University of Groningen
Towards prevention of AF progression
Hobbelt, Anne
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Chapter 4
Progression of atrial fibrillation in
a well-characterized low-risk AF
population
Anne H. Hobbelt, Ruben R. De With, Elton A.M.P. Dudink, Harry J.G.M. Crijns, Michiel Rienstra, and Isabelle C. Van Gelder
44 Chapter 4
absTRaCT background
Atrial fibrillation (AF) is a progressive disease. Prediction of patients who will progress to more permanent forms of AF is relevant in order to institute optimal therapy. Except for clinical characteristics also AF patterns and burden may help to improve risk prediction for AF progression. Our aim was to investigate the clinical characteristics associated with different AF patterns, and the role of AF patterns, for prediction of AF progression.
methods
We studied patients with paroxysmal and persistent AF included in the Identification of a risk profile to guide AF therapy (AF-Risk) study who underwent repeat Holter monitoring. Patients who used antiarrhythmic drugs and/or underwent any kind of ablation during the study were excluded from the present analysis. Based on subsequent Holter monitoring patients were classified into four AF patterns: (1) scattered paroxysmal AF (PAF) when there was no AF seen on all performed Holters (group 1), (2) short self-terminating PAF (AF episodes < 60minutes, group 2), (3) long-lasting self-terminating PAF episodes (AF episodes > 60minutes, group 3), or (4) non-self-terminating AF (group 4).
Results
In the present analysis 295 patients were included, mean age 60 ± 12, 45% were women. Significant differences between the groups existed in age (p = 0.01), heart failure (p < 0.01), coronary artery disease (p = 0.01) and in functional and structural measures of the left ventricular and atrial sizes and volume. After 12 months follow-up 7% of the patients in group 1 showed progression to more persistent forms of AF, 13% in group 2, and 13% in group 3. In group 4 43% showed progression to permanent AF.
Conclusion
Clinical characteristics varied according to AF pattern based on Holter monitoring. Pro-gression of AF varied according to AF pattern on 24 hours Holter monitoring.
inTRoduCTion
Atrial fibrillation (AF) is a progressive disease due to continuous atrial structural and electrical remodeling. AF usually starts in a paroxysmal self-terminating form and gradu-ally proceeds to a more persistent state, eventugradu-ally deteriorating into permanent AF.1,2
Especially fibrosis is considered to be a key player in the structural changes that initiate and perpetuate AF. Novel techniques are currently giving more insights into the extent and the electrophysiological consequences of the remodeling processes associated with AF.3,4 The causes of AF can roughly be divided into “triggers” and “underlying substrate”.
Every AF episode needs a trigger to initiate the arrhythmia. Substrate on the other hand, being a consequence of increased atrial stretch triggering left atrial dilation, inflamma-tion, fibrosis, and infiltration of fat, causes local electrical disturbances and structural remodeling setting the stage for sustenance of AF. Underlying cardiovascular diseases, for example hypertension, heart failure, obesity and diabetes contribute to the progression of substrate, and subsequently deterioration of self-terminating AF into more permanent forms of AF.3,5 Triggered AF in the absence of a severe substrate is often self-terminating
and easier to treat.6,7 On the other hand, ongoing triggers as well can contribute to the
development of deterioration of the substrate, causing a shift from one group to the other.4
So, differences in underlying diseases and risk factors, and AF itself may lead to different patterns of AF. Because current clinical AF classification does not take into consideration a careful characterization of AF patterns, this may be one of the reasons why, at present, we are not able to adequately predict AF progression, as well as expected treatment outcome. An improved AF classification based on continuous rhythm monitoring and actual AF patterns, in addition to clinical characteristics may improve prediction of AF progression and guide AF therapy. For this reason, it was our aim was to investigate the clinical charac-teristics associated with different AF patterns, and the role of AF patterns for prediction of AF progression in a well-characterized group of AF patients.
meThods study design
For the present analysis we used data from the identification of a risk profile to guide atrial fibrillation (AF-Risk) study. The AF-Risk study was a prospective, observational, multi-cen-ter study. Aim of the study was to assess the risk profile associated with success of rhythm control therapy in short-lasting, meaning a short history of AF, symptomatic paroxysmal AF. The study was performed in the University Medical Center Groningen (UMCG) and the Maastricht University Medical Center (MUMC), The Netherlands. The study was approved
46 Chapter 4
by the institutional review boards and was registered on clinicaltrials.gov (NCT01510197). All patients signed informed consent.
Patient population
Total target for this study was 500 patients and total follow up will be 5 years. Between April 2011 and March 2016 all 500 patients were included in the study. All patients aged 18 years and older who presented at the cardiology department with short-lasting symptom-atic paroxysmal AF (total AF history less than 2 years, or total AF history less than 3 years in case of ≤ 2 AF episodes of ≤ 48 hours per month terminating spontaneously) or with short-lasting persistent AF (total AF history < 2 years, and total persistent AF duration > 7 days and < 12 months) in whom a rhythm control strategy was preferred and who had no contra-indication for oral anticoagulation were eligible for participation. Patients with a history of heart failure > 3 years; a history of severe valvular disease; post-operative AF; or one of the following events in the past month: acute coronary syndrome, myocardial infarction (MI), percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) were not eligible to participate. For the present analysis we included patients without AAD and/or PVI previous to and/or during the study. Also, to exclude substrate remodeling due to AAD use or ablation, only patients without AAD and/or PVI previous to and/or during the study were included in present analysis.
study procedures
After inclusion and baseline data collection all patients received a rhythm control treat-ment according to current AF guidelines,8
which includes: (1) causal treatment of under-lying (heart) disease; (2) adequate rate control therapy with beta-blocker or verapamil/ diltiazem; (3) adequate anticoagulation depending on CHA2DS2-VASc score. During the
AF-Risk study antiarrhythmic drugs (AAD) were instituted in case of frequent recurrences of symptomatic AF and pulmonary vein isolation (PVI) was performed in case of side ef-fects of AAD or in symptomatic patients despite adequate AADs.
At inclusion patients’ demographics and clinical characteristics concerning underlying cardiovascular disease, cardiovascular risk factors, lifestyle, specific AF triggers, AF com-plaints, and medication use were collected. All patients underwent physical examination, electrocardiogram, 24-48 hours Holter monitoring, 2 weeks of rhythm monitoring by Vitaphone Tele-ECG-card, echocardiography, and exercise test.
echocardiography
At baseline all patients underwent a standard two-dimensional transthoracic echocardio-gram (General Electric Vivid E9) during continuous ECG monitoring. Standard views were the parasternal long and short axis, apical 2-,3- and 4-chamber view. Atrial and ventricular dimensions, and valvular function were measured according to the recommendations of
the European Society of Cardiology.9 Systolic left ventricular function was measured using
the “eyeballing” technique or the Simpson biplane method of discs.
holter monitoring
At baseline and during follow-up all patients underwent 24-48 hours Holter monitoring. All Holters that were performed ≤ 6 months before or after inclusion and ≤ 6 months before or after 12 months follow-up were suitable for analysis. Holters were analyzed by a central core lab before they were used for clinical classification of AF pattern. Holters of patients who were using or started with class I or class III AADs, or who underwent PVI or any other kind of ablation were excluded from further analysis.
Based on all subsequent Holters performed ≤ 6 months before and/or after inclusion individuals were classified into four AF patterns. Patients were classified as (1) scattered paroxysmal AF (PAF) when there was no AF seen on all performed Holters, (2) short self-terminating AF when all AF episodes monitored during Holter monitoring terminated spontaneously within 60 minutes, (3) long-lasting self-terminating AF episodes when the longest AF episode(s) terminated spontaneously after a minimum duration of 60 minutes, or (4) non-self-terminating AF, when all Holters showed continuously AF and AF episodes did not terminate spontaneously. The same classification was made after 12 months follow-up, based on Holter monitoring performed ≤6 months before and/or after 12 months follow-up.
Covariate definitions
Total AF duration was defined as time from first documented AF episode until study inclu-sion. Coronary heart disease was defined as history of percutaneous coronary intervention or myocardial infarction or coronary artery bypass grafting. Hypertension was defined as systolic blood pressure > 140 mmHg and diastolic blood pressure > 90 mmHg or by use of antihypertensive medication. Diabetes mellitus was defined as the use of anti-diabetic drugs. Hypercholesterolemia was defined as using lipid-lowering medication. Patients were classified as having heart failure in the presence of a LVEF ≤ 45% at baseline or LVEF > 45% with symptoms associated with heart failure (New York Heart Association functional class II or III) or previous hospitalization for heart failure. Chronic obstructive pulmonary disease was defined as use of bronchodilators. The ratio of weight to height squared (kg/ m2) was used for calculation of body mass index. Obesity was defined as body mass index >
30 kg/m2
. AF progression was defined as a shift in AF pattern towards a pattern with longer AF episode duration and/or development of permanent AF. AF regression was defined as a shift in AF pattern towards a pattern with shorter AF episode duration and/or (new) absence of AF.
48 Chapter 4
statistical analysis
Descriptive statistics are presented as mean ± standard deviation (SD) or median (inter-quartile range (IQR)) for continuous variables and numbers with percentages for categori-cal variables. Differences between groups were analyzed by c2
test for categorical variables. For continuous variables differences between groups were evaluated by One-way Analysis Of Variance for normally distributed variables and Kruskal-Wallis Test for variables that were not normally distributed. A p-value was considered significant when < 0.05. All analyses were conducted using SPSS program version 22.
ResulTs
baseline characteristics
We included 295 patients in the present analysis. Mean age was 60 ± 12, 45% were women. In Table 1, clinical risk factors, cardiovascular diseases, and echocardiographic character-istics are depicted according to AF pattern. Significant differences between the AF pattern groups were seen in clinical characteristics such as age (p = 0.01), heart failure (p < 0.01), coronary artery disease (p = 0.01), and functional and structural measures of the left ven-tricle and atrial sizes and volume. Also complaints differed.
af progression
After 12 months follow-up progression to more persistent forms of AF occurred in 7% of patients in the scattered PAF group, in 13% in the short-lasting self-terminating AF group, with 1 additional patient (3%) who progressed to permanent AF, and in 13% in the long-lasting self-terminating AF group. In that group 2 patients (9%) developed permanent AF. In the non-self-terminating AF group 43% of the patients showed progression to perma-nent AF (Table 2 and Figure 1).
af regression
After 12 months follow-up 82% of the patients in the scattered PAF group again showed no AF during repeat Holter monitoring. In the short-lasting self-terminating AF group 41% had a stable AF pattern and 38%, showed regression of AF to no AF during Holter monitor-ing. In the long-lasting self-terminating AF group 30% had a stable AF pattern and 48% showed regression of AF to no AF or shorter AF episodes during Holter monitoring. In the non-self-terminating persistent AF group 13% had a stable AF pattern and 35% showed regression of AF to no AF or shorter AF episodes during Holter monitoring (Table 2 and Figure 1).
Table 1. Baseline characteristics Characteristic Scattered paroxysmal AF (n=186)
Self-terminating AF episodes <60min
(n=32)
Self-terminating AF episodes >60min
(n=23)
Non-self-terminating
persistent AF
(n=54)
p-value
Age at inclusion (years)
58±13 62±12 63±10 64±10 0.01 Male sex 115 (62%) 19 (59%) 16 (70%) 43 (80%) 0.09 Tot al AF History (months) 3.6 (1.7-13.9) 3.4 (1.7-8.7) 2.6 (1.4-6.1) 5.3 (2.7-12.6) 0.29 AF pattern at inclusion Paroxysmal 179 (96%) 31 (97%) 21 (91%) 5 (9%) <0.01 Persistent 7 (4%) 1 (3%) 2 (9%) 47 (87%) Permanent -2 (3.7) Underlying conditions CHA2DS2-VASc score* 1.0 (1.0-2.0) 1.5 (1.0-2.0) 2.0 (0.5-2.0) 2.0 (1.0-3.5) 0.06 Hypertension 91 (49%) 13 (41%) 10 (44%) 33 (61%) 0.19 TIA/Stroke 11 (6%) 2 (6%) 1 (4%) 6 (11%) 0.54 Heart failure 11 (6%) 1 (3%) 3 (13%) 17 (32%) <0.01
Peripheral artery disease
3 (2%)
-2 (9%)
3 (6%)
0.09
Coronary artery disease
16 (9%) -10 (19%) 0.01 Diabetes mellitus 18 (10%) 2 (6%) 2 (9%) 6 (11%) 0.89 Hypercholesterolemia 73 (39%) 9 (28%) 9 (39%) 25 (46%) 0.39 COPD 14 (8%) 3 (9%) 2 (9%) 7 (13%) 0.65 Thyroid dysfunction 15 (8%) 3 (9%) 1 (4%) 3 (6%) 0.88 Cardiomyopathy 9 (5%) 1 (3%) 2 (9%) 6 (11%) 0.31 Obesity (BMI > 30kg/m 2) 56 (30%) 5 (16%) 6 (26%) 20 (37%) 0.20 Intoxication Smoking 93 (50%) 18 (56%) 15 (65%) 26 (48%) 0.34
50 Chapter 4 Chapter 4 51 Table 1. Baseline characteristic s (continued) Characteristic Scattered paroxysmal AF (n=186)
Self-terminating AF episodes <60min
(n=32)
Self-terminating AF episodes >60min
(n=23) Non-self-terminating persistent AF (n=54) p-value Past 65 (35%) 10 (31%) 11 (48%) 13 (24%) Present 28 (15%) 8 (25%) 4 (17%) 13 (24%) Physical examination
Systolic blood pressure (mmHg)
130.4±17.1
131.9±15.6
125.0±10.9
130.5±17.4
0.45
Diastolic blood pressure (mmHg)
78.6±9.3 78.8±8.7 74.9±8.9 82.3±10.7 0.01 BMI (kg/m 2) 27.4 (24.7-30.5) 25.7 (23.2-28.1) 27.1 (25.0-30.4) 28.9 (24.7-31.6) 0.07 Electrocardiogram PQ duration (ms) 162.0 (144.0-179.5) 162.0 (151.5-177.5) 171.0 (156.5-184.0) 192.0 (167.0-212.0) <0.01 Exercise test Maximum load (W att) 166.9±62.4 156.1±57.6 151.7±48.5 135.1±39.3 0.17 Echocardiographic parameters LV mass (g) 166±45 168±40 162±47 192±52 0.01 LV mass index (g/m 2) 78 (69-90) 84 (70-91) 81 (66-95) 91 (76-107) 0.01 Septum thickness (mm) 10 (9-12) 9 (9-11) 10 (8-11) 10 (9-12) 0.03
Posterior wall thickness
9 (8-10) 9 (8-10) 9 (8-10) 10 (9-11) 0.02 LVEDD (mm) 48.0 (44.0-53.0) 50.0 (45.0-53.0) 49.0 (43.0-52.0) 49.5 (45.0-55.8) 0.27 LVESD (mm) 31.0 (27.0-34.0) 32.0 (27.5-35.5) 30.0 (29.0-35.0) 33.0 (30.0-38.0) 0.01 LA PLAX (mm) 38.8±5.5 36.4±6.8 38.7±5.6 45.2±5.5 <0.01 LA length (mm) 55.1±7.6 54.8±8.1 58.8±8.9 62.5±9.0 <0.01 LA width (mm) 41.8±5.4 41.4±5.4 42.8±5.0 46.1±6.3 <0.01 LAED V (ml) 33.0 (25.1-41.0) 30.8 (21.0-49.3) 50.4 (24.1-59.9) 53.6 (37.4-72.7) <0.01 LAESV (ml) 65.2±20.7 60.4±20.2 67.1±19.5 90.2±26.5 <0.01
Table 1. Baseline characteristic s (continued) Characteristic Scattered paroxysmal AF (n=186)
Self-terminating AF episodes <60min
(n=32)
Self-terminating AF episodes >60min
(n=23) Non-self-terminating persistent AF (n=54) p-value RA major (mm) 52.1±6.1 51.2±5.9 52.5±7.3 59.8±7.6 <0.0 1 RA minor (mm) 39.3±6.1 40.1±5.2 41.2±8.0 42.4±7.4 0.04 LVEF (%) 57.5 (55.0-60.0) 57.5 (57.1-60.0) 58.8 (55.0-60.0) 55.0 (50.0-57.5) <0.01 Complaints Palpit ations 126 (68%) 25 (78%) 11 (48%) 24 (44%) <0.01 Dyspnea 29 (16%) 5 (16%) 5 (22%) 28 (52%) <0.01 Dyspnea d’effort 33 (17%) 8 (25%) 6 (26%) 34 (63%) <0.01 Fatigue 48 (26%) 8 (25%) 8 (35%) 30 (56%) <0.01 Angina 21 (11%) 5 (16%) 4 (17%) 3 (6%) 0.37 Presyncope 16 (9%) 5 (16%) 2 (9%)) 4 (7%) 0.61 Medication Bet a-blocker 108 (58%) 20 (63%) 18 (78%) 37 (69%) 0.16 A CE-inhibitor 57 (31%) 6 (19%) 8 (35%) 26 (48%) 0.02
Angiotensin Receptor Blocker
23 (12%)
3 (9%)
1 (4%)
6 (11%)
0.70
Aldosterone Receptor Ant
agonist 7 (4%) 1 (3%) - 3 (6%) 0.69 Verapamil 15 (8%) 3 (9%) 2 (9%) 2 (4%) 0.72 Other calcium-ant agonist 21 (11%) 4 (13%) 2 (9%) 6 (11%) 0.98 Digoxin 4 (2%) -1 (4%) 5 (9%) 0.047 Vit amin K ant
agonist oral anti-coagulant
90 (48%) 14 (44%) 12 (52%) 40 (74%) <0.01 Non-vit amin K ant
agonist oral anticoagulant
23 (12%) 2 (6%) 1 (4%) 9 (17%) 0.31 St atin 57 (31%) 7 (22%) 6 (26%) 24 (44%) 0.17
acetylsalicylic acid / carbasalate calcium
27 (15%)
7 (22%)
5 (22%)
11 (20%)
52 Chapter 4 Table 1. Baseline characteristic s (continued) Characteristic Scattered paroxysmal AF (n=186)
Self-terminating AF episodes <60min
(n=32)
Self-terminating AF episodes >60min
(n=23) Non-self-terminating persistent AF (n=54) p-value P2Y12-inhibitor 2 (1%) 1 (3%) 1 (4%) - 0.37 Diuretic 38 (20%) 6 (19%) 6 (26%) 20 (37%) 0.06 Dat
a are expressed as mean ± SD
, median (interquartile range) or numbers (%).
A CE, angiotensin converting enzyme; AF , atrial fibrillation; BMI, body mass index; bpm, beats per minute; cm, centimeter; COPD , chronic obstructive pulmonary disease; g, gram; g/m 2, gram per square meter; IQR, interquartile range; kg, kilogram; kg/m 2, kilogram per square meter; LA, left atrium; LAED V, left atrium end diastolic volume; LAESV , left atrium end systolic volume; LV , left ventricular; LVEDD , left ventricular end diastolic diameter; LVEF , left ventricular ejection fraction; LVESD , left ventricular end systolic diameter; LA, left atrium; ml, milliliter; mm, millimeter; mmHg, millimeters of mercury; ms, millisecond; no., number; NYHA classification, New York Heart As
-sociation classification ; PLAX, parasternal long axis; SD
, st
andard deviation; RA, right atrium; TIA, transient ischemic att
ack. * The CHA 2 DS 2 -V ASc score assesses thromboembolic risk. C, congestive heart failure/L V dysfunction; H, hypertension; A2, age ≥75 years; D , diabetes mellitus; S2, stroke/
transient ischemic att
ack/systemic embolism; V
, vascular disease; A, age 65–74
disCussion
In the present analysis we studied the clinical risk factors of individuals with four different AF patterns based on Holter monitoring and its association with AF progression. We found that clinical and echocardiographic characteristics differed importantly among the four AF patterns defined with Holter monitoring. In addition, we showed that AF progression and
Figure 1. Bar chart showing evolution of AF patterns after 12 months follow-up Table 2. Evolution of AF patterns
Characteristic Scattered paroxysmal AF (n=186) Self-terminating AF episodes < 60min (n=32) Self-terminating AF episodes > 60min (n=23) Non-self-terminating AF (n=54) AF regression - 12 (38%) 11 (48%) 19 (35%) AF stable 152 (82%) 13 (41%) 7 (30%) 7 (13%) AF progression 13 (7%) 4 (13%) 3 (13%) 23 (43%) Permanent AF at 12 months follow-up 0 (0%) 1 (3%) 2 (9%) 25 (46%)
Data are expressed as number of patients (%). AF, atrial fibrillation.
54 Chapter 4
regression varied according to AF pattern. In previous studies studying AF progression, AF progression was mainly based on data collection from medical records, medical data collection systems, and questionnaires.10-12
In addition to clinical characterization based on commonly used data collection methods our study provides prospectively collected data within our AF risk study and, importantly, repeat Holter monitoring for more accurate AF pattern characterization.
af pattern and severity of underlying diseases
AF is a progressive disease and usually starts in a paroxysmal self-terminating form and gradually progresses to a more persistent state and eventually a permanent presence of the arrhythmia1,2
, a phenomenon which is called AF progression. AF progression is increas-ingly recognized as a complication of AF and is associated with AF associated adverse events.4 AF progression is the result of AF as a multifactorial progressive disease of the
atria, in which several mechanisms are proposed to influence the pathophysiology of AF progression. 1,2
These mechanisms include ion channel dysfunction, abnormalities in calcium-handling, structural remodeling and autonomic neural dysregulation.13 A great
amount of risk factors and comorbidities associated with AF are thought to be associated with an increased AF risk and subsequently progression of AF, among others advancing age, heart failure, hypertension, diabetes mellitus, valvular heart disease, larger atrial size, and obesity, and this list keeps on growing.10-12 These underlying conditions can cause
structural changes of the myocardial tissue, as a consequence of hemodynamic and other pathophysiological processes, e.g. inflammation, setting the stage for AF occurrence and AF progression.14
For example, hypertension is known for its effects on atrial enlarge-ment, atrial dysfunction, fibrosis and conduction abnormalities.15-17 This is in line with
our results on hypertension and blood pressure, and echocardiographic parameters such as atrial dimensions and LV ejection fraction. Furthermore, in current literature there is extensive knowledge on the relation between AF and heart failure, with both heart failure with preserved and reduced ejection fraction being important AF risk factors due to me-chanical and hemodynamic factors as well as activation of neurohumoral and other path-ways causing structural (and electrical) remodeling18,19
. This is depicted in our analysis by significant differences in prevalence of heart failure at baseline and significant differences in LV dimensions, LV mass, septal and posterior wall thickness, and LV ejection fraction among the different AF patterns.
af pattern and af progression
In addition to the presence of underlying comorbidities, AF itself and its episode duration, i.e. AF pattern, also plays an important role in the occurrence and progression of AF. It is known that once AF is present the process of remodeling is enhanced due to electrophysi-ological and structural changes to the atria which promote perpetuation and subsequently
progression of AF.20-22 Several mechanisms are proposed to play an important role, such
as electrical remodeling due to changes in in atrial refractoriness and conduction, as a consequence of alteration in the expression of ion channels, shortening of the wavelength of the atrial impulse and changes in the duration of the action potential 23-28
, structural remodeling and contractile dysfunction as a consequence of changes in cellular metabo-lism and calcium handling. In common practice four different states of AF are used to communicate the persistence of AF, namely paroxysmal AF, persistent AF, longstanding persistent AF, and permanent AF. Not only are these different states a reflection of the underlying remodeling, the effect of therapeutic interventions is in a considerable degree related to the form of AF. This means that with progression of AF there will be increasing resistance in maintaining sinus rhythm and the effects of restoration of sinus rhythm will be limited, creating a vicious circle.29
We now further refined this stratification by dividing PAF into three different AF patterns. Patients with the longest AF episodes, i.e. the more severe AF pattern, had more AF progression than patients with shorter AF episodes.
identification of af severity as guidance for personalized therapy
In common practice four different types of AF are used to communicate the persistence of AF. This AF classification is often used to choose the appropriate therapy or treatment strategies for an individual patient.2
For example, a rhythm control strategy is almost always applied in PAF patients, whereas patients with permanent AF are usually treated with rate control.2,30 However, recent studies have shown us that the current clinical classification
poorly reflects the temporal persistence of AF when the classification is correlated to AF through monitoring by an implantable device.30
A possible explanation for this phenom-enon is that current AF classification is not taking the severity of the underlying substrate in consideration, nor the total AF burden.3 As the initiation and progression AF depends
on specific patient characteristics, such as the persistence of AF episodes and the presence and severity of underlying diseases, the question raises whether or not progression of AF is inevitable when tailored AF treatment is offered targeting patient specific risk factors.31-35
Our study may contribute to a further refinement of choice of therapy in the individual patient.
strengths and limitations
Important strengths of our analysis are the careful evaluation of included patients and a large population in which Holter monitoring was performed. Limitations are mainly the result of the observational study design, precluding conclusions on cause-effect relations. Other limitations are that the study population is relatively small and the number of Holter monitoring was very different between patients (range 1-8). Furthermore, detection of AF episodes in patients with long AF episodes that occur infrequently is challenging and might be missed.
56 Chapter 4
ConClusion
In our study significant differences in clinical characteristics were seen between patients with dissimilar AF patterns on Holter monitoring. Furthermore, progression rates varied according to the AF patterns on Holter monitoring. Implementing Holter AF patterns in risk prediction scores for AF progression may improve choice of treatment in the individual patient.
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