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KMID : 0363320220430010053
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Journal of Korean Oriental Internal Medicine
2022 Volume.43 No. 1 p.53 ~ p.67
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Heart Rate Variability Analysis According to Clinical Characteristics in Patients with Burning Mouth Syndrome
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Lee Hyun-Jin
Ha Na-Yeon
Ko Seok-Jae
Park Jae-Woo
Kim Jin-Sung
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Abstract
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Objectives: This study aimed to identify the characteristics of the heart rate variability (HRV) index in patients with burning mouth syndrome (BMS) and to evaluate the predictive value of HRV for Korean medical treatment responsiveness to tongue pain in BMS.
Methods: Among BMS patients who visited the Oral Diseases Clinic of Kyung Hee University Korean Medicine Hospital from January 1, 2018, to May 31, 2021, we reviewed the clinical records of 217 women aged 50 years and over. HRV and unstimulated saliva flow rate (USFR) measurements were carried out, and all patients were asked to indicate their degree of tongue pain using the visual analog scale (VAS). The subjects were classified into subgroups according to the presence or absence of hyposalivation and VAS improvement, and each subgroup was compared using the HRV index. Logistic regression analysis was conducted to confirm whether cause?effect relationships were present between statistically significant values.
Results: BMS patients had lower LF and HF than healthy people. Subgroup analysis demonstrated that there were some statistically significant differences in the HRV index according to salivation rate and degree of pain. In addition, as the LF/HF ratio increased, the pain improvement rate decreased after treatment.
Conclusion: The autonomic nerve activity of BMS patients was lower than that of healthy people; however, autonomic balance was not impaired. In addition, tongue pain in BMS patients responded more favorably to Korean medical treatment when patients fell within a normal range of the LF/HF ratio, suggesting that autonomic imbalance could be used as one of the predictable factors in clinical practice.
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KEYWORD
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burning mouth syndrome, heart rate variability, predictive factors, regression analysis
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