School of Engineering, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.
Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia.
Biomed Eng Online. 2022 Feb 11;21(1):13. doi: 10.1186/s12938-022-00981-0.
Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration.
A stochastic model of E is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each.
From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol.
Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
机械通气（MV）是治疗呼吸衰竭患者的主要手段。MV 参数的设置通常基于一般临床指南、直觉和经验。这种方法不是针对患者个体的，因此患者可能会接受不理想、潜在有害的 MV 治疗。本研究提出了 Stochastic integrated VENT（SiVENT）方案，该方案结合了先前工作中 VENT 方案的基于模型的方法，并结合随机建模来考虑患者呼吸弹性随时间的变化。
将 E 的随机模型整合到先前工作中的 VENT 方案中，开发 SiVENT 方案，以考虑患者内和患者间的变异性。使用基于回顾性患者数据的 20 名虚拟 MV 患者队列来验证该方法在容量控制（VC）通气中的性能。进行了性能评估，其中在 1080 个实例中分别实施了 SiVENT 和 VENT 方案，以比较这两种方案，并评估每种方案在减少可能的 MV 设置方面的差异。
从最初的 189000 种可能的 MV 设置组合中，VENT 方案将数量减少到中位数 10612，在整个队列中减少了 94.4%。通过整合随机模型组件，SiVENT 方案将数量从 189000 减少到中位数 9329，在整个队列中减少了 95.1%。与 VENT 方案相比，SiVENT 方案为用户提供的可能组合数量减少了 1000 多个。
在选择 MV 设置的基于模型的方法中添加随机模型组件可以提高决策支持系统推荐患者特定 MV 设置的能力。它特别考虑了呼吸弹性的患者内和患者间变异性，并根据临床推荐的压力阈值消除潜在有害的设置。临床输入和本地协议可以进一步减少安全设置组合的数量。SiVENT 方案的结果证明了进一步研究其预测准确性和临床验证试验的合理性。
