Background

Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV.

Methods

An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers.

Results

Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH₂O for both volume and pressure control cohorts. R² values for predicting peak inspiration pressure (PIP) and additional retained lung volume, V_frc in VC, are R²=0.86 and R²=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and V_frc yield R²=0.86 and R²=0.83. Absolute PIP, PIV and V_frc errors are relatively small.

Conclusions

Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.

中文翻译：

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虚拟病人在重症监护室进行机械通气

背景

机械通气（MV）是重症监护室（ICU）的核心疗法。病人之间和病人内部肺力学和状况的明显差异使MV管理变得困难。准确预测患者对MV设置变化的反应，可以优化，个性化和提高生产效率，从而改善治疗效果并降低成本。这项研究开发了一种通用的数字克隆模型或计算机模拟虚拟患者，以准确预测对MV变化的肺力学。

方法

可识别的非线性磁滞回线模型（HLM）捕获从测量的呼吸机数据中识别出的特定于患者的肺动力。虚拟患者模型的识别和创建是完全自动化的，它使用磁滞回线分析（HLA）方法从临床数据中识别出肺弹性。使用来自18名进行分步募集操作的通气量控制（VC）和14位通气压力控制（PC）的患者的临床数据评估性能。

结果

特定于患者的虚拟患者模型可准确预测PEEP变化至12 cmH ₂ O的肺反应，无论是体积控制还是压力控制。[R ^2倍的值，用于预测峰值吸气压力（PIP）和额外保留肺容积，V _FRC在VC，为R ² = 0.86和R ²为106个预测超过18例患者= 0.90。对于14名PC患者和84个预测，预测峰值吸气量（PIV）和V _frc产生R ² = 0.86和R ² = 0.83。PIP，PIV和V _frc的绝对误差相对较小。

结论

总体结果验证了虚拟患者模型用于捕获和预测特定于患者的肺部力学非线性变化的准确性和多功能性。准确的反应预测使机械和生理相关的虚拟患者能够指导个性化和优化的MV治疗。