создают основу для построения продукционной базы знаний СППВР, позволяющей врачу в условиях цейтнота получать обоснованные рекомендации. Дальнейшие исследования должны быть направлены на клиническую валидацию предложенных критериев и разработку объяснимых алгоритмов искусственного интеллекта для персонализации респираторной поддержки.

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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The authors declare that there are no conflicts of interest present.