Tusman Gerardo, Scandurra Adriana G, Böhm Stephan H, Echeverría Noelia I, Meschino Gustavo, Kremeier P, Sipmann Fernando Suarez
Department of Anesthesiology, Hospital Privado de Comunidad, Mar del Plata, Buenos Aires, 7600, Argentina.
Bioengineering Laboratory, Facultad de Ingeniería, ICYTE-CONICET, Universidad Nacional de, Mar del Plata, Argentina.
J Clin Monit Comput. 2025 Apr;39(2):415-425. doi: 10.1007/s10877-024-01253-z. Epub 2024 Dec 26.
To investigate the feasibility of non-invasively estimating the arterial partial pressure of carbon dioxide (PaCO) using a computational Adaptive Neuro-Fuzzy Inference System (ANFIS) model fed by noninvasive volumetric capnography (VCap) parameters. In 14 lung-lavaged pigs, we continuously measured PaCO with an optical intravascular catheter and VCap on a breath-by-breath basis. Animals were mechanically ventilated with fixed settings and subjected to 0 to 22 cmHO of positive end-expiratory pressure steps. The resultant 8599 pairs of data points - one PaCO value matched with twelve Vcap and ventilatory parameters derived in one breath - fed the ANFIS model. The data was separated into 7370 data points for training the model (85%) and 1229 for testing (15%). The ANFIS analysis was repeated 10 independent times, randomly mixing the total data points. Bland-Altman plot (accuracy and precision), root mean square error (quality of prediction) and four-quadrant and polar plots concordance indexes (trending ability) between reference and estimated PaCO were analyzed. The Bland-Altman plot performed in 10 independent tested ANFIS models showed a mean bias between reference and estimated PaCO of 0.03 ± 0.03 mmHg, with limits of agreement of 2.25 ± 0.42 mmHg, and a root mean square error of 1.15 ± 0.06 mmHg. A good trending ability was confirmed by four quadrant and polar plots concordance indexes of 95.5% and 94.3%, respectively. In an animal lung injury model, the Adaptive Neuro-Fuzzy Inference System model fed by noninvasive volumetric capnography parameters can estimate PaCO with high accuracy, acceptable precision, and good trending ability.
为研究使用由无创容积式二氧化碳描记法（VCap）参数驱动的计算自适应神经模糊推理系统（ANFIS）模型无创估计动脉血二氧化碳分压（PaCO₂）的可行性。在14只肺灌洗猪中，我们使用光学血管内导管逐次呼吸连续测量PaCO₂和VCap。动物采用固定设置进行机械通气，并接受0至22 cmH₂O的呼气末正压递增。由此产生的8599对数据点——一个PaCO₂值与一次呼吸中得出的12个VCap和通气参数相匹配——用于训练ANFIS模型。数据被分为7370个数据点用于训练模型（85%）和1229个用于测试（15%）。ANFIS分析独立重复进行10次，对所有数据点进行随机混合。分析了参考值与估计的PaCO₂之间的Bland-Altman图（准确性和精密度）、均方根误差（预测质量）以及四象限和极坐标图一致性指数（趋势能力）。在10个独立测试的ANFIS模型中进行的Bland-Altman图显示，参考值与估计的PaCO₂之间的平均偏差为0.03±0.03 mmHg，一致性界限为2.25±0.42 mmHg，均方根误差为1.15±0.06 mmHg。四象限和极坐标图一致性指数分别为95.5%和94.3%，证实了良好的趋势能力。在动物肺损伤模型中，由无创容积式二氧化碳描记法参数驱动的自适应神经模糊推理系统模型能够以高精度、可接受的精密度和良好的趋势能力估计PaCO₂。
