Heart Rate Variability Predicts Therapeutic Response to Metoprolol in Children With Postural Tachycardia Syndrome
Year: 2019
Type: article
Abstract: Purpose: To improve the metoprolol therapeutic effectiveness, we aimed to explore whether baseline heart rate variability (HRV) indicators before metoprolol treatment were useful for predicting its efficacy for postural tachycardia syndrome (POTS). Methods: We recruited 45 children with POTS who received metoprolol and 17 healthy controls. All children underwent a standing test or basic head-up tilt test and 24-hr dynamic electrocardiography before treatment. After 3 months of metoprolol, therapeutic responsiveness was evaluated. The usefulness of baseline HRV parameters in predicting the effectiveness of metoprolol was studied and the long-term cumulative symptom rate was analyzed. Results: The baseline HRV frequency domain indicators for power, ultra-low frequency, very-low frequency, low frequency (LF), high frequency (HF), high frequency normalized units and total power (TP) as well as time domain indicators were significantly lower for responders than non-responders to metoprolol; however, low-frequency normalized units and LF/HF ratio were markedly greater for responders than non-responders. On series-parallel analysis, combined baseline triangular (TR) index ≤ 33.7 and standard deviation of all normal-to-normal intervals (SDNN) index ≤ 79.0 ms as cut-off values yielded sensitivity, specificity and accuracy of 85.3%, 81.8% and 84.4%, respectively, to predict therapeutic responsiveness to metoprolol. On long-term follow-up, the cumulative symptom rate was significantly higher with TR index > 33.7 and SDNN index ≤ 79.0 ms, TR index ≤ 33.7 and SDNN index > 79.0 ms or TR index > 33.7 and SDNN index > 79.0 ms than TR index ≤ 33.7 and SDNN index ≤ 79.0 ms (P<0.05). Conclusion: Combined TR index ≤ 33.7 and SDNN index ≤ 79.0 ms were useful preliminary measures to predict therapeutic response to metoprolol in pediatric POTS.
Source: Frontiers in Neuroscience
Authors: Yuanyuan Wang, Chunyu Zhang, Selena Chen, Ping Liu, Yuli Wang, Chaoshu Tang, Hongfang Jin, Junbao Du
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,950
Awards: G2214751693, G3266794886
OpenAlex ID: W2987159528
