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Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit

机译:重症监护室机械通气ARDS患者基于模型的最佳PEEP

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摘要

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. Methods: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. Results: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. Conclusions: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods.
机译:背景:呼气末正压(PEEP)的最佳水平在治疗急性呼吸窘迫综合征(ARDS)患者中仍存在广泛争议。当前选择PEEP的方法仅提供一定范围的值,而不能提供针对患者的独特解决方案。基于模型的方法提供了一种使用无创压力量(PV)测量来估计患者可招募性的新颖方法。本文在试点临床试验中研究了此类模型的临床可行性,以辅助治疗,优化患者特异性PEEP,评估疾病状态和随时间推移的反应。方法:10例急性肺损伤或ARDS患者进行了递增的PEEP募集操作。 PV数据以5 cmH(2)O的增量进行测量,并适合募集模型。进行吸气和呼气屏气以测量气道阻力和自动PEEP。基于打开压力,关闭压力和净招聘,使用了三个基于模型的指标来优化PEEP。 ARDS的状态是通过模型参数评估的,该参数捕获了招聘和合规情况。结果:所有患者均经历自动PEEP的通货膨胀和通货紧缩的模型拟合误差中位数分别为2.8%和1.02%。在所有这三个指标的情况下,基于模型的最佳PEEP均高于临床选择的PEEP。随着时间的推移,两名患者经历了多次募集演习,模型指标反映并跟踪了该州或其ARDS。结论:对于ARDS患者,本文提出的基于模型的方法提供了一种独特的,非侵入性的方法来选择最佳的患者特异性PEEP。另外,该模型具有使用这些相同的模型和方法随时间评估疾病状态的能力。

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