Rodiera Claudia, Fortuny Helena, Valls Adaia, Borras Rosa, Ramírez Carlos, Ros Bibiana, Rodiera Josep, Santaliestra Jesús, Lanau Miquel, Rodríguez Nacho
Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain.
Department of Maxilofacial Instituto Maxilofacial, Centro Medico Teknon, Quironsalud Group Barcelona Spain.
Health Sci Rep. 2024 Dec 9;7(12):e70246. doi: 10.1002/hsr2.70246. eCollection 2024 Dec.
An unanticipated difficult airway is one of the greatest challenges for anesthesiologists. Proper preoperative airway assessment is crucial to reducing complications. However, current screening tests based on anthropometric features are of uncertain benefit. Therefore, our study explores using voice analysis with machine learning algorithms to predict a difficult airway.
Observational, multicenter study with N = 438 patients initially enrolled at Centro Medico Teknon and Institut Universitari Dexeus (2019-2022) for the research study. After excluding 125 patients, N = 313 were included. Ethics committee approval was obtained. Adults ASA I-III scheduled for elective procedures under general anesthesia with endotracheal intubation were selected. Patient clinical features and traditional predictive tests were collected. Vowels "A, E, I, O, U" were recorded in normal, flexion, and extension positions. Cormack grade was assessed, and data were analyzed using KNIME, resulting in multiple models based on demographics and voice data. ROC curves and other metrics were evaluated for each model.
Among multiple models evaluated, two yielded the best performance to predict a difficult airway both exclusively analyzing Cormack I and IV cases which showed the most distinct differences. The variables included in each model were the following: Model 1; included demographic data, vowel "A" in all positions and harmonics of the voice achieving an AUC of 0.91. Model 2; Included demographic data, vowel "O" in normal positions and voice parameters (Shimmer, Jitter, HNR); achieving in an AUC of 0.90. In contrast, models which focused on analyzing all Cormack grades (I, II, III, IV) cases performed less effectively.
Acoustic parameters of the voice together with the demographic data of the patients, when introduced into classification algorithms based on machine learning showed promising signs of predicting a difficult airway.
意外的困难气道是麻醉医生面临的最大挑战之一。术前进行恰当的气道评估对于减少并发症至关重要。然而，目前基于人体测量特征的筛查试验的益处尚不确定。因此，我们的研究探索使用语音分析和机器学习算法来预测困难气道。
本研究为观察性多中心研究，最初纳入了438例在Teknon医学中心和德修斯大学研究所（2019 - 2022年）参与该研究的患者。排除125例患者后，纳入313例患者。研究获得了伦理委员会的批准。选取计划在全身麻醉下行气管插管择期手术的美国麻醉医师协会（ASA）I - III级成年患者。收集患者的临床特征和传统预测试验结果。记录元音“A、E、I、O、U”在正常、屈曲和伸展位的发音。评估Cormack分级，并使用KNIME软件分析数据，生成基于人口统计学和语音数据的多个模型。对每个模型评估ROC曲线和其他指标。
在评估的多个模型中，有两个模型在预测困难气道方面表现最佳，它们专门分析了Cormack I级和IV级病例，这两级病例表现出最明显的差异。每个模型纳入的变量如下：模型1：纳入人口统计学数据、所有位置的元音“A”以及语音谐波成分，曲线下面积（AUC）为0.91。模型2：纳入人口统计学数据、正常位的元音“O”以及语音参数（闪烁度、抖动度、谐噪比），AUC为0.90。相比之下，专注于分析所有Cormack分级（I、II、III、IV级）病例的模型效果较差。
将患者的语音声学参数与人口统计学数据引入基于机器学习的分类算法时，显示出预测困难气道的良好前景。
