- Email: [email protected]
Variability of Sleep Apnea Severity and Risk of Atrial Fibrillation
JACC: CLINICAL ELECTROPHYSIOLOGY
VOL. 5, NO. 6, 2019
CROWN COPYRIGHT ª 2019 PUBLISHED BY ELSEVIER ON BEHALF OF THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION. ALL RIGHTS RESERVED.
Variability of Sleep Apnea Severity and Risk of Atrial Fibrillation The VARIOSA-AF Study Dominik Linz, MD, PHD,a Anthony G. Brooks, PHD,b Adrian D. Elliott, PHD,a Chrishan J. Nalliah, MBBS,c Jeroen M.L. Hendriks, PHD,a Melissa E. Middeldorp, PHD,a Celine Gallagher, RN,a Rajiv Mahajan, MD, PHD,a Jonathan M. Kalman, MBBS, PHD,c R. Doug McEvoy, MD,d Dennis H. Lau, MBBS, PHD,a Prashanthan Sanders, MBBS, PHDa
ABSTRACT OBJECTIVES This study sought to determine night-to-night variability in the severity of sleep-disordered breathing (SDB) and the dynamic intraindividual relationship to daily risk of incident atrial fibrillation (AF) by using simultaneous long-term day-by-day SDB and AF monitoring. BACKGROUND Night-to-night variability in SDB severity may result in a dynamic exposure to SDB related conditions impacting the timing and extent of cardiovascular responses. METHODS This study was an observational cohort study. Daily data for AF burden and average respiratory disturbance index (RDI) were extracted from pacemakers capable of monitoring nightly SDB and daily AF burden in 72 patients. Nightly RDI values were grouped into quartiles of severity within each patient. AF burdens of >5 min, >1 h, and >12 h were the outcome variables. RESULTS A total of 32% of patients had a mean RDI of $20/h, indicative of overall severe SDB. There was significant night-to-night variation in RDI reflected by an absolute SD of 6.3 events/h (range 2 to 14 events/h) within any given patient. Within each patient, the nights with the highest RDI (in their highest quartile) conferred a 1.7-fold (1.2 to 2.2; p < 0.001), 2.3-fold (1.6 to 3.5; p < 0.001), and 10.2-fold (3.5 to 29.9; p < 0.001) increase risk of having at least 5 min, 1 h, and 12 h, respectively, of AF during the same day compared with the best sleep nights (in their lowest quartiles). CONCLUSIONS There is considerable night-to-night variability in SDB severity which cannot be detected by 1 single overnight sleep study. SDB burden may be a better metric with which to assess the extent of dynamic SDB related cardiovascular responses such as daily AF risk than the categorical diagnosis of SDB. (Night-to-Night Variability in Severity of Sleep Apnea and Daily Dynamic Atrial Fibrillation Risk [VARIOSA-AF]; ACTRN 12618000757213) (J Am Coll Cardiol EP 2019;5:692–701) Crown Copyright © 2019 Published by Elsevier on behalf of the American College of Cardiology Foundation. All rights reserved.
T
he number of apnea and hypopnea episodes
However, recent studies suggest that SDB severity in
per
[AHI])
an individual patient is not stable over time but
determined during a single overnight sleep
exhibits considerable night-to-night variability which
hour
(apnea-hypopnea-index
study is clinically used to diagnose and assess the
cannot be detected by 1 overnight sleep study (2,3).
severity of sleep-disordered breathing (SDB) (1).
This
night-to-night
variability
in
SDB
severity
From the aCentre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, South Australia, Australia; bCardiac Rhythm Management, MicroPort, Scoresby, Victoria, Australia; cDepartment of Cardiology, Royal Melbourne Hospital and Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia; and the dAdelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, and Sleep Health Service, Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia. Dr. Linz is supported by a Beacon research fellowship, University of Adelaide; is an advisory board member for LivaNova/MicroPort and Medtronic; receives lecture and consulting fees from LivaNova/MicroPort, Medtronic,
ISSN 2405-500X/$36.00
https://doi.org/10.1016/j.jacep.2019.03.005
Linz et al.
JACC: CLINICAL ELECTROPHYSIOLOGY VOL. 5, NO. 6, 2019 JUNE 2019:692–701
may result in a variable and dynamic exposure to SDB
lacking. Using simultaneous long-term night-
ABBREVIATIONS
related conditions which may impact the timing and
by-night SDB and AF monitoring, this study
AND ACRONYMS
extent of cardiovascular responses (4).
characterized night-to-night variability in
In patients with atrial fibrillation (AF), the preva-
SDB severity and examined the relationship
lence of categorically moderate-to-severe SDB ranges
between SDB and AF, with each patient
between 21% and 72% due to variable selected co-
acting as their own control. Previously, pre-
horts, applied scoring rules, and thresholds used
liminary findings highlighted the nightly
(5–8). Observational studies suggest that SDB re-
variation in SDB (18); the present study de-
duces the efficacy of catheter-based and pharmaco-
scribes the complete and expanded analysis
logical antiarrhythmic therapy and that treatment
of the VARIOSA-AF (VARIability in severity
with continuous positive airway pressure (CPAP)
Of Sleep Apnea and daily dynamic Atrial
lowers the rate of AF recurrence after electrical car-
Fibrillation) risk study.
dioversion and improves catheter ablation success rates in AF patients (9,10). Despite evolving evidence supporting an important role for the categorical SDB diagnosis in the management of AF patients, the pathophysiological
importance
of
night-to-night
variability in SDB severity for AF risk is unclear. SEE PAGE 702
AHI = apnea-hypopnea index AF = atrial fibrillation CPAP = continuous positive airway pressure
RDI = respiratory disturbance index
SAM = sleep apnea monitoring SDB = sleep-disordered breathing
METHODS STUDY DESIGN. This study was an observational
cohort study. The study was approved by the Clinical Research Ethics Committee of the Royal Adelaide Hospital, Adelaide, Australia, and was registered in Australian New Zealand Clinical Trials Registry
Pre-clinical and clinical observations suggest that
(ACTRN identifier 12618000757213).
SDB together with chronic concomitant conditions
PATIENT POPULATION. The cohort was extracted
such as hypertension and obesity may promote an
from a de-identified home monitoring database of
arrhythmogenic progressive structural remodeling
patients with dual-chamber pacemakers implanted
progress in SDB (11–14). In addition to the chronic
according to guideline-directed indications (19). A
structural alterations in the atria, transient apnea-
data dump was performed in a sample of 191 patients
associated arrhythmogenic changes may further
with
enhance
dynamic
(MicroPort, Clamart, France) with implemented SDB
AF
693
Sleep Apnea and AF
susceptibility,
creating
a
Reply
200
or
Kora
100
DR
pacemakers
arrhythmogenic substrate in the atrium (11,15–17).
monitoring measuring a respiratory disturbance in-
Theoretically, rather than the categorical diagnosis
dex (RDI) (20,21). Daily SDB and AF burdens of all
of SDB obtained from a single diagnostic overnight
downloaded patient data were extracted by the
sleep study, AF risk may be mediated through dy-
MicroPort research department and provided in a de-
namic night-to-night changes in SDB severity.
identified file. Demographic or clinical descriptive
Although chronic structural alterations induced by
data could not be retrieved beyond that recorded on
SDB have already been described in AF patients with
the pacemaker, given the de-identified nature of the
SDB (13,14), clinical evidence for a dynamic AF sub-
dataset. Figure 1 provides a CONSORT Figure of the
strate in patients related to nightly SDB severity is
study population.
and ResMed; and has received funding from Sanofi, ResMed, and Medtronic. Drs Elliott and Hendriks are supported by Early Career Fellowships from the National Heart Foundation of Australia. Dr. Hendriks holds a Derek Frewin Lectureship, University of Adelaide; and is a consultant for Medtronic and Pfizer/BMS. Ms. Middeldorp is supported by a postgraduate scholarship from the National Health and Medical Research Council (NHMRC) of Australia. Ms. Gallagher is supported by a Leo J. Mahar scholarship, University of Adelaide. Dr. Mahajan is supported by an Early Career Fellowship, NHMRC, and a Leo J. Mahar lectureship, University of Adelaide; receives lecture and consulting fees from Medtronic, Abbott, Bayer, and Pfizer; and has received funding through his institution from Abbott, Medtronic, and Bayer. Drs. McEvoy, Kalman, and Sanders are supported by Practitioner Fellowships, NHMRC of Australia. Dr. McEvoy has received funding from Philips Respironics, ResMed, and Fisher and Paykel. Dr. Sanders is an advisory board member of Biosense-Webster, Medtronic, St. Jude Medical, Boston Scientific, CathRx; and has received funding through his institution from Biosense-Webster, Medtronic, St. Jude Medical, Boston Scientific and LivaNova/ MicroPort. Dr. Lau is supported by a Robert J. Craig Lectureship, University of Adelaide; and has received lecture and consulting fees from Abbott, Biotronik, Boehringer Ingelheim, Bayer, and Pfizer. Dr. Brooks is an employee of LivaNova/MicroPort. Dr. Lim is supported by a Neil Hamilton Fairley Early Career Fellowship, NHMRC of Australia. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. William Stevenson, MD, served as Guest Editor for this paper. All authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Clinical Electrophysiology author instructions page. Manuscript received December 17, 2018; revised manuscript received February 25, 2019, accepted March 13, 2019.
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and are processed to determine the amplitude and
F I G U R E 1 CONSORT Figure of the Study Population
period of respiratory cycles (i.e., minute ventilation). The SAM algorithm defines ventilation pause as an absence of respiratory cycle for >10 s and ventilation reduction as a decrease of respiratory amplitude by at least 50% for >10 s. An RDI for the respective day is then calculated as the total number of ventilation pauses and reductions per hour during a 5-h programmable monitoring period between 12:00
AM
and
5:00 AM . In the DREAM study, an RDI $20/h identified patients with severe SDB (AHI $30/h) by polysomnography performed on the same night with 89% sensitivity and 85% specificity (NCT01537718) (20,21). The RDI is determined on a nightly basis. Night-to-night variability in RDI was determined using a nightly coefficient of variation and/or SD within the patient monitoring period and then averaged across the sample. To characterize the SDB pattern within each patient, SDB category changes between severe (mean RDI: $20/h) or nonsevere SDB (mean RDI: <20/h) based on the average RDI during consecutive, nonoverlapping, 1-week blocks were determined. A paroxysmal pattern was present if consecutive weeks of 1 SDB category were separated by at least 1 week with a different SDB category. In contrast, a persistent SDB patterns was present if all consecutive
weeks
displayed
a
mean
RDI
of
either $20/h or <20/h. To standardize across patients, each patient’s daily RDIs were grouped into quartiles, with the highest quartile representing the 25% of nights with the highest RDI within each patient. ATRIAL Exclusion criteria included <1 month of follow-up since implantation (n ¼ 44), SDB monitoring was turned off (n ¼ 27), >10% invalid RDI measurements (n ¼ 10), unreliable mode switch estimates due to atrial under-sensing (n ¼ 14), persistent AF throughout the recording (n ¼ 6), and unreliable AV block statistics in patients programmed to Safe-R (DDD
FIBRILLATION/ATRIAL
TACHYCARDIA
EVENTS. The outcome variables were classified ac-
cording to pacemaker-defined “mode switch events” (MicroPort) (22). In a MicroPort dual-chamber device, a mode switch requires a sustained pathological ac-
switching with >50% of V sense in a committed period [n ¼ 10]), or
celeration of the atrial channel (>25% acceleration
incomplete data for interrupted interrogations (n ¼ 2). Seventy-two included
compared to the mean of 8 preceding “normal” atrial
patients had <25% mode switch burden (lower overall AF burden). Six additional patients with >25% mode switch burden but at least 1 reference period of sinus rhythm (higher overall AF burden) were included for the
cycles) >120 beats/min. At least 28 of 32 atrial accelerated beats (87.5%) (primary criterion) or secondarily
investigation of the directionality of the SDB and AF relationship only. AF ¼
at least 36 of 64 accelerated atrial beats (56.3%) are
atrial fibrillation; AV ¼ atrioventricular; CONSORT ¼ Consolidated Statement
required for mode switch declaration. The duration of
of Reporting Trials; DDD ¼ dual chamber paced, sensed and response to
mode switch for each day was determined to range
sensing; DR ¼ rate adaptive; Kora 100 or Reply 200 ¼ Kora 100 or Reply 200
from 1 to 86,400 s (24 h) after the primary or secondary
DR pacemakers (MicroPort); RDI ¼ respiratory disturbance index; Safe-R ¼ Safe-R mode; SDB ¼ sleep-disordered breathing.
criterion were met. To assess the relationship between SDB severity and AF burden, analyses of 3 different total cumulative mode switch episodes were recorded:
NIGHT-TO-NIGHT SDB MONITORING. Regardless of
>5 min per day; >1 h per day; and >12 h per day were
the diagnosis of SDB, daily SDB severity was assessed
included. Thus, 5 1-min mode switch episodes were
by the pacemaker-based sleep apnea monitoring
effectively treated the same as a 1 5-min episode of
(SAM) (MicroPort) algorithm that was described and
AF. Electrograms of up to 11 mode switch events were
validated
polysomnography
available, and all mode switch recordings were
(20,21). Briefly, transthoracic impedance measure-
manually adjudicated to ensure genuine AF, atrial
ments are measured at 6 Hz (through micropulses)
flutter, or atrial tachycardia.
previously
against
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F I G U R E 2 Representative SDB Recordings
Patient 1 shows overall mild SDB with a paroxysmal SDB pattern, high night-to-night variability in SDB severity, and lower overall SDB burden (below the RDI of 20/h during most nights). Patient 2 shows overall moderate SDB with a paroxysmal SDB pattern, high night-to-night variability in SDB severity and higher overall SDB burden. Patient 3 shows overall severe SDB with a persistent SDB pattern (above RDI 20/h most nights), high night-to-night variability in SDB severity and higher overall SDB burden. Q1 to Q4 ¼ quartiles 1 to 4; other abbreviations as in Figure 1.
DIRECTION
OF
RELATIONSHIP. To
examine the
performed our analyses in patient subsets that had at
directionality of relationship “SDB begets AF” or “AF
least 1 day of cumulative daily AF >5 min (n ¼ 32), >1 h
begets SDB,” the prediction analysis for RDI data was
(n ¼ 29), and >12 h (n ¼ 11). Patients who did not have a
run (e.g., from Friday night to Saturday morning) and
suitable “AF event” were excluded because their
the AF burden (e.g., from Saturday) of the same day
within-patient OR were null. Mean changes in RDI in
and reran the analysis with a 1-day offset between the
the subgroup with higher AF burden (n ¼ 6) were
AF episode and the RDI, such that the AF burden
assessed using mixed effects models with device serial
during 1 day (e.g., during Saturday) then corre-
number as the random factor. All data were analyzed
sponded to the SDB severity determined by RDI of the
using SPSS version 24 software (IBM, Armonk, New
following morning (e.g., from Saturday night to Sun-
York) and significance was set at a p value of <0.05.
day morning). We also examined the 6-patient subset with higher overall AF burden was also examined
RESULTS
(>25% AF burden) and means tested the nocturnal RDI on days with 100% AF burden compared to days
SAMPLE CHARACTERISTICS. The sample consisted
without AF within each patient.
of 72 de-identified SAM-monitored dual-chamber
STATISTICAL ANALYSIS. Data are mean SD when
normally distributed and median (interquartile range [IQR]) if data followed a skewed distribution. Categorical data are proportion with numerator and denominator in brackets. A binary logistic generalized estimating equation was used to develop a prediction model for the dichotomous AF outcome (>5 min odds ratio [OR]: >1 h; OR >12 h of cumulative mode switch per day) using the within-patient standardized (quartiles) SDB severity data. A subject variable of
pacemaker patients with lower overall AF burden (<25% AF burden). The median percent atrial pace was 55% (range 20% to 85%), and the percent of ventricular pace was 2.4% (range 0% to 41%). Ninety percent of the sample was programed to Safe-R (65 of 72) a minimized ventricular pacing algorithm; 58% of patients (42 of 72) had rate-response activated. The mean follow-up per patient was 21 8 weeks, resulting in a total daily monitoring time for the sample of >28 years (10,383 days).
device serial number was introduced as the clustering
SLEEP-DISORDERED
variable to account for the nesting of outcome events
BURDEN AND NIGHT-TO-NIGHT VARIABILITY. The
BREATHING
SEVERITY:
within the individual. The SDB severity reference
SAM algorithm provided a valid RDI estimate in 10,315
category was always the lowest RDI quartile. We
of 10,383 days (99%). The average RDI across the
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Sleep Apnea and AF
JUNE 2019:692–701
F I G U R E 3 Proportion of Days With AF
Percentage of days with at least >5 min, >1 h, and >12 h of AF for each within-patient quartile (Q1 to 4) of SDB severity, assessed by nightly RDI. Pie chart black sections indicate days with at least >5 min, >1 h, and >12 h of AF; yellow sections indicate days without at least >5 min, >1 h, and >12 h of AF. Abbreviations as in Figure 1.
Linz et al.
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Sleep Apnea and AF
sample was 17.9 11.5 events/h. In 32% of patients (23
episode and the RDI. It was found that all relation-
of 72) the mean RDI was $20/h, indicative of overall
ships dissolved (Figure 4). Six patients with a higher
severe SDB.
AF burden (AF burden: >25%) were analyzed sepa-
Figure 2 shows the night-to-night RDI variability in
rately to examine the potential relationship between
3 representative patients with different levels of un-
longer AF episodes and increased RDI. In this sub-
derlying SDB severity. Fifty-six percent of patients
group, the number of days with 24 h of AF, 0% AF,
(40 of 72) had at least 1 week with a mean RDI $20/h,
and paroxysmal AF (>0%, but <24 h) was 422 (50%),
of whom 73% (29 of 40) demonstrated paroxysmal
139 (17%), and 279 (33%) days, respectively. It was
weekly SDB patterns (i.e., weeks of RDI $20/h sepa-
found that days with 100% AF burden were associ-
rated by at least 1 week with an RDI <20/h) in contrast
ated with significantly higher RDIs (p < 0.001) than
to persistent SDB patterns in the minority of 28% (11
those days without AF.
of 40) where all consecutive weeks displayed a mean RDI $20/h. An RDI $20/h during at least 1 night was
DISCUSSION
observed in 85% of patients (61 of 72). The individual mean night-to-night RDI coefficient of variation was
Long-term SDB monitoring by chronically implanted
41 16%, which reflected an absolute SD of 6.3
devices revealed a considerable night-to-night vari-
events/h within each patient (range 2 to 14 events/h).
ability in SDB severity, which cannot be detected by a
The median RDI for each within-patient quartile (Q1
single overnight sleep study. Long-term SDB and
to 4) was 8.2 (IQR: 4.6 to 15.2), 12.6 (IQR: 7.8 to 21.6),
simultaneous AF monitoring showed for the first time
16.4 (IQR: 10.4 to 26.8), and 23.2 (IQR: 15.4 to 35.5).
that, in individual patients, the nights with the
From the lowest to the highest quartile for the entire
highest SDB severity conferred a more than 2.3-fold
sample.
increase risk of having at least 1 h of AF the same
ATRIAL
FIBRILLATION/ATRIAL
TACHYCARDIA
BURDEN. Of patients with lower overall AF burden
(<25% AF burden), 57% of patients (41 of 72) had at least a single mode switch episode recorded on their device. Cumulative AF on any single day of their follow-up of >5 min, >1 h, and >12 h was observed in 44% (32 of 72), 40% (29 of 72), and 15% of patients (11 of 72), respectively. The total mode switch burden ranged from 0% to 24.2% with the median daily episode duration of 11.6 min (range 1.3 to 84 min). The total number of days satisfying >5 min, >1 h, and >12 h AF events within the monitoring period of each patient subset was 661 of 4,543 days (14.5%), 472 of 3,994 days (11.8%), and 130 of 1,748 days (7.4%), respectively.
day compared to nights with the lowest SDB severity. By contrast, there was no increased risk of AF in the 24 h before the nights with the highest SDB severity. These 2 contrasting observations provide strong evidence for a feed-forward mechanistic link between SDB and AF, whereby the increased cardiac stress induced by even a single night of more severe SDB can establish conditions conducive to AF. These findings have important implications for the assessment of SDB severity and the guidance of SDB treatment in patients. The result of a single overnight sleep study may not be representative of SDB severity during the remaining 364 days of a year in a specific patient and could thus lead to frequent misclassification of SDB status in patients if just 1 overnight sleep study is performed (4). It may also help explain
RELATIONSHIP BETWEEN SDB SEVERITY AND AF
the highly variable prevalence of SDB (2–4) and may
BURDEN. Figure 3 summarizes the proportion of days
have contributed to the negative results of recent
with at least >5 min, >1 h, and >12 h AF for each
randomized
within-patient quartile of SDB severity. Within each
studies (23–25). The proportion of nights with high
patient, the nights (12:00
RDI (SDB burden), rather than a categorical diagnosis
AM
to 5:00
AM )
with the
controlled
SDB
treatment
outcome
highest RDI (in their highest quartile) conferred a 1.7-
of SDB per se, may better reflect the exposure to SDB-
fold (range 1.2- to 2.2-fold; p < 0.001), a 2.3-fold
related factors and may be a more useful metric to
(range 1.6- to 3.5-fold; p < 0.001), and a 10.2-fold
determine SDB severity in the management of
(range 3.5- to 29.9-fold; p < 0.001) increased risk of
concomitant cardiovascular diseases such as AF (4).
having at least 5 min, 1 h, and 12 h of AF during the
Importantly, the relationship between nights with
same day compared with the quartile of nights with
more severe SDB and increased AF risk held for all
the lowest RDI (Figure 4).
individuals, regardless of the clinical diagnosis of SDB
To test for a potential bidirectional relationship
and mean RDI. This suggests that it may be the rela-
with AF begetting SDB, the dataset of patients with
tive rather than the absolute increase in SDB severity
lower
(AF
on a single night that increases AF risk in the indi-
burden: <25%) with a 1-day offset between the AF
vidual patient. Different mechanisms such as longer
overall
AF
burden
was
reanalyzed
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Sleep Apnea and AF
F I G U R E 4 Relationship Between SDB Severity and AF
(Upper panels) Relationship between SDB severity, assessed by RDI during 1 night (12:00 AM to 5:00 AM), and the risk of having at least 5 min, 1 h, and 12 h of AF during the same day compared with the quartile with the lowest RDI for each within-patient quartile (Q1 to 4) of RDI severity (“SDB begets AF”). (Lower panels) Relationship with a 1-day offset between the AF episode and the RDI (“AF begets SDB”). Unadjusted odds ratios are shown. Abbreviations as in Figure 1.
time spent sleeping supine, more alcohol consump-
contributes to a dynamic AF substrate. Pre-clinical
tion before bed, and variable compliance or with-
studies suggest that intermittent hypoxia, auto-
drawal from CPAP treatment may contribute to
nomic dysregulation, and intrathoracic pressure
variable SDB severity. SDB burden determined by
swings
continuous long-term SDB monitoring incorporates
together with chronic concomitant conditions like
the considerable night-to-night variability in SDB
hypertension and obesity, promote an arrhythmo-
severity documented in this study and previously
genic progressive structural remodeling process in
described in patients with and without co-occurring
the atrium (4,11). This structural remodeling, charac-
cardiovascular disease (2,3,20,21).
terized by more scar formation in the atrium, could be
during
obstructive
respiratory
events,
The present study provides the first clinical evi-
documented by electroanatomical mapping studies in
dence for the pathophysiological relevance of the
AF patients with SDB (13,14). In animal models of
night-to-night variability in SDB severity which
SDB,
simulated
obstructive
respiratory
events
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Sleep Apnea and AF
C ENTR AL I LL U STRA T I O N Simultaneous Long-Term Day-by-Day Sleep Apnea and Atrial Fibrillation Monitoring
Nightly RDI (Events/h)
20
Cumulative Daily AF Burden (h)
25
24
15 10 5 0
18 12 AF ive t a l mu
6 >1 h
cu
0 10
20
24% days AF >1 h
30
40
50 60 Day Number
20% days AF >1 h
AF
70
80
90
21% days AF >1 h
AF
100
39% days AF >1 h
AF AF
Q2
Q3
Most Severe SDB (Q4)
20.0
20.0
20.0
20.0
15.0
15.0
15.0
15.0
10.0
10.0
10.0
10.0
5.0
5.0
5.0
5.0
.0
.0
.0
.0 2 5 6 7 8 12 13 14 20 23 27 29 30 31 50 56 61 66 74 80 85 87 90 91 94 98
25.0
3 4 9 11 16 18 19 24 26 32 33 36 39 41 45 62 64 70 71 72 73 79 81 90 95 97 101 102
25.0
10 15 21 25 34 37 38 40 42 43 44 46 47 48 51 53 55 59 67 75 76 77 86 99 100
25.0
17 22 29 36 49 52 54 57 58 60 63 65 68 69 78 82 83 84 89 92 93 96 103 104 105
Nightly RDI (Events/h)
Least Severe SDB (Q1) 25.0
Day Number
Day Number
Day Number
Day Number
Least Severe SDB (Q1)
Q2
Q3
Linz, D. et al. J Am Coll Cardiol EP. 2019;5(6):692–701.
AF ¼ atrial fibrillation; RDI ¼ respiratory disturbance index; SDB ¼ sleep-disoriented breathing.
Most Severe SDB (Q4)
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Sleep Apnea and AF
reproducibly and reversibly shortened the atrial re-
The present study provides evidence for a dynamic
fractory period and transiently enhanced the induc-
substrate for AF, where SDB severity on a specific
ibility of AF (15–17) and occurrence AF triggers (26).
night directly relates to AF risk during the same day
These short-term transient SDB-related arrhythmo-
(Central Illustration). Instead of the current practice of
genic changes may further contribute to the AF sub-
establishing a categorical diagnosis of SDB from a
strate. The present study provides clinical evidence
single overnight sleep study, SDB burden determined
supporting this hypothesized dynamic relationship
as the proportion of nights with higher RDI may be a
between nocturnal SDB severity and the same day AF
better metric to assess the extent of dynamic SDB-
risk, which has not been reported in humans before
related cardiovascular responses such as daily AF
(11).
risk and cardiovascular outcomes (27,28).
Until recently, the causal link between SDB and
ACKNOWLEDGMENT The
MicroPort research and
cardiovascular disease has been believed to be mainly
development department exported the de-identified
unidirectional, with SDB having a detrimental effect
pacemaker records.
on the cardiovascular system and the heart (“SDB begets AF”) (4,11). Interestingly, emerging evidence
ADDRESS FOR CORRESPONDENCE: Dr. Prashanthan
points toward a crucial involvement of cardiovascular
Sanders,
hemodynamics, nocturnal fluid shifts, and cardiac
Centre
for
Heart
Rhythm
Disorders,
Department of Cardiology, Royal Adelaide Hospital,
performance in the genesis of particularly central but
Port Road, Adelaide 5000, Australia. E-mail: prash.
also obstructive respiratory events in AF individuals
[email protected].
(“AF begets SDB”) (4,11). To test for this potential bidirectional relationship, the dataset with a 1-day offset between the AF episode and SDB severity was reanalyzed, which showed no significant relationship. However, more persistent AF episodes, which were associated with significantly higher SDB severity in our study, may impact SDB severity in longer term follow-up, which needs to be evaluated in future studies.
PERSPECTIVES COMPETENCY IN MEDICAL KNOWLEDGE: Given the documented night-to-night variability in SDB severity, repeated sleep studies should be considered in patients with high clinical suspicion of SDB, especially in the setting of treatment-resistant hypertension or AF recurrence after catheter ablation. Many
STUDY LIMITATIONS. Using this de-identified sam-
patients use CPAP intermittently which results in an
ple of patients provided clear evidence that the
artificial night-to-night variability in SDB severity;
preceding night’s (12:00
long-term SDB monitoring could help to document
AM
to 5:00
AM )
RDI can
modify the risk of experiencing AF during the same
adherence to CPAP and response in respect to reduc-
day within each patient. Attention was focused
tion in SDB and AF burden upon treatment.
exclusively on the intraindividual analysis of the interaction between SDB and AF burden, and com-
TRANSLATIONAL OUTLOOK: Technologies
parisons between patients were not performed.
implemented in implanted pacemaker home moni-
Clinical risk factors are likely to remain constant in
toring modules built in CPAP machines as well as
the maximum 6-month window that was analyzed.
radar-based or ballistic noncontact biomotion sensors
Further studies in larger well-characterized patient
with actimetry for remote monitoring of breathing
cohorts are needed. The SAM algorithm cannot
during sleep can provide valuable data on short- and
distinguish between obstructive and central apneas
long-term variability in SDB. Future prospective and
or hypopneas. However, a detailed characterization
randomized intervention studies in well characterized
of SDB events is not necessary to determine SDB
patient cohorts are warranted A) to identify the best
severity assessed by the AHI (4). Additionally,
technology to assess daily SDB severity, B) to deter-
detection of SDB by the SAM algorithm might not
mine feasibility, accuracy and cost effectiveness of the
have always been accurate (20,21).
implementation of long-term SDB monitoring as a component of risk factor modification programs, and
CONCLUSIONS Long-term SDB monitoring demonstrates considerable night-to-night variability in SDB severity, which cannot be detected by 1 single overnight sleep study.
C) to determine, whether SDB severity assessed in terms of SDB burden is a better indicator of cardiovascular outcomes than the categorical diagnosis of SDB.
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Sleep Apnea and AF
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20. Defaye P, Pépin JL, Poezevara Y, et al. Automatic recognition of abnormal respiratory events during sleep by a pacemaker transthoracic impedance sensor. J Cardiovasc Electrophysiol 2004;15:1034–40. 21. Defaye P, de la Cruz I, Martí-Almor J, et al. A pacemaker transthoracic impedance sensor with an advanced algorithm to identify severe sleep apnea: the DREAM European study. Heart Rhythm 2014;11:842–8. 22. Géroux L, Limousin M, Cazeau S. Clinical performance of a new mode switch function based on a statistical analysis of the atrial rhythm. Herzschr Elektrophys 1999;10: Suppl 1. I/15–21. 23. Cowie MR, Woehrle H, Wegscheider K, et al. Adaptive servo-ventilation for central sleep apnea in systolic heart failure. N Engl J Med 2015;373: 1095–105. 24. McEvoy RD, Antic NA, Heeley E, et al., for the SAVE Investigators and Coordinators. CPAP for prevention of cardiovascular events in obstructive sleep apnea. N Engl J Med 2016;375:919–31. 25. Yu J, Zhou Z, McEvoy RD, et al. Association of positive airway pressure with cardiovascular events and death in adults with sleep apnea: a systematic review and meta-analysis. JAMA 2017; 318:156–66. 26. Linz D, Hohl M, Ukena C, et al. Obstructive respiratory events increase premature atrial contractions after cardioversion. Eur Respir J 2015;45: 1332–40. 27. McDonald K, O’Hanlon R, Savage HO, et al. Sleep-disordered breathing in chronic heart failure is highly variable when measured remotely using a novel non-contact biomotion sensor. Eur J Heart Fail 2017;19:688–90. 28. Middeldorp ME, Pathak RK, Meredith M, et al. PREVEntion and regReSsive Effect of weight-loss and risk factor modification on Atrial Fibrillation:
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study.
Europace
2018;20:
KEY WORDS atrial fibrillation, burden,
701
VOL. 5, NO. 6, 2019
CROWN COPYRIGHT ª 2019 PUBLISHED BY ELSEVIER ON BEHALF OF THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION. ALL RIGHTS RESERVED.
Variability of Sleep Apnea Severity and Risk of Atrial Fibrillation The VARIOSA-AF Study Dominik Linz, MD, PHD,a Anthony G. Brooks, PHD,b Adrian D. Elliott, PHD,a Chrishan J. Nalliah, MBBS,c Jeroen M.L. Hendriks, PHD,a Melissa E. Middeldorp, PHD,a Celine Gallagher, RN,a Rajiv Mahajan, MD, PHD,a Jonathan M. Kalman, MBBS, PHD,c R. Doug McEvoy, MD,d Dennis H. Lau, MBBS, PHD,a Prashanthan Sanders, MBBS, PHDa
ABSTRACT OBJECTIVES This study sought to determine night-to-night variability in the severity of sleep-disordered breathing (SDB) and the dynamic intraindividual relationship to daily risk of incident atrial fibrillation (AF) by using simultaneous long-term day-by-day SDB and AF monitoring. BACKGROUND Night-to-night variability in SDB severity may result in a dynamic exposure to SDB related conditions impacting the timing and extent of cardiovascular responses. METHODS This study was an observational cohort study. Daily data for AF burden and average respiratory disturbance index (RDI) were extracted from pacemakers capable of monitoring nightly SDB and daily AF burden in 72 patients. Nightly RDI values were grouped into quartiles of severity within each patient. AF burdens of >5 min, >1 h, and >12 h were the outcome variables. RESULTS A total of 32% of patients had a mean RDI of $20/h, indicative of overall severe SDB. There was significant night-to-night variation in RDI reflected by an absolute SD of 6.3 events/h (range 2 to 14 events/h) within any given patient. Within each patient, the nights with the highest RDI (in their highest quartile) conferred a 1.7-fold (1.2 to 2.2; p < 0.001), 2.3-fold (1.6 to 3.5; p < 0.001), and 10.2-fold (3.5 to 29.9; p < 0.001) increase risk of having at least 5 min, 1 h, and 12 h, respectively, of AF during the same day compared with the best sleep nights (in their lowest quartiles). CONCLUSIONS There is considerable night-to-night variability in SDB severity which cannot be detected by 1 single overnight sleep study. SDB burden may be a better metric with which to assess the extent of dynamic SDB related cardiovascular responses such as daily AF risk than the categorical diagnosis of SDB. (Night-to-Night Variability in Severity of Sleep Apnea and Daily Dynamic Atrial Fibrillation Risk [VARIOSA-AF]; ACTRN 12618000757213) (J Am Coll Cardiol EP 2019;5:692–701) Crown Copyright © 2019 Published by Elsevier on behalf of the American College of Cardiology Foundation. All rights reserved.
T
he number of apnea and hypopnea episodes
However, recent studies suggest that SDB severity in
per
[AHI])
an individual patient is not stable over time but
determined during a single overnight sleep
exhibits considerable night-to-night variability which
hour
(apnea-hypopnea-index
study is clinically used to diagnose and assess the
cannot be detected by 1 overnight sleep study (2,3).
severity of sleep-disordered breathing (SDB) (1).
This
night-to-night
variability
in
SDB
severity
From the aCentre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, South Australia, Australia; bCardiac Rhythm Management, MicroPort, Scoresby, Victoria, Australia; cDepartment of Cardiology, Royal Melbourne Hospital and Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia; and the dAdelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, and Sleep Health Service, Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia. Dr. Linz is supported by a Beacon research fellowship, University of Adelaide; is an advisory board member for LivaNova/MicroPort and Medtronic; receives lecture and consulting fees from LivaNova/MicroPort, Medtronic,
ISSN 2405-500X/$36.00
https://doi.org/10.1016/j.jacep.2019.03.005
Linz et al.
JACC: CLINICAL ELECTROPHYSIOLOGY VOL. 5, NO. 6, 2019 JUNE 2019:692–701
may result in a variable and dynamic exposure to SDB
lacking. Using simultaneous long-term night-
ABBREVIATIONS
related conditions which may impact the timing and
by-night SDB and AF monitoring, this study
AND ACRONYMS
extent of cardiovascular responses (4).
characterized night-to-night variability in
In patients with atrial fibrillation (AF), the preva-
SDB severity and examined the relationship
lence of categorically moderate-to-severe SDB ranges
between SDB and AF, with each patient
between 21% and 72% due to variable selected co-
acting as their own control. Previously, pre-
horts, applied scoring rules, and thresholds used
liminary findings highlighted the nightly
(5–8). Observational studies suggest that SDB re-
variation in SDB (18); the present study de-
duces the efficacy of catheter-based and pharmaco-
scribes the complete and expanded analysis
logical antiarrhythmic therapy and that treatment
of the VARIOSA-AF (VARIability in severity
with continuous positive airway pressure (CPAP)
Of Sleep Apnea and daily dynamic Atrial
lowers the rate of AF recurrence after electrical car-
Fibrillation) risk study.
dioversion and improves catheter ablation success rates in AF patients (9,10). Despite evolving evidence supporting an important role for the categorical SDB diagnosis in the management of AF patients, the pathophysiological
importance
of
night-to-night
variability in SDB severity for AF risk is unclear. SEE PAGE 702
AHI = apnea-hypopnea index AF = atrial fibrillation CPAP = continuous positive airway pressure
RDI = respiratory disturbance index
SAM = sleep apnea monitoring SDB = sleep-disordered breathing
METHODS STUDY DESIGN. This study was an observational
cohort study. The study was approved by the Clinical Research Ethics Committee of the Royal Adelaide Hospital, Adelaide, Australia, and was registered in Australian New Zealand Clinical Trials Registry
Pre-clinical and clinical observations suggest that
(ACTRN identifier 12618000757213).
SDB together with chronic concomitant conditions
PATIENT POPULATION. The cohort was extracted
such as hypertension and obesity may promote an
from a de-identified home monitoring database of
arrhythmogenic progressive structural remodeling
patients with dual-chamber pacemakers implanted
progress in SDB (11–14). In addition to the chronic
according to guideline-directed indications (19). A
structural alterations in the atria, transient apnea-
data dump was performed in a sample of 191 patients
associated arrhythmogenic changes may further
with
enhance
dynamic
(MicroPort, Clamart, France) with implemented SDB
AF
693
Sleep Apnea and AF
susceptibility,
creating
a
Reply
200
or
Kora
100
DR
pacemakers
arrhythmogenic substrate in the atrium (11,15–17).
monitoring measuring a respiratory disturbance in-
Theoretically, rather than the categorical diagnosis
dex (RDI) (20,21). Daily SDB and AF burdens of all
of SDB obtained from a single diagnostic overnight
downloaded patient data were extracted by the
sleep study, AF risk may be mediated through dy-
MicroPort research department and provided in a de-
namic night-to-night changes in SDB severity.
identified file. Demographic or clinical descriptive
Although chronic structural alterations induced by
data could not be retrieved beyond that recorded on
SDB have already been described in AF patients with
the pacemaker, given the de-identified nature of the
SDB (13,14), clinical evidence for a dynamic AF sub-
dataset. Figure 1 provides a CONSORT Figure of the
strate in patients related to nightly SDB severity is
study population.
and ResMed; and has received funding from Sanofi, ResMed, and Medtronic. Drs Elliott and Hendriks are supported by Early Career Fellowships from the National Heart Foundation of Australia. Dr. Hendriks holds a Derek Frewin Lectureship, University of Adelaide; and is a consultant for Medtronic and Pfizer/BMS. Ms. Middeldorp is supported by a postgraduate scholarship from the National Health and Medical Research Council (NHMRC) of Australia. Ms. Gallagher is supported by a Leo J. Mahar scholarship, University of Adelaide. Dr. Mahajan is supported by an Early Career Fellowship, NHMRC, and a Leo J. Mahar lectureship, University of Adelaide; receives lecture and consulting fees from Medtronic, Abbott, Bayer, and Pfizer; and has received funding through his institution from Abbott, Medtronic, and Bayer. Drs. McEvoy, Kalman, and Sanders are supported by Practitioner Fellowships, NHMRC of Australia. Dr. McEvoy has received funding from Philips Respironics, ResMed, and Fisher and Paykel. Dr. Sanders is an advisory board member of Biosense-Webster, Medtronic, St. Jude Medical, Boston Scientific, CathRx; and has received funding through his institution from Biosense-Webster, Medtronic, St. Jude Medical, Boston Scientific and LivaNova/ MicroPort. Dr. Lau is supported by a Robert J. Craig Lectureship, University of Adelaide; and has received lecture and consulting fees from Abbott, Biotronik, Boehringer Ingelheim, Bayer, and Pfizer. Dr. Brooks is an employee of LivaNova/MicroPort. Dr. Lim is supported by a Neil Hamilton Fairley Early Career Fellowship, NHMRC of Australia. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. William Stevenson, MD, served as Guest Editor for this paper. All authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Clinical Electrophysiology author instructions page. Manuscript received December 17, 2018; revised manuscript received February 25, 2019, accepted March 13, 2019.
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and are processed to determine the amplitude and
F I G U R E 1 CONSORT Figure of the Study Population
period of respiratory cycles (i.e., minute ventilation). The SAM algorithm defines ventilation pause as an absence of respiratory cycle for >10 s and ventilation reduction as a decrease of respiratory amplitude by at least 50% for >10 s. An RDI for the respective day is then calculated as the total number of ventilation pauses and reductions per hour during a 5-h programmable monitoring period between 12:00
AM
and
5:00 AM . In the DREAM study, an RDI $20/h identified patients with severe SDB (AHI $30/h) by polysomnography performed on the same night with 89% sensitivity and 85% specificity (NCT01537718) (20,21). The RDI is determined on a nightly basis. Night-to-night variability in RDI was determined using a nightly coefficient of variation and/or SD within the patient monitoring period and then averaged across the sample. To characterize the SDB pattern within each patient, SDB category changes between severe (mean RDI: $20/h) or nonsevere SDB (mean RDI: <20/h) based on the average RDI during consecutive, nonoverlapping, 1-week blocks were determined. A paroxysmal pattern was present if consecutive weeks of 1 SDB category were separated by at least 1 week with a different SDB category. In contrast, a persistent SDB patterns was present if all consecutive
weeks
displayed
a
mean
RDI
of
either $20/h or <20/h. To standardize across patients, each patient’s daily RDIs were grouped into quartiles, with the highest quartile representing the 25% of nights with the highest RDI within each patient. ATRIAL Exclusion criteria included <1 month of follow-up since implantation (n ¼ 44), SDB monitoring was turned off (n ¼ 27), >10% invalid RDI measurements (n ¼ 10), unreliable mode switch estimates due to atrial under-sensing (n ¼ 14), persistent AF throughout the recording (n ¼ 6), and unreliable AV block statistics in patients programmed to Safe-R (DDD
FIBRILLATION/ATRIAL
TACHYCARDIA
EVENTS. The outcome variables were classified ac-
cording to pacemaker-defined “mode switch events” (MicroPort) (22). In a MicroPort dual-chamber device, a mode switch requires a sustained pathological ac-
switching with >50% of V sense in a committed period [n ¼ 10]), or
celeration of the atrial channel (>25% acceleration
incomplete data for interrupted interrogations (n ¼ 2). Seventy-two included
compared to the mean of 8 preceding “normal” atrial
patients had <25% mode switch burden (lower overall AF burden). Six additional patients with >25% mode switch burden but at least 1 reference period of sinus rhythm (higher overall AF burden) were included for the
cycles) >120 beats/min. At least 28 of 32 atrial accelerated beats (87.5%) (primary criterion) or secondarily
investigation of the directionality of the SDB and AF relationship only. AF ¼
at least 36 of 64 accelerated atrial beats (56.3%) are
atrial fibrillation; AV ¼ atrioventricular; CONSORT ¼ Consolidated Statement
required for mode switch declaration. The duration of
of Reporting Trials; DDD ¼ dual chamber paced, sensed and response to
mode switch for each day was determined to range
sensing; DR ¼ rate adaptive; Kora 100 or Reply 200 ¼ Kora 100 or Reply 200
from 1 to 86,400 s (24 h) after the primary or secondary
DR pacemakers (MicroPort); RDI ¼ respiratory disturbance index; Safe-R ¼ Safe-R mode; SDB ¼ sleep-disordered breathing.
criterion were met. To assess the relationship between SDB severity and AF burden, analyses of 3 different total cumulative mode switch episodes were recorded:
NIGHT-TO-NIGHT SDB MONITORING. Regardless of
>5 min per day; >1 h per day; and >12 h per day were
the diagnosis of SDB, daily SDB severity was assessed
included. Thus, 5 1-min mode switch episodes were
by the pacemaker-based sleep apnea monitoring
effectively treated the same as a 1 5-min episode of
(SAM) (MicroPort) algorithm that was described and
AF. Electrograms of up to 11 mode switch events were
validated
polysomnography
available, and all mode switch recordings were
(20,21). Briefly, transthoracic impedance measure-
manually adjudicated to ensure genuine AF, atrial
ments are measured at 6 Hz (through micropulses)
flutter, or atrial tachycardia.
previously
against
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F I G U R E 2 Representative SDB Recordings
Patient 1 shows overall mild SDB with a paroxysmal SDB pattern, high night-to-night variability in SDB severity, and lower overall SDB burden (below the RDI of 20/h during most nights). Patient 2 shows overall moderate SDB with a paroxysmal SDB pattern, high night-to-night variability in SDB severity and higher overall SDB burden. Patient 3 shows overall severe SDB with a persistent SDB pattern (above RDI 20/h most nights), high night-to-night variability in SDB severity and higher overall SDB burden. Q1 to Q4 ¼ quartiles 1 to 4; other abbreviations as in Figure 1.
DIRECTION
OF
RELATIONSHIP. To
examine the
performed our analyses in patient subsets that had at
directionality of relationship “SDB begets AF” or “AF
least 1 day of cumulative daily AF >5 min (n ¼ 32), >1 h
begets SDB,” the prediction analysis for RDI data was
(n ¼ 29), and >12 h (n ¼ 11). Patients who did not have a
run (e.g., from Friday night to Saturday morning) and
suitable “AF event” were excluded because their
the AF burden (e.g., from Saturday) of the same day
within-patient OR were null. Mean changes in RDI in
and reran the analysis with a 1-day offset between the
the subgroup with higher AF burden (n ¼ 6) were
AF episode and the RDI, such that the AF burden
assessed using mixed effects models with device serial
during 1 day (e.g., during Saturday) then corre-
number as the random factor. All data were analyzed
sponded to the SDB severity determined by RDI of the
using SPSS version 24 software (IBM, Armonk, New
following morning (e.g., from Saturday night to Sun-
York) and significance was set at a p value of <0.05.
day morning). We also examined the 6-patient subset with higher overall AF burden was also examined
RESULTS
(>25% AF burden) and means tested the nocturnal RDI on days with 100% AF burden compared to days
SAMPLE CHARACTERISTICS. The sample consisted
without AF within each patient.
of 72 de-identified SAM-monitored dual-chamber
STATISTICAL ANALYSIS. Data are mean SD when
normally distributed and median (interquartile range [IQR]) if data followed a skewed distribution. Categorical data are proportion with numerator and denominator in brackets. A binary logistic generalized estimating equation was used to develop a prediction model for the dichotomous AF outcome (>5 min odds ratio [OR]: >1 h; OR >12 h of cumulative mode switch per day) using the within-patient standardized (quartiles) SDB severity data. A subject variable of
pacemaker patients with lower overall AF burden (<25% AF burden). The median percent atrial pace was 55% (range 20% to 85%), and the percent of ventricular pace was 2.4% (range 0% to 41%). Ninety percent of the sample was programed to Safe-R (65 of 72) a minimized ventricular pacing algorithm; 58% of patients (42 of 72) had rate-response activated. The mean follow-up per patient was 21 8 weeks, resulting in a total daily monitoring time for the sample of >28 years (10,383 days).
device serial number was introduced as the clustering
SLEEP-DISORDERED
variable to account for the nesting of outcome events
BURDEN AND NIGHT-TO-NIGHT VARIABILITY. The
BREATHING
SEVERITY:
within the individual. The SDB severity reference
SAM algorithm provided a valid RDI estimate in 10,315
category was always the lowest RDI quartile. We
of 10,383 days (99%). The average RDI across the
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JUNE 2019:692–701
F I G U R E 3 Proportion of Days With AF
Percentage of days with at least >5 min, >1 h, and >12 h of AF for each within-patient quartile (Q1 to 4) of SDB severity, assessed by nightly RDI. Pie chart black sections indicate days with at least >5 min, >1 h, and >12 h of AF; yellow sections indicate days without at least >5 min, >1 h, and >12 h of AF. Abbreviations as in Figure 1.
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Sleep Apnea and AF
sample was 17.9 11.5 events/h. In 32% of patients (23
episode and the RDI. It was found that all relation-
of 72) the mean RDI was $20/h, indicative of overall
ships dissolved (Figure 4). Six patients with a higher
severe SDB.
AF burden (AF burden: >25%) were analyzed sepa-
Figure 2 shows the night-to-night RDI variability in
rately to examine the potential relationship between
3 representative patients with different levels of un-
longer AF episodes and increased RDI. In this sub-
derlying SDB severity. Fifty-six percent of patients
group, the number of days with 24 h of AF, 0% AF,
(40 of 72) had at least 1 week with a mean RDI $20/h,
and paroxysmal AF (>0%, but <24 h) was 422 (50%),
of whom 73% (29 of 40) demonstrated paroxysmal
139 (17%), and 279 (33%) days, respectively. It was
weekly SDB patterns (i.e., weeks of RDI $20/h sepa-
found that days with 100% AF burden were associ-
rated by at least 1 week with an RDI <20/h) in contrast
ated with significantly higher RDIs (p < 0.001) than
to persistent SDB patterns in the minority of 28% (11
those days without AF.
of 40) where all consecutive weeks displayed a mean RDI $20/h. An RDI $20/h during at least 1 night was
DISCUSSION
observed in 85% of patients (61 of 72). The individual mean night-to-night RDI coefficient of variation was
Long-term SDB monitoring by chronically implanted
41 16%, which reflected an absolute SD of 6.3
devices revealed a considerable night-to-night vari-
events/h within each patient (range 2 to 14 events/h).
ability in SDB severity, which cannot be detected by a
The median RDI for each within-patient quartile (Q1
single overnight sleep study. Long-term SDB and
to 4) was 8.2 (IQR: 4.6 to 15.2), 12.6 (IQR: 7.8 to 21.6),
simultaneous AF monitoring showed for the first time
16.4 (IQR: 10.4 to 26.8), and 23.2 (IQR: 15.4 to 35.5).
that, in individual patients, the nights with the
From the lowest to the highest quartile for the entire
highest SDB severity conferred a more than 2.3-fold
sample.
increase risk of having at least 1 h of AF the same
ATRIAL
FIBRILLATION/ATRIAL
TACHYCARDIA
BURDEN. Of patients with lower overall AF burden
(<25% AF burden), 57% of patients (41 of 72) had at least a single mode switch episode recorded on their device. Cumulative AF on any single day of their follow-up of >5 min, >1 h, and >12 h was observed in 44% (32 of 72), 40% (29 of 72), and 15% of patients (11 of 72), respectively. The total mode switch burden ranged from 0% to 24.2% with the median daily episode duration of 11.6 min (range 1.3 to 84 min). The total number of days satisfying >5 min, >1 h, and >12 h AF events within the monitoring period of each patient subset was 661 of 4,543 days (14.5%), 472 of 3,994 days (11.8%), and 130 of 1,748 days (7.4%), respectively.
day compared to nights with the lowest SDB severity. By contrast, there was no increased risk of AF in the 24 h before the nights with the highest SDB severity. These 2 contrasting observations provide strong evidence for a feed-forward mechanistic link between SDB and AF, whereby the increased cardiac stress induced by even a single night of more severe SDB can establish conditions conducive to AF. These findings have important implications for the assessment of SDB severity and the guidance of SDB treatment in patients. The result of a single overnight sleep study may not be representative of SDB severity during the remaining 364 days of a year in a specific patient and could thus lead to frequent misclassification of SDB status in patients if just 1 overnight sleep study is performed (4). It may also help explain
RELATIONSHIP BETWEEN SDB SEVERITY AND AF
the highly variable prevalence of SDB (2–4) and may
BURDEN. Figure 3 summarizes the proportion of days
have contributed to the negative results of recent
with at least >5 min, >1 h, and >12 h AF for each
randomized
within-patient quartile of SDB severity. Within each
studies (23–25). The proportion of nights with high
patient, the nights (12:00
RDI (SDB burden), rather than a categorical diagnosis
AM
to 5:00
AM )
with the
controlled
SDB
treatment
outcome
highest RDI (in their highest quartile) conferred a 1.7-
of SDB per se, may better reflect the exposure to SDB-
fold (range 1.2- to 2.2-fold; p < 0.001), a 2.3-fold
related factors and may be a more useful metric to
(range 1.6- to 3.5-fold; p < 0.001), and a 10.2-fold
determine SDB severity in the management of
(range 3.5- to 29.9-fold; p < 0.001) increased risk of
concomitant cardiovascular diseases such as AF (4).
having at least 5 min, 1 h, and 12 h of AF during the
Importantly, the relationship between nights with
same day compared with the quartile of nights with
more severe SDB and increased AF risk held for all
the lowest RDI (Figure 4).
individuals, regardless of the clinical diagnosis of SDB
To test for a potential bidirectional relationship
and mean RDI. This suggests that it may be the rela-
with AF begetting SDB, the dataset of patients with
tive rather than the absolute increase in SDB severity
lower
(AF
on a single night that increases AF risk in the indi-
burden: <25%) with a 1-day offset between the AF
vidual patient. Different mechanisms such as longer
overall
AF
burden
was
reanalyzed
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Sleep Apnea and AF
F I G U R E 4 Relationship Between SDB Severity and AF
(Upper panels) Relationship between SDB severity, assessed by RDI during 1 night (12:00 AM to 5:00 AM), and the risk of having at least 5 min, 1 h, and 12 h of AF during the same day compared with the quartile with the lowest RDI for each within-patient quartile (Q1 to 4) of RDI severity (“SDB begets AF”). (Lower panels) Relationship with a 1-day offset between the AF episode and the RDI (“AF begets SDB”). Unadjusted odds ratios are shown. Abbreviations as in Figure 1.
time spent sleeping supine, more alcohol consump-
contributes to a dynamic AF substrate. Pre-clinical
tion before bed, and variable compliance or with-
studies suggest that intermittent hypoxia, auto-
drawal from CPAP treatment may contribute to
nomic dysregulation, and intrathoracic pressure
variable SDB severity. SDB burden determined by
swings
continuous long-term SDB monitoring incorporates
together with chronic concomitant conditions like
the considerable night-to-night variability in SDB
hypertension and obesity, promote an arrhythmo-
severity documented in this study and previously
genic progressive structural remodeling process in
described in patients with and without co-occurring
the atrium (4,11). This structural remodeling, charac-
cardiovascular disease (2,3,20,21).
terized by more scar formation in the atrium, could be
during
obstructive
respiratory
events,
The present study provides the first clinical evi-
documented by electroanatomical mapping studies in
dence for the pathophysiological relevance of the
AF patients with SDB (13,14). In animal models of
night-to-night variability in SDB severity which
SDB,
simulated
obstructive
respiratory
events
Linz et al.
JACC: CLINICAL ELECTROPHYSIOLOGY VOL. 5, NO. 6, 2019 JUNE 2019:692–701
Sleep Apnea and AF
C ENTR AL I LL U STRA T I O N Simultaneous Long-Term Day-by-Day Sleep Apnea and Atrial Fibrillation Monitoring
Nightly RDI (Events/h)
20
Cumulative Daily AF Burden (h)
25
24
15 10 5 0
18 12 AF ive t a l mu
6 >1 h
cu
0 10
20
24% days AF >1 h
30
40
50 60 Day Number
20% days AF >1 h
AF
70
80
90
21% days AF >1 h
AF
100
39% days AF >1 h
AF AF
Q2
Q3
Most Severe SDB (Q4)
20.0
20.0
20.0
20.0
15.0
15.0
15.0
15.0
10.0
10.0
10.0
10.0
5.0
5.0
5.0
5.0
.0
.0
.0
.0 2 5 6 7 8 12 13 14 20 23 27 29 30 31 50 56 61 66 74 80 85 87 90 91 94 98
25.0
3 4 9 11 16 18 19 24 26 32 33 36 39 41 45 62 64 70 71 72 73 79 81 90 95 97 101 102
25.0
10 15 21 25 34 37 38 40 42 43 44 46 47 48 51 53 55 59 67 75 76 77 86 99 100
25.0
17 22 29 36 49 52 54 57 58 60 63 65 68 69 78 82 83 84 89 92 93 96 103 104 105
Nightly RDI (Events/h)
Least Severe SDB (Q1) 25.0
Day Number
Day Number
Day Number
Day Number
Least Severe SDB (Q1)
Q2
Q3
Linz, D. et al. J Am Coll Cardiol EP. 2019;5(6):692–701.
AF ¼ atrial fibrillation; RDI ¼ respiratory disturbance index; SDB ¼ sleep-disoriented breathing.
Most Severe SDB (Q4)
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Sleep Apnea and AF
reproducibly and reversibly shortened the atrial re-
The present study provides evidence for a dynamic
fractory period and transiently enhanced the induc-
substrate for AF, where SDB severity on a specific
ibility of AF (15–17) and occurrence AF triggers (26).
night directly relates to AF risk during the same day
These short-term transient SDB-related arrhythmo-
(Central Illustration). Instead of the current practice of
genic changes may further contribute to the AF sub-
establishing a categorical diagnosis of SDB from a
strate. The present study provides clinical evidence
single overnight sleep study, SDB burden determined
supporting this hypothesized dynamic relationship
as the proportion of nights with higher RDI may be a
between nocturnal SDB severity and the same day AF
better metric to assess the extent of dynamic SDB-
risk, which has not been reported in humans before
related cardiovascular responses such as daily AF
(11).
risk and cardiovascular outcomes (27,28).
Until recently, the causal link between SDB and
ACKNOWLEDGMENT The
MicroPort research and
cardiovascular disease has been believed to be mainly
development department exported the de-identified
unidirectional, with SDB having a detrimental effect
pacemaker records.
on the cardiovascular system and the heart (“SDB begets AF”) (4,11). Interestingly, emerging evidence
ADDRESS FOR CORRESPONDENCE: Dr. Prashanthan
points toward a crucial involvement of cardiovascular
Sanders,
hemodynamics, nocturnal fluid shifts, and cardiac
Centre
for
Heart
Rhythm
Disorders,
Department of Cardiology, Royal Adelaide Hospital,
performance in the genesis of particularly central but
Port Road, Adelaide 5000, Australia. E-mail: prash.
also obstructive respiratory events in AF individuals
[email protected].
(“AF begets SDB”) (4,11). To test for this potential bidirectional relationship, the dataset with a 1-day offset between the AF episode and SDB severity was reanalyzed, which showed no significant relationship. However, more persistent AF episodes, which were associated with significantly higher SDB severity in our study, may impact SDB severity in longer term follow-up, which needs to be evaluated in future studies.
PERSPECTIVES COMPETENCY IN MEDICAL KNOWLEDGE: Given the documented night-to-night variability in SDB severity, repeated sleep studies should be considered in patients with high clinical suspicion of SDB, especially in the setting of treatment-resistant hypertension or AF recurrence after catheter ablation. Many
STUDY LIMITATIONS. Using this de-identified sam-
patients use CPAP intermittently which results in an
ple of patients provided clear evidence that the
artificial night-to-night variability in SDB severity;
preceding night’s (12:00
long-term SDB monitoring could help to document
AM
to 5:00
AM )
RDI can
modify the risk of experiencing AF during the same
adherence to CPAP and response in respect to reduc-
day within each patient. Attention was focused
tion in SDB and AF burden upon treatment.
exclusively on the intraindividual analysis of the interaction between SDB and AF burden, and com-
TRANSLATIONAL OUTLOOK: Technologies
parisons between patients were not performed.
implemented in implanted pacemaker home moni-
Clinical risk factors are likely to remain constant in
toring modules built in CPAP machines as well as
the maximum 6-month window that was analyzed.
radar-based or ballistic noncontact biomotion sensors
Further studies in larger well-characterized patient
with actimetry for remote monitoring of breathing
cohorts are needed. The SAM algorithm cannot
during sleep can provide valuable data on short- and
distinguish between obstructive and central apneas
long-term variability in SDB. Future prospective and
or hypopneas. However, a detailed characterization
randomized intervention studies in well characterized
of SDB events is not necessary to determine SDB
patient cohorts are warranted A) to identify the best
severity assessed by the AHI (4). Additionally,
technology to assess daily SDB severity, B) to deter-
detection of SDB by the SAM algorithm might not
mine feasibility, accuracy and cost effectiveness of the
have always been accurate (20,21).
implementation of long-term SDB monitoring as a component of risk factor modification programs, and
CONCLUSIONS Long-term SDB monitoring demonstrates considerable night-to-night variability in SDB severity, which cannot be detected by 1 single overnight sleep study.
C) to determine, whether SDB severity assessed in terms of SDB burden is a better indicator of cardiovascular outcomes than the categorical diagnosis of SDB.
Linz et al.
JACC: CLINICAL ELECTROPHYSIOLOGY VOL. 5, NO. 6, 2019 JUNE 2019:692–701
Sleep Apnea and AF
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Europace
2018;20:
KEY WORDS atrial fibrillation, burden,
701