Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain.
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
Sci Rep. 2020 Aug 17;10(1):13911. doi: 10.1038/s41598-020-70814-4.
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.
患者-呼吸机失同步可以通过临床医生密切监测呼吸机屏幕或通过自动化算法来检测。然而，检测由呼吸频率变化和/或失同步簇组成的复杂患者-呼吸机相互作用（CP-VI）是一个挑战。从 27 名危重症患者获得的气道流量（SE-Flow）和气道压力（SE-Paw）波形的样本熵（SE）用于开发和验证用于检测 CP-VI 的自动化算法。该算法的性能与金标准（CP-VI 的呼吸机波形记录由三位专家进行视觉评分；Fleiss' kappa=0.90（0.87-0.93））进行比较。使用马修斯相关系数（MCC）作为有效性的衡量标准，使用重复留一交叉验证程序来优化不同 SE 设置（嵌入维度 m 和容差 r）、衍生 SE 特征（平均值和最大值）以及从患者自身基线 SE 值变化的阈值（Th）的组合。使用最大 SE-Flow（m=2，r=0.2，Th=25%）和最大 SE-Paw（m=4，r=0.2，Th=30%）的最大值获得最准确的结果，报告 MCC 分别为 0.85（0.78-0.86）和 0.78（0.78-0.85），准确性分别为 0.93（0.89-0.93）和 0.89（0.89-0.93）。这种方法有望提高 CP-VI 的准确检测，并进一步研究其临床意义。
