Riedlinger Axel, Kretschmer Jörn, Möller Knut
Institute of Technical Medicine, Furtwangen University, Jakob-Kienzle-Straße 17, 78054, Villingen-Schwenningen, Germany.
Biomed Eng Online. 2015 Sep 4;14:82. doi: 10.1186/s12938-015-0077-6.
Successful application of mechanical ventilation as a life-saving therapy implies appropriate ventilator settings. Decision making is based on clinicians' knowledge, but can be enhanced by mathematical models that determine the individual patient state by calculating parameters that are not directly measurable. Evaluation of models may support the clinician to reach a defined treatment goal. Bedside applicability of mathematical models for decision support requires a robust identification of the model parameters with a minimum of measuring effort. The influence of appropriate data selection on the identification of a two-parameter model of pulmonary gas exchange was analyzed.
The model considers a shunt as well as ventilation-perfusion-mismatch to simulate a variety of pathologic pulmonary gas exchange states, i.e. different severities of pulmonary impairment. Synthetic patient data were generated by model simulation. To incorporate more realistic effects of measurement errors, the simulated data were corrupted with additive noise. In addition, real patient data retrieved from a patient data management system were used retrospectively to confirm the obtained findings. The model was identified to a wide range of different FiO 2 settings. Just one single measurement was used for parameter identification. Subsequently prediction performance was obtained by comparing the identified model predicted oxygen level in arterial blood either to exact data taken from simulations or patients measurements.
Structural identifiability of the model using one single measurement for the identification process could be demonstrated. Minimum prediction error of blood oxygenation depends on blood gas level at the time of system identification i.e. the measurement situation. For severe pulmonary impairment, higher FiO 2 settings were required to achieve a better prediction capability compared to less impaired pulmonary states. Plausibility analysis with real patient data could confirm this finding.
Dependent on patients' pulmonary state, the influence of ventilator settings (here FiO 2) on model identification of the gas exchange model could be demonstrated. To maximize prediction accuracy i.e. to find the best individualized model with as few data as possible, best ranges of FiO 2-settings for parameter identification were obtained. A less effort identification process, which depends on the pulmonary state, can be deduced from the results of this identifiability analysis.
机械通气作为一种挽救生命的治疗方法，其成功应用意味着要有合适的通气机设置。决策基于临床医生的知识，但通过数学模型可以得到加强，这些模型通过计算一些无法直接测量的参数来确定个体患者的状态。对模型的评估可以帮助临床医生实现既定的治疗目标。数学模型在床边用于决策支持时，需要以最少的测量工作量对模型参数进行可靠识别。分析了适当的数据选择对肺气体交换双参数模型识别的影响。
该模型考虑了分流以及通气-灌注不匹配，以模拟各种病理性肺气体交换状态，即不同严重程度的肺损伤。通过模型模拟生成合成患者数据。为了纳入测量误差的更现实影响，模拟数据被添加噪声破坏。此外，回顾性使用从患者数据管理系统获取的真实患者数据来证实所得结果。该模型针对广泛的不同FiO₂设置进行了识别。仅使用一次测量进行参数识别。随后，通过将识别出的模型预测的动脉血氧水平与从模拟或患者测量中获取的准确数据进行比较，获得预测性能。
可以证明在识别过程中使用单次测量对模型进行结构可识别性。血液氧合的最小预测误差取决于系统识别时的血气水平，即测量情况。对于严重的肺损伤，与肺损伤较轻的状态相比，需要更高的FiO₂设置才能获得更好的预测能力。对真实患者数据的合理性分析可以证实这一发现。
取决于患者的肺部状态，可以证明通气机设置（此处为FiO₂）对气体交换模型识别的影响。为了最大限度地提高预测准确性，即使用尽可能少的数据找到最佳个体化模型，获得了用于参数识别的FiO₂设置的最佳范围。从这种可识别性分析的结果中可以推断出一个依赖于肺部状态的工作量较小的识别过程。
