Heart rate transition patterns reveal autonomic dysfunction in heart failure with renal function decline: a symbolic and Markov model approach
Abstract Around half of heart failure (HF) patients develop chronic kidney disease (CKD) and early detection of renal impairment in HF remains a clinical challenge. Both HF and CKD are characterized by autonomic dysfunction, suggesting that early identification of autonomic dysregulation may assist...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
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| Series: | BioData Mining |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13040-025-00460-x |
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| Summary: | Abstract Around half of heart failure (HF) patients develop chronic kidney disease (CKD) and early detection of renal impairment in HF remains a clinical challenge. Both HF and CKD are characterized by autonomic dysfunction, suggesting that early identification of autonomic dysregulation may assist in early diagnosis and intervention. Conventional heart rate variability (HRV) metrics serve as non-invasive markers of autonomic nervous system (ANS) function; however, they are limited in their ability to capture directional and nonlinear dynamics associated with autonomic impairment during renal function decline. In this study, we digitized heart rate (HR) changes from 5-minute electrocardiogram (ECG) recordings in 358 patients with chronic HF (CHF). We applied a first-order Markov model and motif pattern analyses to compare HR transition dynamics between patients with normal and reduced estimated glomerular filtration rate (eGFR). The results revealed decreased monotonic HR transitions and increased tonic fluctuations in patients with reduced eGFR. Building on these findings, we introduced a transition stability index (TSI), which was significantly lower in patients with reduced eGFR compared to those with normal eGFR (p < 0.05). These results suggest that TSI may serve as a novel indicator of autonomic dysfunction associated with renal decline. Motif analysis further supported these findings by identifying distinctive HR transition patterns in patients with low eGFR. |
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| ISSN: | 1756-0381 |