Autonomic neuroscience : basic & clinical · 2026
Deceleration capacity as a predictor of vasovagal syncope subtypes.
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Chen Aiyue, Tu Bin, Lai Zihao, Maimaitijiang Pakezhati, Wang Xinyi, Zheng Lihui, Yao Yan
Аннотация
BACKGROUND: Autonomic function may play a crucial role in the pathogenesis of vasovagal syncope (VVS). However, the characteristics of autonomic function across different VVS subtypes remain unclear. Deceleration capacity (DC), a novel vagal indicator, may offer improved discriminatory value.
OBJECTIVE: To evaluate the ability of DC to distinguish between different subtypes of VVS.
METHODS: Patients with VVS confirmed by a positive head-up tilt test were included. All heart rate variability (HRV) and DC data were obtained from 24-h Holter monitoring and analyzed among different subtypes using ANOVA, logistic regression, and ROC analyses. A DC-based subtype prediction model was further developed.
RESULTS: Among 141 patients (40 ± 18 years; 54 males), 36 (25.5%) had cardioinhibitory VVS, 20 (14.2%) vasodepressor, and 85 (60.3%) mixed type. Significant differences across subtypes were observed in minimum HR, maximum HR, SDNN, VLF, LF/HF and DC (all p < 0.05). Among these parameters, DC showed the strongest discriminatory ability in differentiating patients with cardioinhibitory responses (cardioinhibitory and mixed types) from those without (vasodepressor type), outperforming traditional HRV indices (p < 0.05). A predictive model based on daytime DC achieved an AUC of 0.811 (95% CI: 0.731-0.89). Each 1-ms increase in daytime DC was associated with a 33.3% higher odds of a cardioinhibitory response.
CONCLUSION: Baseline autonomic function varies among VVS subtypes and vagal indicators like DC might help predict patient subtypes.
OBJECTIVE: To evaluate the ability of DC to distinguish between different subtypes of VVS.
METHODS: Patients with VVS confirmed by a positive head-up tilt test were included. All heart rate variability (HRV) and DC data were obtained from 24-h Holter monitoring and analyzed among different subtypes using ANOVA, logistic regression, and ROC analyses. A DC-based subtype prediction model was further developed.
RESULTS: Among 141 patients (40 ± 18 years; 54 males), 36 (25.5%) had cardioinhibitory VVS, 20 (14.2%) vasodepressor, and 85 (60.3%) mixed type. Significant differences across subtypes were observed in minimum HR, maximum HR, SDNN, VLF, LF/HF and DC (all p < 0.05). Among these parameters, DC showed the strongest discriminatory ability in differentiating patients with cardioinhibitory responses (cardioinhibitory and mixed types) from those without (vasodepressor type), outperforming traditional HRV indices (p < 0.05). A predictive model based on daytime DC achieved an AUC of 0.811 (95% CI: 0.731-0.89). Each 1-ms increase in daytime DC was associated with a 33.3% higher odds of a cardioinhibitory response.
CONCLUSION: Baseline autonomic function varies among VVS subtypes and vagal indicators like DC might help predict patient subtypes.