Perdereau Jade, Chamoux Thibaut, Gayat Etienne, Le Gall Arthur, Vallée Fabrice, Cartailler Jérôme, Joachim Jona
From the Université Paris Cité, INSERM UMRS 942 (MASCOT), Paris, France.
Entrepôt de données de santé, Assistance Publique Hôpitaux de Paris, Paris, France.
Anesth Analg. 2025 Jan 1;140(1):119-128. doi: 10.1213/ANE.0000000000007295. Epub 2024 Dec 16.
Due to their invasiveness, arterial lines are not typically used in routine monitoring, despite their superior responsiveness in hemodynamic monitoring and detecting intraoperative hypotension. To address this issue, noninvasive, continuous arterial pressure monitoring is necessary. We developed a deep-learning model that reconstructs continuous mean arterial pressure (MAP) using the photoplethysmograhy (PPG) signal and compared it to the arterial line gold standard.
We analyzed high-frequency PPG signals from 117 patients in neuroradiology and digestive surgery with a median of 2201 (interquartile range [IQR], 788-4775) measurements per patient. We compared models with different combinations of convolutional and recurrent layers using as inputs for our neural network high-frequency PPG and derived features including dicrotic notch relative amplitude, perfusion index, and heart rate. Mean absolute error (MAE) was used as performance metrics. Explainability of the deep-learning model was reconstructed with Grad-CAM, a visualization technique using saliency maps to highlight the parts of an input that are significant for a deep-learning model decision-making process.
An MAP baseline model, which consisted only of standard cuff measures, reached an MAE of 6.1 (± 14.5) mm Hg. In contrast, the deep-learning model achieved an MAE of 3.5 (± 4.4) mm Hg on the external test set (a 42.6% improvement). This model also achieved the narrowest confidence intervals and met international standards used within the community (grade A). The saliency map revealed that the deep-learning model primarily extracts information near the dicrotic notch region.
Our deep-learning model noninvasively estimates arterial pressure with high accuracy. This model may show potential as a decision-support tool in operating-room settings, particularly in scenarios where invasive blood pressure monitoring is unavailable.
尽管动脉导管在血流动力学监测和检测术中低血压方面具有卓越的响应能力，但由于其侵入性，通常不用于常规监测。为解决这一问题，无创连续动脉压监测十分必要。我们开发了一种深度学习模型，该模型利用光电容积脉搏波描记术（PPG）信号重建连续平均动脉压（MAP），并将其与动脉导管这一黄金标准进行比较。
我们分析了神经放射科和消化外科117例患者的高频PPG信号，每位患者的测量次数中位数为2201次（四分位间距[IQR]，788 - 4775次）。我们使用高频PPG和衍生特征（包括重搏波切迹相对振幅、灌注指数和心率）作为神经网络的输入，比较了具有不同卷积层和循环层组合的模型。平均绝对误差（MAE）用作性能指标。深度学习模型的可解释性通过Grad-CAM进行重建，Grad-CAM是一种可视化技术，使用显著性图来突出对深度学习模型决策过程有重要意义的输入部分。
仅由标准袖带测量组成的MAP基线模型的MAE为6.1（±14.5）mmHg。相比之下，深度学习模型在外部测试集上的MAE为3.5（±4.4）mmHg（提高了42.6%）。该模型还实现了最窄的置信区间，并符合该领域使用的国际标准（A级）。显著性图显示，深度学习模型主要提取重搏波切迹区域附近的信息。
我们的深度学习模型能够高精度地无创估计动脉压。该模型在手术室环境中可能显示出作为决策支持工具的潜力，特别是在无法进行有创血压监测的情况下。
