Lower ultra-short-term heart rate variability can predict worse mucosal healing in ulcerative colitis

BACKGROUND: Psychological stress has been proved to be a risk factor for exacerbation for ulcerative colitis (UC). However, traditional approaches of quantifying psychological stress using psychological scales are time-consuming and the results may not be comparable among patients with different educational levels and cultural backgrounds. Alternatively, heart rate variability (HRV) is an indicator for psychological stress and not biased by educational and cultural backgrounds. AIMS: In this study, we try to explore the relationship between psychological stress and UC by analyzing the effect of ultra-short-term HRV on mucosal and histological remission status of UC. METHODS: This is a retrospective case–control study on UC inpatients from 2018 through 2020. Ultra-short-term HRV were calculated using baseline electrocardiography. Patients were divided intocase and control groups according to their Mayo endoscopic scores or histological Geboes scores. Three variables of ultra-short-term HRV (the standard deviation of normal to normal R-R intervals (SDNN), the standard deviation of successive differences between adjacent normal to normal R-R intervals (SDSD), the root mean square of successive differences of normal to normal R-R intervals (RMSSD)) were compared between different groups. And for those variables with significant differences, we built univariate and multivariate logistic regressions to depict the relationship between HRV variables and remission status of UC. RESULTS: All three HRV variables showed significant differences between the mucosal groups. However, none of them showed significant difference between the histological groups. In further logistic regression analyses, smaller RMSSD can predict severe mucosal healing status (OR = 5.21). CONCLUSIONS: Lower ultra-short-term HRV (i.e. smaller RMSSD) is shown to positively correlate with worse mucosal healing status. However, ultra-short-term HRV cannot predict histological healing status according to our data.