m+Medelevator.RU
← Поиск публикаций
Global surgical education : journal of the Association for Surgical Education · 2026

High-fidelity simulation as a translational and educational platform for crisis ventilation: quantifying and mitigating tidal volume maldistribution during shared mechanical ventilation.

СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Inoguchi Yohei, Hatano Koji, Kato Taigo, Kawashima Atsunari, Abe Toyofumi, Fukuhara Shinichiro, Uemura Motohide, Kiuchi Hiroshi, Imamura Ryoichi, Nonomura Norio

Аннотация

BACKGROUND: Shared mechanical ventilation has been proposed during ventilator shortages, but unequal tidal volume delivery remains a major limitation when lung compliance differs. Professional societies have cautioned against this practice, yet the intervention has never been validated clinically, and ethical constraints preclude direct patient testing. Translational simulation-simulation applied beyond education to probe clinical systems, test crisis-driven innovations, and generate actionable safety data-offers a methodological pathway for evaluating interventions that cannot be studied in vivo [1, 2]. In parallel, high-fidelity simulation offers a controlled environment in which clinicians can be taught the physiologic hazards of shared ventilation without risk to patients.

METHODS: Two high-fidelity patient simulators were connected to one ventilator in volume-control assist-control mode using a parallel circuit. One simulator was set to normal compliance and the other to reduced compliance. Tidal volumes were measured before and after adjustment of the higher-flow limb with inline flowmeters. This study was designed as a translational simulation to evaluate a crisis-care equipment configuration under controlled, reproducible conditions. The objective was to use high-fidelity simulation as a controlled, reproducible experimental platform to (1) empirically quantify the compliance-driven tidal volume reduction predicted by respiratory-mechanics theory during shared ventilation, (2) test whether inline flowmeters with adjustable valves partially mitigate this, and (3) evaluate the feasibility of high-fidelity simulation as a translational and educational platform for crisis-care innovations that cannot ethically or logistically be studied in real patients. We hypothesized that (H1) shared ventilation of two lung units with mismatched compliance would produce clinically relevant tidal volume reduction in the low-compliance limb, and (H2) this would be counteracted by inline flowmeters with adjustable valves.

RESULTS: Without adjustment, the low-compliance simulator's delivered volume fell markedly relative to its normal-compliance baseline; after flow regulation, it returned toward baseline at both 900 mL and 1100 mL tidal volume settings.

CONCLUSIONS: This study demonstrates how high-fidelity simulation can function as a translational research platform for crisis-care innovations that are ethically and logistically impossible to study in clinical practice. Beyond confirming the physiologic limitations of unregulated ventilator sharing, the simulation generated reproducible, condition-specific tidal volume data that can inform crisis preparedness protocols and equipment pre-positioning decisions. This application of simulation as a systems-testing and device-evaluation tool, in addition to translating directly into a crisis-preparedness educational scenario, represents an underutilized but critically important role for simulation science in disaster and pandemic preparedness.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s44186-026-00589-1.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты