Simultaneous laryngopharyngeal pH monitoring (Restech) and conventional esophageal pH monitoring—correlation using a large patient cohort of more than 100 patients with suspected gastroesophageal reflux disease

2018 ◽  
Vol 31 (10) ◽  
Author(s):  
H F Fuchs ◽  
D T Müller ◽  
F Berlth ◽  
M K Maus ◽  
C Fuchs ◽  
...  
Keyword(s):  
Gastroesophageal Reflux Disease ◽  
Gastroesophageal Reflux ◽  
Reflux Disease ◽  
Ph Monitoring ◽  
Esophageal Ph Monitoring ◽  
Patient Cohort ◽  
Large Patient Cohort ◽  
Large Patient ◽  
Esophageal Ph

scholarly journals Research on Gastroesophageal Reflux Disease Based on Dynamic Features of Ambulatory 24-Hour Esophageal pH Monitoring

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Shuang Liu ◽  
Minpeng Xu ◽  
Jiajia Yang ◽  
Hongzhi Qi ◽  
Feng He ◽  
...  
Keyword(s):  
Gastroesophageal Reflux Disease ◽  
Gastroesophageal Reflux ◽  
Information Entropy ◽  
Reflux Disease ◽  
Ph Monitoring ◽  
Esophageal Ph Monitoring ◽  
Dynamic Features ◽  
Recording Time ◽  
Wavelet Energy ◽  
Esophageal Ph

Ambulatory 24-hour esophageal pH monitoring has been considered as the gold standard for diagnosing gastroesophageal reflux disease (GERD), and in clinical application, static parameters are widely used, such as DeMeester score. However, a shortcoming of these static variables is their relatively high false negative rate and long recording time required. They may be falsely labeled as nonrefluxers and not appropriately treated. Therefore, it is necessary to seek more accurate and objective parameters to detect and quantify GERD. This paper first describes a new effort that investigated the feasibility of dynamic features of 24-hour pH recording. Wavelet energy, information entropy, and wavelet entropy were estimated for three groups (severe, mild-to-moderate, and normal). The results suggest that wavelet energy and entropy are physiologically meaningful since they differentiated patients with varying degrees of GERD.K-means clustering algorithm was employed to obtain the sensitivity and specificity of new parameters. It is obvious that information entropy goes with the highest sensitivity of 87.3% and wavelet energy has the highest specificity of 97.1%. This would allow a more accurate definition of the best indicators to detect and quantify GERD as well as provide an alternative insight into the early diagnosis of GERD.

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