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Atrial fibrillation in heart failure patients with and without sleep-disordered breathing
Background: More than 50% of HF patients are suffering from sleep-disordered breathing (SDB). Patients with SDB are at high risk of cardiac arrhythmias, especially atrial fibrillation (AF). The aim of this study is to assess the prevalence of AF and SDB in a cohort of patient diagnosed with HF....
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| Format: | Doctoral Thesis |
| Language: | English |
| Published: |
Philipps-Universität Marburg
2024
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| Online Access: | PDF Full Text |
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| Summary: | Background:
More than 50% of HF patients are suffering from sleep-disordered breathing (SDB).
Patients with SDB are at high risk of cardiac arrhythmias, especially atrial fibrillation
(AF). The aim of this study is to assess the prevalence of AF and SDB in a cohort of
patient diagnosed with HF. Furthermore, it aims to investigate the potential association
between AF and SDB.
Methods:
We screened a total of 289 out of 302 patients with heart failure (HF) and a left
ventricular ejection fraction (LVEF) of 50% or less for sleep-disordered breathing
(SDB) using cardiorespiratory polysomnography (PSG), excluding those with known
SDB. Two distinct apnea-hypopnea index (AHI) cutoff values (5 and 15 events per hour
of sleep) were utilized. Our investigation included various clinical parameters such as
BMI, sex, age, and blood pressure. Additionally, we examined the New York Heart
Association (NYHA) classification of HF and the occurrence of atrial fibrillation (AF)
within our cohort. Polysomnography was employed to assess sleep parameters,
including total sleep time (TIB), AHI, mean oxygen saturation (SaO2 %), SaO2 < 90%,
arousal index per hour, sleep efficiency, and sleep latency. We then compared patients
with SDB (obstructive sleep apnea, central sleep apnea) and those without SDB using
both AHI cutoffs (≥15 and 5 events/h) for parameters such as age, BMI, Epworth
Sleepiness Scale (ESS), ejection fraction (EF), and AF presence. Further analyses were
conducted to explore differences in the mentioned parameters among patients with or
without AF in our cohort.
The results were presented as mean ± standard deviation. Basic data underwent a
preliminary Kolmogorov-Smirnov test to assess normal distribution, followed by
ANOVA for normally distributed data and ANOVA on ranks (Kruskal-Wallis) in
instances where normal distribution was not established. The Spearman's rank
correlation test was employed to characterize the correlation between variables listed in
table 9.
Results:
Among the 289 patients, 219 (75.78%) were male, and 70 (24.22%) were female. The
average LVEF was 33.8%. The most of patients were evenly distributed between
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NYHA classes II and III. AF was found in 65 of 289 patients (22.49%). Using an AHI
cutoff of 5 and 15 per hour of sleep, polysomnography revealed the presence of SDB in
72% and 47% of cases, respectively. OSA with an AHI > 5/h was present in 75 patients
(26%) and 43 patients (15%) had moderate oder severe OSA with an AHI >15/h.
Otherwise CSA with an AHI > 5/h was present in 134 patients (46%) and 93 patients
(32%) had moderate oder severe CSA with an AHI >15/h. The levels of sleep
parameters like TIB and AHI were elevated in SDB patients compared to non-SDB
patients. In contrast, non-SDB patients had higher mean SaO2 levels and arousal index.
Advanced age (>60), elevated BMI (28 kg/m2), and male gender were identified as
SDB risk factors compared to non-SDB patients. The occurrence of SDB was
similar regardless of the presence or absence of AF, suggesting a lack of correlation
between AF and OSA or CSA. However, an increased prevalence of AF was observed
in CSA patients.
Conclusion:
Our study determined that age, BMI, and male gender increase the risk of SDB in HF
patients, emphasizing the need for personalized management. The study
also revealed the importance of standardized SDB definitions, device usage, and
personalized management approaches. While no direct correlation was found between
AF and OSA/CSA, our findings provide valuable insights into the complex
relationships among SDB, HF, and AF, highlighting potential clinical benefits in
addressing SDB during HF management. |
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| DOI: | 10.17192/z2024.0293 |