Analysis of respiratory properties using the low-frequency piezoelectric sensor in patients undergoing intravenous sedation: A prospective observational study

被引:0
|
作者
Ando, Shinnosuke [1 ]
Takagi, Saori [1 ]
Harada, Tatsuya [1 ]
Oono, Yuka [1 ]
Maeda, Yuka [2 ]
Kawakami, Ryudai [3 ]
Hironaka, Tetsuo [3 ]
Ishiguro, Takashi [4 ]
Kohase, Hikaru [1 ]
机构
[1] Meikai Univ, Sch Dent, Dept Diagnost & Therapeut Sci, Div Dent Radiol, Urayasu, Saitama, Japan
[2] Univ Tsukuba, Fac Engn Informat & Syst, Tsukuba, Ibaraki, Japan
[3] Hiroshima City Univ, Grad Sch Informat Sci, Dept Comp & Network Engn, Hiroshima 7313194, Japan
[4] DreaME Lab & Co, Maebashi, Gunma, Japan
基金
日本学术振兴会;
关键词
Airway obstruction; Monitoring physiologic; Postoperative complications; Piezoelectric sensor; Breath monitoring; DIFFICULT TRACHEAL INTUBATION; UPPER-AIRWAY; SLEEP; OBSTRUCTION; COLLAPSIBILITY; RISK;
D O I
10.1016/j.tacc.2023.101309
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Background: Patients who undergo general anesthesia may experience respiratory failure postoperatively because of respiratory depression or upper airway obstruction.Aim: We investigated whether a piezoelectric sensor (AYA-P sensor) can be used to detect upper airway obstruction in patients undergoing intravenous sedation.Methods: This prospective observational study included 26 patients who underwent dental treatment under sedation at a dental clinic. Participants were evaluated using a newly defined airway obstruction score (AOS). The AYA-P sensors were attached to the suprasternal notch and the midpoint between the umbilicus and xiphoid process. The patient's respiratory status was classified into four categories based on thoracic movements, capnography, and breathing sounds from recorded data (S1: no airflow, S2: hypoventilation, S3: normal breathing, and S4: artifact). The data obtained from the AYA-P sensors were processed using a 0.4-Hz low-pass filter, calculated using the moving standard deviation (MSD) method, and further processed using a 0.1-Hz low-pass filter (fMSD). Next, these data were differentiated (D-fMSD). We defined the positive predictive value (PPV) as fMSD or D-fMSD values higher than a threshold alpha in categories S1 and S2. PPV(alpha MAX) was defined as the maximum PPV, and the negative predictive value (NPV[alpha MAX]) was defined using the same alpha MAX.Results: We compared each of the apnea-hypopnea time ratio (AHT%) and AOS with PPV(alpha MAX) and NPV(alpha MAX). The AHT% and AOS were correlated with PPV(alpha MAX) and NPV(alpha MAX) in the neck and abdomen, respectively. These findings suggest that an AYA-P sensor attached to the neck and abdomen can detect a patient's respiratory status.Conclusion: Processed data obtained from AYA-P sensors can predict upper airway obstruction in patients, and the predicted data may be dependent on individual anatomical differences.Clinical trial registration: This study was registrated in the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) clinical trial: Unique ID: UMIN000047007.
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页数:9
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