Анализ современных подходов и формализация параметров для управления режимами искусственной вентиляции легких

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.

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