European Heart Journal_ Acute Cardiovascular Care-2013-Almendro-Delia-2048872613517370
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Prognostic impact of atrial fibrillation in i n acute coronary syndromes: results from the ARIAM registry Manuel Almendro-Delia, Almendro-Delia, María José Valle-Caballero, Juan C Garcia-Rubira, Garcia-Rubira, Blanca Muñoz-Calero, Muñoz-Calero, Angel Garcia-Alcantara, Garcia-Alcantara, Antonio Reina-Toral, José Benítez-Parejo, Rafael Hidalgo-Urbano Hidalgo-Urbano and on behalf of the t he ARIAM Andalucia Study Group European Heart Journal: Acute Cardiovascular Cardiovascular Care published online 17 December 2013 DOI: 10.1177/204887261351 10.1177/204887261351737 7370 0 The online version of this article can be found at: http://acc.sagepub.com/content/earl http://acc.sagepub .com/content/early/2013/12/16/20 y/2013/12/16/204887261351 48872613517370 7370
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ACC0010.1177/2048872613517370Eur opeanHeartJournal: AcuteCardiovascularCareAlmendro-Deliaetal.
EUROPEAN SOCIETY OF CARDIOLOGY ®
Original scientific paper
European Heart Journal: Acute Cardiovascular Care 201X, Vol XX(X) 1 –8 © The European Society of Cardiology 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/2048872613517370 acc.sagepub.com
Prognostic impact of atrial fibrillation in acute coronary syndromes: results from the ARIAM registry
Manuel Almendro-Delia 1,*, María José Valle-Caballero 1,*, Juan C Garcia-Rubira 1, Blanca Muñoz-Calero 1, Angel Garcia-Alcantara 2, Antonio Reina-Toral 3, José Benítez-Parejo4 and Rafael HidalgoUrbano1; on behalf of the ARIAM Andalucia Study Group
Abstract Aims: The prognostic ability of atrial fibrillation (AF) in acute coronary syndromes (ACS) is unclear. Studies regarding patient outcomes with respect to the timing of AF are scarce and conflicting. The present study aimed to determine the frequency, predictors and impact on clinical outcome of AF in patients with ACS. Methods: We analysed 39,237 consecutive patients with ACS included in the ARIAM registry between January /2001 a nd December 2011. Patients with AF were compared with patients in sinus rhythm. We differentiate between new-onset AF and previous AF cases to analyse mortality and other major adverse cardiac events (MACE) during hospitalization. Results: Of the patients, 2851 (7.3%) developed AF; 1568 (55%) of these were new-onset AF and 1283 (45%) had previous AF. The AF group had a higher risk profile at baseline and poorer clinical presentation at admission than non-AF patients. Compared with previous AF patients, new-onset AF presented with fewer comorbidities, including hypertension, diabetes, prior myocardial infarction, and chronic renal impairment. The inhospital mortality for newonset AF, previous AF, and non-AF patients were 14, 11.6, and 5.2%, respectively (new-onset AF unadjusted HR 2.19, 95% CI 1.9–2.53, p<0.001; adjusted HR 1.70, 95% CI 1.12–3.4, p<0.001). After propensity score analysis, only new-onset AF persisted as an independent predictor for mortality (HR 1.62, 95% CI 1.09–2.89, p<0.001). Other MACE such as reinfarction, malignant arrhythmias, and heart failure were also more frequent in new-onset AF patients than in previous AF or non-AF patients. Conclusions: These findings suggest that the presence of new-onset AF during ACS is associated with a significant increase in mortality, even after adjusting for confounding variables. Keywords Acute coronary syndrome, atrial fibrillation, heart failure, myocardial infarction, propensity score, registry Received: 12 September 2013; accepted: 28 November 2013
Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia observed in clinical practice and a frequent complication of acute coronary syndromes (ACS), with an incidence ranging between 5 and 23%. 1–13 The prognostic implication of AF in the ACS setting is unclear. Some studies have found an association between AF and increased morbidity and mortality, 6,7,9,14–19 whereas other studies have failed to detect this association. 1,8,20 The
1Virgen
Macarena University Hospital, Seville, Spain. de la Victoria Hospital, Málaga, Spain. 3Virgen de las Nieves Hospital, Granada, Spain. 4Coresoft, Málaga, Spain. *These authors contributed equally to this paper. 2Virgen
Corresponding author: Manuel Almendro-Delia, Coronary Care Unit. Cardiology Department, Virgen Macarena University Hospital, Avd Dr. Fedriani 3, 41071, Seville, Spain. Email: [email protected]
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majority of these findings were derived from studies using unadjusted multivariate models 2–10 or post-hoc analyses of randomized control trials; 6–7,9,14,15,17,21 only few studies differentiated between previous (PrAF) and new-onset AF (NAF).14–25 AF is frequently associated with heart failure (HF) during ACS. 11–13,26 Thus, AF may be considered a surrogate marker of HF that is associated with increased left ventricle filling pressure. 11,13,27–29 Current clinical guidelines for treatment of AF during an ACS provide recommendations based on limited evidence. 29,30 The efficacy of long-term antithrombotic and antiarrhythmic drugs is unclear, especially in NAF that is resolved at discharge. This study aims to determine the incidence of AF in ACS patients, identify risk factors for its development, and determine if AF during ACS is an independent risk factor for inhospital mortality.
Variables
ST-segment elevation myocardial infarction (STEMI) was defined based on the following criteria: acute ischaemic symptoms and ST-segment elevation ≥2 mV in two contiguous precordial leads as well as ≥1 mV in inferior leads or a presumed/new left bundle branch block on initial electrocardiogram. Non-ST-segment elevation myocardial infarction (NSTEMI) was defined as the elevation of cardiac enzymes above the upper limit of normal in patients with ischaemic symptoms but without persistent ST segment elevation or new left bundle branch block. Unstable angina was defined as the presence of symptoms of angina at rest, new-onset angina, or accelerated ischaemic symptoms with or without electrocardiographic changes but without elevation of cardiac enzymes. Stroke was defined as the occurrence of a new neurological deficit caused by an ischaemic event with residual symptoms lasting for at least 24 h after the onset of the ischaemic event. Cardiogenic shock was defined as having clinical signs of pulmonary congestion and impaired end-organ perfusion with persisMethods tent hypotension defined as a systolic blood pressure less ARIAM registry than 90 mmHg for more than 30 min or the need for vasoThe ‘ARIAM Andalucia’ (Análisis del Retraso en el pressor therapy to maintain a systolic pressure above 90 Infarto Agudo de Miocardio/Analysis of Delay in Acute mmHg. Major and minor bleeding were defined according Myocardial Infarction) registry is a multicentre project to the TIMI definitions. 33 involving 44 medical centres with the goal of improving the quality of treatment for patients with ischaemic heart Statistical analysis disease in Spain. The details of the registry have been previously published.31,32 In brief, data from patients admit- Data are expressed as the mean±standard deviation for ted to coronary care units in Andalucia, Spain with the quantitative normally distributed variables, median (interdiagnosis of ACS were collected from January 2001 to quartile range), or n (%). The differences between categoriDecember 2011. Case inclusion was prospective, although cal variables were compared using Chi-squared or Fisher data interpretation and the development of the study were exact tests and two-tailed Student t-test or Mann–Whitney retrospective. The categorization and identification of U-test for continuous variables, as appropriate. Stepwise events and clinical variables were determined by individ- logistic regression was used to assess AF predictors. ual investigators using the protocol definitions. The study A Cox proportional-hazards model was used to assess and this article followed the ethics and privacy guidelines predictors for inhospital mortality using the hazard ratio of the independent local ethics and research committees (HR) with a 95% confidence interval (CI). Parsimonious related to the ARIAM project. The approval of ARIAM models were developed in a forward manner using prede primary investigators was obtained, and all of the patients fined covariates. The model was adjusted to account for the gave informed consent. presence of malignant arrhythmias (either ventricular fibrillation (VF) and/or sustained ventricular tachycardia (SVT)) or cardiogenic shock, the two variables with the highest Study group statistical weight for hospital mortality as determined by The study group consisted of patients with ACS who devel- univariate analysis. The model was also adjusted for baseoped AF during hospitalization. AF was defined as an irreg- line characteristics related to mortality by univariate analyular rhythm with the absence of discrete atrial activation sis ( p<0.2), including age, diabetes mellitus, blood pressure, (atrial fibrillation or flutter) recorded in a 12-lead ECG. 29 renal impairment (creatinine clearance), previous myocarWe differentiated between those patients with previous dial infarction (MI), heart failure (Killip class), GRACE clear documentation or records of either paroxysmal, per- risk score, mechanical complications of MI, and myocarsistent, or permanent AF (PrAF), and those with new-onset dial revascularization. Kaplan–Meier curves were also perAF (NAF), which was defined as the absence of medical formed to examine outcomes based on AF status. records of AF but the development of arrhythmia, either at The propensity score model (PS) for the appearance of admission or during hospitalization. AF was obtained by logistic regression using baseline
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Almendro-Delia et al. Table 1. Baseline characteristics.
Characteristic
Non AF (n=36,386)
Female 9305 (25.6) Age (years) 63.8±12.6 Smoking 12,661 (35.6) BMI ≥30 kg/m2) 3928 (11) Diabetes 11,397 (32) Hypertension 18,643 (52.5) Previous MI 6355 (18) Prior CHF 1508 (4.3) Prior stroke 1903 (5.4) AF history 1557 (4.4) Peripheral arterial disease 1759 (5) Renal impairment (CrCl 494 (1.4) <60 ml/min) Previous PCI 3079 (10) Prior CV surgery 909 (2.9) Clinical presentation at admission VF 504 (1.5) SVT 1170 (3.4) 3-grade AV block 1039 (3) Systolic BP (mmHg) 117±26 Heart rate (bpm) 84±17 GRACE risk score 130 (108–155) LVEF (%) 50.6±13 Cardiac troponina 10,295 (59) STEMI 22,004 (60.5) Anterior MI 10,454 (39.6) Killip class (%) I 80 II 12 III 5 IV 3
AF (n=2851)
p
Previous AF (n=1283)
New AF (n=1568)
p
986 (34.6) 71.2±9.8 546 (1.7) 318 (11.5) 1097 (39.5) 1836 (66) 553 (20) 285 (10.4) 275 (10) 809 (29.4) 226 (8.2) 122 (4.4)
<0.001 <0.001 <0.001 0.520 <0.001 <0.001 0.01 <0.001 <0.001 <0.001 <0.001 <0.001
423 (34) 71±10 225 (19) 192 (16) 477 (40) 842 (70.5) 239 (20.3) 141 (12) 139 (12) 1283 (100) 96 (8.1) 94 (8)
529 (35) 71±9.6 311 (21) 124 (8.4) 575 (39) 919 (62.3) 272 (18.5) 127 (8.7) 128 (8.7) – 121 (8.2) 28 (1.9)
0.533 0.634 0.150 <0.001 0.614 <0.001 0.285 0.005 0.009 <0.001 0.927 <0.001
231 (9.3) 133 (5.4)
0.31 <0.001
139 (12) 75 (6.6)
82 (6.6) 47 (3.8)
<0.001 0.002
0.007 <0.001 0.55 0.13 <0.001 <0.001 <0.001 <0.001 <0.001 0.312
38 (3) 23 (2) 20 (2.3) 130±32 93±31 160 (136–182) 45.3±14 293 (31.2) 540 (43.5) 326 (35)
58 (2.1) 445 (16.2) 88 (3.2) 118±28 94±26 161 (136–181) 44.2±14 1009 (70.8) 1485 (52) 805 (38.5) 52 25 14 9
<0.001
55 27 12 6
20 (1.3) 422 (28) 60 (4) 108±19 95±19 162 (138–183) 43±14 196 (40) 945 (63) 444 (41) 50 24 16 10
<0.001 <0.001 0.01 <0.001 0.014 0.432 0.029 <0.001 <0.001 0.008 <0.001
Data are mean±standard deviation, n (%), or median (interquartile range). Categories do not add up to 100% for all variables due to missing values. aCardiac troponin levels >5 times the upper limit of normal. Data from 20,228 patients with available and valid cardiac troponin values AF, atrial fibrillation; AV, atrioventricular; BMI, body mass index; BP, blood pressure; CHF, chronic heart failure; CrCl, creatinine clearance; CV surgery, cardiovascular surgery; LVEF, left ventricular ejection fraction; MI, myocardial infarction; PCI, percutaneous coronary intervention; RI, renal impairment; SVT, sustained ventricular tachycardia; VF, ventricular fibrillation.
clinical characteristics as covariates. 34 Nearest neighbour STEMI and 48% were NSTE-ACS, with 41% NSTEMI PS matching was used in terms of Mahalanobis distance and 7% unstable angina. (calliper 0.2). Goodness of fit was achieved if the standardized differences were ≤10%.35 A two-sided p-value <0.05 was considered statistically significant. The analyses were Clinical data performed using SPSS version 18.0 (SPSS, Chicago, IL, The baseline characteristics are shown in Table 1. AF USA) and R version 2.10.0 (The R Foundation for Statistical patients were more likely to be female, older, and sicker Computing). than those with sinus rhythm. At the time of admission, the AF group had a higher incidence of malignant arrhythmias (VF/SVT) and higher heart rates and plasma cardiac troResults ponin levels than non-AF. Killip class at admission was This study included 39,237 patients. Among these, 2851 worse and the left ventricle ejection fraction (LVEF) was patients (7.3%) developed AF during hospitalization. Of lower in AF patients than in non-AF patients. Compared these, 1568 (55%) developed NAF and 1283 (45%) with the PrAF group, the NAF patients presented with fewer developed PrAF. Of all of the included patients, 52% comorbidities but showed a worse clinical presentation at
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Table 2. Treatments.
Non AF (n=36,386) Pharmacological treatment (at discharge) Aspirin 27,162 (78) Clopidogrel 21,506 (61.7) ACE Inhibitors 21,579 (67.5) Beta-blockers 21,738 (68) Digoxin 87 (0.3) Diuretics 2545 (8) GP inhibitorsa 7139 (26) Inotropesa 2071 (5.9) b Aldosterone antagonist 554/4395 (12.6) Statinsb 2349/4395 (53.4) Vitamin K antagonistsb 86/4395 (2) c Reperfusion treatment Thrombolysis 12,711 (57.8) pPCI 2607 (11.8) No reperfusion 6691 (30.4) PCI during hospitalization 15,092 (68.6)
AF (n=2851)
p
Previous AF (n=1283)
New AF (n=1 568)
p
2176 (78.8) 1753 (63.5) 1600 (65.5) 1273 (52.2) 131 (5.4) 566 (23.2) 449 (19.3) 512 (18.5) 88/433 (18.5) 209/433 (48.3) 86/433 (20)
0.26 0.06 0.05 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.039 <0.001
1141 (93) 1021 (84) 589 (48) 545 (45) 73 (6) 410 (41) 147 (15) 202 (16.5) 59 (12) 885 (84) 67 (25)
967 (67.2) 694 (48.2) 736 (51) 589 (41) 10 (1) 564 (45) 275 (22) 297 (21) 297 (21) 761 (59) 8 (9.2)
<0.001 <0.001 0.134 0.05 <0.001 0.05 <0.001 0.007 0.016 <0.001 0.002
804 (54.1) 194 (13.1) 487 (32.8) 975 (65.7)
0.006 0.16 0.04 0.02
239 (44) 112 (21) 189 (35) 351 (65)
565 (60) 82 (9) 298 (31) 624 (66)
<0.001 <0.001 0.002 0.687
Data are n (%). Categories do not add up to 100% for all variables due to missing values. aDrugs used in the acute stage. bData from 4828 patients. cData from 20,403 patients with STEMI diagnosis. ACE, angiotensin-converting enzyme; AF, atrial fibrillati on; GP, glycoprotein; pPCI, primary percutaneous coronary intervention; STEMI, ST-elevation myocardial infarction.
admission with a greater proportion of patients with STEMI and anterior MI, a higher Killip class, and cardiac troponin levels and lower LVEF, together with more ventricular arrhythmias.
Pharmacological and reperfusion treatment Table 2 summarizes the pharmacological treatment and revascularization strategies during hospitalization. In general, the AF group was less likely to be treated with evidence-based therapies. Thrombolysis was applied less frequently to AF patients diagnosed with STEMI than to non-AF STEMI patients. During hospitalization, AF patients as a group received less reperfusion, although in the STEMI cohort, more AF patients received primary percutaneous coronary intervention (pPCI) than the nonAF STEMI patients. Compared with PrAF patients, the NAF group was less likely to receive antiplatelet agents, statins, vitamin K antagonist, beta-blockers, and pPCI. Thrombolysis was more frequently performed in the NAF group than in the PrAF group, but NAF and PrAF patients underwent similar numbers of PCI procedures during hospitalization.
Figure 1. Predictors of atrial fibrillation. *, Every ten years increased; †, Every 5 bpm increased; ‡, Every 5 points increased. AF, atrial fibrillation; HR, heart rate; LVEF, left ventricular ejection fraction. C-statistic: 0.79 (0.72-0.86 95% CI; p<0.0001).
admission, and heart failure, whereas a better ejection fraction behaved as a protective factor (Figure 1).
Hospital outcomes
AF patients had a poorer prognosis, with a higher incidence of malignant arrhythmias, reinfarction, ischaemic mitral regurgitation, heart failure, cardiogenic shock, and inhospital mortality than non-AF patients. NAF patients had a poorer outcome than PrAF patients (Table 3). There was no difference in bleeding between AF and non-AF patients. Predictors of AF Table 4 summarizes the impact of AF on hospital mortality. The predictor variables for AF development included age, In the unadjusted analysis, both PrAF and NAF patients had being female, prior history of AF, elevated heart rate at an approximately 2-fold increase in mortality compared with
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Almendro-Delia et al. Table 3. Differences in outcomes in patients with and without atrial fibrillation during hospitalization.
Ventricular fibrillation SVT AV block Asystolia Reinfarction Cardiac rupture VSR Ischaemic MR Stroke HF (KC ≥2) Cardiogenic shock Total bleeding (major and minor) Total inhospital mortality CCU stay (days) Hospital stay (days)
Non AF (n=36,386)
AF (n=2851)
p
Previous AF (n=1283)
New AF (n=1568)
p
1165 (3.2) 1994 (5.5) 2119 (5.8) 542 (1.5) 532 (1.9) 93 (0.3) 45 (0.2) 67 (0.2) 99 (2.4) 8174 (22.6) 1473 (4.1) 587 (14.5) 1881(5.2) 1 (1–2) 2 (1–6)
147 (5.2) 565 (19.8) 259 (9.1) 99(3.5) 62 (2.2) 13 (0.5) 4 (0.1) 16 (0.6) 21 (2.9) 1590 (56) 396 (14) 90 (13) 365 (12.8) 2 (1–3) 3 (1–7)
<0.001 <0.001 0.02 <0.001 0.003 0.22 0.85 0.001 0.44 <0.001 <0.001 0.196 <0.001 <0.001 <0.001
51 (4) 62 (5) 19 (1.5) 40 (32) 18 (1.6) 5 (0.4) 1 (0.1) 8 (0.7) 6 (2.6) 648 (52) 156 (12,6) 32 (14) 144 (11.6) 1 (1–2) 3 (1–7)
95 (6.3) 500 (33.3) 103 (7) 55 (35) 44 (2.9) 8 (0.5) 3 (0.2) 8 (0.5) 14 (3) 881 (59) 231 (15.4) 35 (12) 210 (14) 1 (1–4) 4 (2–7)
0.01 <0.001 <0.001 0.531 0.009 0.750 0.472 0.558 0.765 0.001 0.035 0.434 0.001 <0.001 0.001
Data are n (%)or median (interquartile range). Categories do not add up to 100% for all variables due to missing values. AF, atrial fibrilation; AV, atrioventricular; CCU, coronary care unit; HF, heart failure; KC, Ki llip class; MR, mitral regurgitation; SVT, sustained ventricular tachycardia; VSR, ventricular septal rupture.
Table 4. Predictors of mortality: multivariate analysis.
Unadjusted
PrAF NAF
Adjusted
PS cohort
HR
p
HR
p
HR
p
1.89 (1.6–2.4) 2.19 (1.9–2.53)
<0.001 <0.001
– 1.70 (1.12–3.40)
– <0.001
– 1.62 (1.09–2.89)
– <0.001
Values are hazard ratios (95% confidence interval). NAF, new atrial fibrillation; PrAF, previous atrial fibrillation; PS, propensity score.
non-AF patients; the increase was slightly more pronounced our findings. Schmitt et al. concluded that AF during MI for NAF patients (Supplementary Table S1, available online). implied worse short- and long-term outcomes, independent After adjustment, NAF but not PrAF remained an independent of MI treatment strategies. Two well-conducted meta-anal predictor of mortality (Figure 2 and Supplementary Table S2). yses focusing on the prognostic implication of AF during By using PS matching, 1908 pairs of AF and non-AF patients MI have been published. The first one, by Jabre et al., 12 were matched with both groups balanced with respect to base- included 43 articles with a total of 278,854 MI patients. line characteristics (Supplementary Table S3). After matching, Their analysis indicated that AF led to a 40% increase in the only NAF remained as an independent predictor of inhospital risk of mortality in the setting of MI, even after adjustment mortality, with a slight reduction in HR (Table 4 and for the same variables as in our study, and concluded that Supplementary Table S4). NAF and PrAF did not differ in mortality risk. The second and most recent study, by Angeli et al., 13 was a meta-analysis of 24 studies comparing outcomes in patients with MI Discussion and sinus rhythm with those observed for AF patients. In We have demonstrated an increased risk of mortality asso- their study, patients with AF had a 2-fold increased risk of ciated with the presence of AF in the setting of ACS. NAF inhospital mortality and patients with clear evidence of is associated with an almost 70% increase in mortality risk. NAF had a 3-fold higher risk of mortality than sinus rhythm This increased risk was not demonstrated for PrAF after a patients, although the study did not adjust for the Killip two well-conducted adjustment analyses. The results of class, an important confounding variable. 15,26–28 previous studies are conflicting regarding the prognostic It is important to note that in the meta-analysis by Jabre 1–6,8,9 impact of AF types in MI. However, our results are et al.,12 the association between AF and mortality was simconsistent with some previous reports. 2–13 ilar for NAF and PrAF. These results persisted even after The predictors and significance of AF in the setting of adjusting for confounding variables such as heart failure MI reviewed by Schmitt et al. 11 are fairly consistent with and revascularization, although only four of the 43 studies Downloaded from acc.sagepub.com at HOSPITAL VIRGEN DE LA VICTORI on February 13, 2014
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Figure 2. Kaplan Meier survival curves for inhospital mortality.
included in his work performed such important adjust- consumption, loss of atrial contraction, and AV synchrony, ments. These results are consistent with ours with respect which lead to a decrease in effective cardiac output. 7,9,12–15,39 to risk ratios for NAF patients but differ in relation to PrAF Secondly, irregular RR intervals can develop malignant patients. To understand these conflicting results, we per- arrhythmias, as was observed in our cohort. 12–15,40 However, formed a PS analysis, demonstrating that only NAF per- the mechanism of AF-related mortality in ACS remains elusists as an independent predictor of inhospital mortality. sive; the association of AF with higher heart rates, lower The association of PrAF with mortality found in the analy- LVEF and an increased incidence of HF suggests that sis of Jabre et al. 12 is most likely explained by the inclusion haemodynamic impairment is a potential explanation as of studies that analysed long-term mortality and not only well as increased adrenergic and neurohormonal inhospital outcomes, as we did in our study. NAF in the activation.9,12,13,21 setting of ACS reflects a greater ischaemic burden than The rate of cerebrovascular events in patients with any PrAF, as shown by the lower LVEF, larger infarct sizes, type of AF in our study was quite low and not significantly and higher Killip class associated with NAF compared different from patients in sinus rhythm. This may have been with PrAF in our study. In contrast, PrAF is associated due to the relatively young age of patients enrolled in our with a worse clinical baseline profile than NAF due to the ACS registry, but we cannot rule out other reasons. The existence of chronic heart disease. 9,36 Along the same line current AF guidelines do not provide a better therapeutic of reasoning, recent data from a large registry in Canada 37 approach for treating AF during an ACS. 29–30 However, has showed that patient with prior history of AF receive European Society of Cardiology STEMI guidelines clearly less evidence-based therapies and inhospital coronary recommend optimized triple antithrombotic therapy for angiography compared to non-AF patients, similarly to our patients with a clear indication of oral anticoagulation. 31 results. As in our observational study, patients with prior The use of oral anticoagulants was quite low in our study, AF were older and sicker, 37 and broadly differ to those below 30%, clearly not in line with clinical guidelines. 29–30 included in the work by Lopes et al. 38 that examined pooled This finding may be due to the use of dual antiplatelet therdata from 10 ACS trials, which includes more cases of apy, thereby avoiding the risk of bleeding associated with NAF and STEMI and probably less patients with signifi- triple therapy. Nonetheless, our data are consistent with cant comorbidities or those receiving warfarin, as is com- those of a Swedish registry (RIKS-HIA), in which longmon in patients included in clinical trials compared with term oral anticoagulation therapy was only given to 30% of those derived from registries. the patients with AF during an acute MI. 41 Likewise, statin The development of AF may worsen the prognosis of use was quite low in our study. Danchin et al. 42 have demACS due to several factors. First, AF triggers some adverse onstrated that early statin therapy reduces the incidence of haemodynamic effects, including increased oxygen AF during MI; thus, the mortality risk associated with AF
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in our study may be greater than expected due to the lack of early use of statin. Considering our results, there is sufficient evidence to consider AF a mortality risk factor in the course of ACS.
Study limitations
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. However, the Health Council of the Junta de Andalucia gives support to the virtual platform of the Registry. References
Several limitations of the present investigation deserve discussion. First, the study population were derived from an ACS-specific registry, not from an AF-specific registry. The results should be considered only as hypothesis generating, and we cannot exclude the influence of other noncontrolled confounders in our conclusions. There is also selection bias because we included only patients admitted to coronary care units. Secondly, data concerning the temporal relation between the onset of AF and the appearance of complications were not available, so we can establish an association but not a causality relationship. Silent episodes of AF could not be analysed in the sinus rhythm group. Furthermore, the adjustment of variables that were not measured at baseline (e.g. mechanical complications of MI and revascularization) in multivariable analysis, and comparison of outcomes which might occur before or after AF (since its timing was not known), should be interpreted with caution. Thirdly, data concerning the causes of death were only available for a minority of patients; thus, the causes of death are not shown. Data concerning treatment strategies used for AF as well as the timing, duration, and final outcome of AF episodes were not assessed. Although rates of reperfusion were low in our registry, the mortality numbers were similar to that reported in the GRACE registry.18 In the final adjusted model, three-vessel disease was not finally included, because in our study coronary angiography rates was low and this might influence the results.
Conclusions AF is not an infrequent event during ACS. The presence of NAF is associated with nearly an 70% mortality increase in MI patients. Our results suggest that the appearance of NAF during ACS should not be considered an isolated benign event but a severe complication with prognosis implications. These results are hypothesis generating and the prognostic impact of treatment strategies for NAF in MI patients must be assessed in future studies. ARIAM Andalucia Study Group
A list of investigators is given in the Appendix available online. Conflict of interest
The authors declare that there is no conflict of interest.
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15. Eldar M, Canetti M, Rotstein Z, et al. Significance of paroxysmal atrial fibrillation complicating acute myocardial infarction in the thrombolytic era: SPRINT and Thrombolytic Survey Groups. Circulation 1998; 97: 965–970. 16. Lehto M, Snapinn S, Dickstein K, et al. Prognostic risk of atrial fibrillation in acute myocardial infarction complicated by left ventricular dysfunction: the OPTIMAAL experience. Eur Heart J 2005; 26: 350–356. 17. Wong CK, White HD, Wilcox RG, et al. New atrial fibrillation after acute myocardial infarction independently predicts death: the GUSTO-III experience. Am Heart J 2000; 140: 878–885. 18. Mehta RH, Dabbous OH, Granger CB, et al. Comparison of outcomes of patients with acute coronary syndromes with and without atrial fibrillation. Am J Cardiol 2003; 92: 1031– 1036. 19. Berton G, Cordiano R, Cucchini F, et al. Atrial fibrillation during acute myocardial infarction: association with allcause mortality and sudden death after 7-year of follow-up. Int J Clin Pract 2009; 63: 712–721. 20. Asanin M, Perunicic J, Mrdovic I, et al. Prognostic significance of new atrial fibrillation and its relation to heart failure following acute myocardial infarction. Eur J Heart Fail 2005; 7: 671–676. 21. Pedersen OD, Bagger H, Kober L, et al. The occurrence and prognostic significance of atrial fibrillation/-flutter following acute myocardial infarction: TRACE Study Group: TRAndolapril Cardiac Evaluation. Eur Heart J 1999; 20: 748–754. 22. Saczynski JS, McManus D, Zhou Z, et al. Trends in atrial fibrillation complicating acute myocardial infarction. Am J Cardiol 2009; 104: 169–174. 23. Trappolini M, Scorza A, Chillotti FM, et al. Prognostic significance of atrial fibrillation in thrombolysed and non thrombolysed patients. Minerva Cardioangiol 2006; 54: 471–479. 24. Poçi D, Hartford M, Kalrsson T, et al. Effect of new versus known versus no atrial fibrillation on 30-day and 10-year mortality in patients with acute coronary syndrome. Am J Cardiol 2012; 110: 217–221. 25. Lau DH, Huynh LT, Chew DP, et al. Prognostic impact of types of atrial fibrillation in acute coronary syndromes. Am J Cardiol 2009; 104: 1317–1323. 26. Cha YM, Redfield MM, Shen WK, et al. Atrial fibrillation and ventricular dysfunction: a vicious electromechanical cycle. Circulation 2004; 109: 2839–2843. 27. Ehrlich JR, Nattel S and Hohnloser SH. Atrial fibrillation and congestive heart failure: specific considerations at the intersection of two common and important cardiac disease sets. J Cardiovasc Electrophysiol 2002; 13: 399–405. 28. Wang TJ, Larson MG, Levy D, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation 2003; 107: 2920–2925.
29. Camm AJ, Kirchhof P, Lip GYH, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010; 31: 2369–2429. 30. Steg PG, James SK, Atar D, et al. ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J 2012; 33: 2569–2619. 31. Álvarez Bueno M, Vera Almazán A, Rodríguez García JJ, et al. Concept, development and objectives of project ARIAM [in Spanish]. Med Intensiva 1999; 23: 273–280. 32. Saturno PJ, Felices F, Segura J, et al. Reducing time delay in the thrombolysis of myocardial infarction: an internal quality improvement project. Am J Med Quality 2000; 15: 85–93. 33. Chesebro JH, Knatterud G, Roberts R, et al. Thrombolysis in Myocardial Infarction (TIMI) Trial, Phase I: a comparison between intravenous tissue plasminogen activator and intravenous streptokinase. Clinical findings through hospital discharge. Circulation 1987; 76: 142–154. 34. Rubin DB. Estimating causal effects from large data sets using propensity scores, Ann Intern Med 1997; 127: 757–763. 35. D’Agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998; 17: 2265–2281. 36. Corradi D, Callegari S, Maestri R, et al. Structural remodeling in atrial fibrillation. Nat Clin Pract Cardiovasc Med 2008; 5: 782–796. 37. Al Khdair D, Alshengeiti L, Elbarouni B, et al.; Global Registry of Acute Coronary Events (GRACE/GRACE2) and the Canadian Registry of Coronary Events (CANRACE) Investigators. Management and outcome of acute coronary syndrome patients in relation to prior history of atrial fibrillation. Can J Cardiol 2012; 28: 443–449. 38. Lopes RD, Pieper KS, Horton JR, et al. Short- and long-term outcomes following atrial fibrillation in patients with acute coronary syndromes with or without ST-segment elevation. Heart 2008; 94: 867–873. 39. Waldecker B. Atrial fibrillation in myocardial infarction complicated by heart failure: cause or consequence?. Eur Heart J 1999; 20: 710–712. 40. Sankaranarayanan R, James MA, Nuta B, et al. Does atrial fibrillation beget ventricular fibrillation in patients with acute myocardial infarction? Pacing Clin Electrophysiol 2008; 31: 1612–1619. 41. Stenestrand U, Lindback J and Wallentin L. Anticoagulation therapy in atrial fibrillation in combination with acute myocardial infarction influences long-term outcome: a prospective cohort study from the Register of Information and Knowledge About Swedish Heart Intensive Care Admissions (RIKS-HIA) Circulation 2005; 112: 3225–3231. 42. Danchin N, Fauchier L, Marijon E, et al. Impact of early statin therapy on development of atrial fibrillation at the acute stage of myocardial infarction: data from the FAST-MI register. Heart 2010; 96: 1809–1814.
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Prognostic impact of atrial fibrillation in i n acute coronary syndromes: results from the ARIAM registry Manuel Almendro-Delia, Almendro-Delia, María José Valle-Caballero, Juan C Garcia-Rubira, Garcia-Rubira, Blanca Muñoz-Calero, Muñoz-Calero, Angel Garcia-Alcantara, Garcia-Alcantara, Antonio Reina-Toral, José Benítez-Parejo, Rafael Hidalgo-Urbano Hidalgo-Urbano and on behalf of the t he ARIAM Andalucia Study Group European Heart Journal: Acute Cardiovascular Cardiovascular Care published online 17 December 2013 DOI: 10.1177/204887261351 10.1177/204887261351737 7370 0 The online version of this article can be found at: http://acc.sagepub.com/content/earl http://acc.sagepub .com/content/early/2013/12/16/20 y/2013/12/16/204887261351 48872613517370 7370
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research-article 2013
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ACC0010.1177/2048872613517370Eur opeanHeartJournal: AcuteCardiovascularCareAlmendro-Deliaetal.
EUROPEAN SOCIETY OF CARDIOLOGY ®
Original scientific paper
European Heart Journal: Acute Cardiovascular Care 201X, Vol XX(X) 1 –8 © The European Society of Cardiology 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/2048872613517370 acc.sagepub.com
Prognostic impact of atrial fibrillation in acute coronary syndromes: results from the ARIAM registry
Manuel Almendro-Delia 1,*, María José Valle-Caballero 1,*, Juan C Garcia-Rubira 1, Blanca Muñoz-Calero 1, Angel Garcia-Alcantara 2, Antonio Reina-Toral 3, José Benítez-Parejo4 and Rafael HidalgoUrbano1; on behalf of the ARIAM Andalucia Study Group
Abstract Aims: The prognostic ability of atrial fibrillation (AF) in acute coronary syndromes (ACS) is unclear. Studies regarding patient outcomes with respect to the timing of AF are scarce and conflicting. The present study aimed to determine the frequency, predictors and impact on clinical outcome of AF in patients with ACS. Methods: We analysed 39,237 consecutive patients with ACS included in the ARIAM registry between January /2001 a nd December 2011. Patients with AF were compared with patients in sinus rhythm. We differentiate between new-onset AF and previous AF cases to analyse mortality and other major adverse cardiac events (MACE) during hospitalization. Results: Of the patients, 2851 (7.3%) developed AF; 1568 (55%) of these were new-onset AF and 1283 (45%) had previous AF. The AF group had a higher risk profile at baseline and poorer clinical presentation at admission than non-AF patients. Compared with previous AF patients, new-onset AF presented with fewer comorbidities, including hypertension, diabetes, prior myocardial infarction, and chronic renal impairment. The inhospital mortality for newonset AF, previous AF, and non-AF patients were 14, 11.6, and 5.2%, respectively (new-onset AF unadjusted HR 2.19, 95% CI 1.9–2.53, p<0.001; adjusted HR 1.70, 95% CI 1.12–3.4, p<0.001). After propensity score analysis, only new-onset AF persisted as an independent predictor for mortality (HR 1.62, 95% CI 1.09–2.89, p<0.001). Other MACE such as reinfarction, malignant arrhythmias, and heart failure were also more frequent in new-onset AF patients than in previous AF or non-AF patients. Conclusions: These findings suggest that the presence of new-onset AF during ACS is associated with a significant increase in mortality, even after adjusting for confounding variables. Keywords Acute coronary syndrome, atrial fibrillation, heart failure, myocardial infarction, propensity score, registry Received: 12 September 2013; accepted: 28 November 2013
Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia observed in clinical practice and a frequent complication of acute coronary syndromes (ACS), with an incidence ranging between 5 and 23%. 1–13 The prognostic implication of AF in the ACS setting is unclear. Some studies have found an association between AF and increased morbidity and mortality, 6,7,9,14–19 whereas other studies have failed to detect this association. 1,8,20 The
1Virgen
Macarena University Hospital, Seville, Spain. de la Victoria Hospital, Málaga, Spain. 3Virgen de las Nieves Hospital, Granada, Spain. 4Coresoft, Málaga, Spain. *These authors contributed equally to this paper. 2Virgen
Corresponding author: Manuel Almendro-Delia, Coronary Care Unit. Cardiology Department, Virgen Macarena University Hospital, Avd Dr. Fedriani 3, 41071, Seville, Spain. Email: [email protected]
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majority of these findings were derived from studies using unadjusted multivariate models 2–10 or post-hoc analyses of randomized control trials; 6–7,9,14,15,17,21 only few studies differentiated between previous (PrAF) and new-onset AF (NAF).14–25 AF is frequently associated with heart failure (HF) during ACS. 11–13,26 Thus, AF may be considered a surrogate marker of HF that is associated with increased left ventricle filling pressure. 11,13,27–29 Current clinical guidelines for treatment of AF during an ACS provide recommendations based on limited evidence. 29,30 The efficacy of long-term antithrombotic and antiarrhythmic drugs is unclear, especially in NAF that is resolved at discharge. This study aims to determine the incidence of AF in ACS patients, identify risk factors for its development, and determine if AF during ACS is an independent risk factor for inhospital mortality.
Variables
ST-segment elevation myocardial infarction (STEMI) was defined based on the following criteria: acute ischaemic symptoms and ST-segment elevation ≥2 mV in two contiguous precordial leads as well as ≥1 mV in inferior leads or a presumed/new left bundle branch block on initial electrocardiogram. Non-ST-segment elevation myocardial infarction (NSTEMI) was defined as the elevation of cardiac enzymes above the upper limit of normal in patients with ischaemic symptoms but without persistent ST segment elevation or new left bundle branch block. Unstable angina was defined as the presence of symptoms of angina at rest, new-onset angina, or accelerated ischaemic symptoms with or without electrocardiographic changes but without elevation of cardiac enzymes. Stroke was defined as the occurrence of a new neurological deficit caused by an ischaemic event with residual symptoms lasting for at least 24 h after the onset of the ischaemic event. Cardiogenic shock was defined as having clinical signs of pulmonary congestion and impaired end-organ perfusion with persisMethods tent hypotension defined as a systolic blood pressure less ARIAM registry than 90 mmHg for more than 30 min or the need for vasoThe ‘ARIAM Andalucia’ (Análisis del Retraso en el pressor therapy to maintain a systolic pressure above 90 Infarto Agudo de Miocardio/Analysis of Delay in Acute mmHg. Major and minor bleeding were defined according Myocardial Infarction) registry is a multicentre project to the TIMI definitions. 33 involving 44 medical centres with the goal of improving the quality of treatment for patients with ischaemic heart Statistical analysis disease in Spain. The details of the registry have been previously published.31,32 In brief, data from patients admit- Data are expressed as the mean±standard deviation for ted to coronary care units in Andalucia, Spain with the quantitative normally distributed variables, median (interdiagnosis of ACS were collected from January 2001 to quartile range), or n (%). The differences between categoriDecember 2011. Case inclusion was prospective, although cal variables were compared using Chi-squared or Fisher data interpretation and the development of the study were exact tests and two-tailed Student t-test or Mann–Whitney retrospective. The categorization and identification of U-test for continuous variables, as appropriate. Stepwise events and clinical variables were determined by individ- logistic regression was used to assess AF predictors. ual investigators using the protocol definitions. The study A Cox proportional-hazards model was used to assess and this article followed the ethics and privacy guidelines predictors for inhospital mortality using the hazard ratio of the independent local ethics and research committees (HR) with a 95% confidence interval (CI). Parsimonious related to the ARIAM project. The approval of ARIAM models were developed in a forward manner using prede primary investigators was obtained, and all of the patients fined covariates. The model was adjusted to account for the gave informed consent. presence of malignant arrhythmias (either ventricular fibrillation (VF) and/or sustained ventricular tachycardia (SVT)) or cardiogenic shock, the two variables with the highest Study group statistical weight for hospital mortality as determined by The study group consisted of patients with ACS who devel- univariate analysis. The model was also adjusted for baseoped AF during hospitalization. AF was defined as an irreg- line characteristics related to mortality by univariate analyular rhythm with the absence of discrete atrial activation sis ( p<0.2), including age, diabetes mellitus, blood pressure, (atrial fibrillation or flutter) recorded in a 12-lead ECG. 29 renal impairment (creatinine clearance), previous myocarWe differentiated between those patients with previous dial infarction (MI), heart failure (Killip class), GRACE clear documentation or records of either paroxysmal, per- risk score, mechanical complications of MI, and myocarsistent, or permanent AF (PrAF), and those with new-onset dial revascularization. Kaplan–Meier curves were also perAF (NAF), which was defined as the absence of medical formed to examine outcomes based on AF status. records of AF but the development of arrhythmia, either at The propensity score model (PS) for the appearance of admission or during hospitalization. AF was obtained by logistic regression using baseline
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Almendro-Delia et al. Table 1. Baseline characteristics.
Characteristic
Non AF (n=36,386)
Female 9305 (25.6) Age (years) 63.8±12.6 Smoking 12,661 (35.6) BMI ≥30 kg/m2) 3928 (11) Diabetes 11,397 (32) Hypertension 18,643 (52.5) Previous MI 6355 (18) Prior CHF 1508 (4.3) Prior stroke 1903 (5.4) AF history 1557 (4.4) Peripheral arterial disease 1759 (5) Renal impairment (CrCl 494 (1.4) <60 ml/min) Previous PCI 3079 (10) Prior CV surgery 909 (2.9) Clinical presentation at admission VF 504 (1.5) SVT 1170 (3.4) 3-grade AV block 1039 (3) Systolic BP (mmHg) 117±26 Heart rate (bpm) 84±17 GRACE risk score 130 (108–155) LVEF (%) 50.6±13 Cardiac troponina 10,295 (59) STEMI 22,004 (60.5) Anterior MI 10,454 (39.6) Killip class (%) I 80 II 12 III 5 IV 3
AF (n=2851)
p
Previous AF (n=1283)
New AF (n=1568)
p
986 (34.6) 71.2±9.8 546 (1.7) 318 (11.5) 1097 (39.5) 1836 (66) 553 (20) 285 (10.4) 275 (10) 809 (29.4) 226 (8.2) 122 (4.4)
<0.001 <0.001 <0.001 0.520 <0.001 <0.001 0.01 <0.001 <0.001 <0.001 <0.001 <0.001
423 (34) 71±10 225 (19) 192 (16) 477 (40) 842 (70.5) 239 (20.3) 141 (12) 139 (12) 1283 (100) 96 (8.1) 94 (8)
529 (35) 71±9.6 311 (21) 124 (8.4) 575 (39) 919 (62.3) 272 (18.5) 127 (8.7) 128 (8.7) – 121 (8.2) 28 (1.9)
0.533 0.634 0.150 <0.001 0.614 <0.001 0.285 0.005 0.009 <0.001 0.927 <0.001
231 (9.3) 133 (5.4)
0.31 <0.001
139 (12) 75 (6.6)
82 (6.6) 47 (3.8)
<0.001 0.002
0.007 <0.001 0.55 0.13 <0.001 <0.001 <0.001 <0.001 <0.001 0.312
38 (3) 23 (2) 20 (2.3) 130±32 93±31 160 (136–182) 45.3±14 293 (31.2) 540 (43.5) 326 (35)
58 (2.1) 445 (16.2) 88 (3.2) 118±28 94±26 161 (136–181) 44.2±14 1009 (70.8) 1485 (52) 805 (38.5) 52 25 14 9
<0.001
55 27 12 6
20 (1.3) 422 (28) 60 (4) 108±19 95±19 162 (138–183) 43±14 196 (40) 945 (63) 444 (41) 50 24 16 10
<0.001 <0.001 0.01 <0.001 0.014 0.432 0.029 <0.001 <0.001 0.008 <0.001
Data are mean±standard deviation, n (%), or median (interquartile range). Categories do not add up to 100% for all variables due to missing values. aCardiac troponin levels >5 times the upper limit of normal. Data from 20,228 patients with available and valid cardiac troponin values AF, atrial fibrillation; AV, atrioventricular; BMI, body mass index; BP, blood pressure; CHF, chronic heart failure; CrCl, creatinine clearance; CV surgery, cardiovascular surgery; LVEF, left ventricular ejection fraction; MI, myocardial infarction; PCI, percutaneous coronary intervention; RI, renal impairment; SVT, sustained ventricular tachycardia; VF, ventricular fibrillation.
clinical characteristics as covariates. 34 Nearest neighbour STEMI and 48% were NSTE-ACS, with 41% NSTEMI PS matching was used in terms of Mahalanobis distance and 7% unstable angina. (calliper 0.2). Goodness of fit was achieved if the standardized differences were ≤10%.35 A two-sided p-value <0.05 was considered statistically significant. The analyses were Clinical data performed using SPSS version 18.0 (SPSS, Chicago, IL, The baseline characteristics are shown in Table 1. AF USA) and R version 2.10.0 (The R Foundation for Statistical patients were more likely to be female, older, and sicker Computing). than those with sinus rhythm. At the time of admission, the AF group had a higher incidence of malignant arrhythmias (VF/SVT) and higher heart rates and plasma cardiac troResults ponin levels than non-AF. Killip class at admission was This study included 39,237 patients. Among these, 2851 worse and the left ventricle ejection fraction (LVEF) was patients (7.3%) developed AF during hospitalization. Of lower in AF patients than in non-AF patients. Compared these, 1568 (55%) developed NAF and 1283 (45%) with the PrAF group, the NAF patients presented with fewer developed PrAF. Of all of the included patients, 52% comorbidities but showed a worse clinical presentation at
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Table 2. Treatments.
Non AF (n=36,386) Pharmacological treatment (at discharge) Aspirin 27,162 (78) Clopidogrel 21,506 (61.7) ACE Inhibitors 21,579 (67.5) Beta-blockers 21,738 (68) Digoxin 87 (0.3) Diuretics 2545 (8) GP inhibitorsa 7139 (26) Inotropesa 2071 (5.9) b Aldosterone antagonist 554/4395 (12.6) Statinsb 2349/4395 (53.4) Vitamin K antagonistsb 86/4395 (2) c Reperfusion treatment Thrombolysis 12,711 (57.8) pPCI 2607 (11.8) No reperfusion 6691 (30.4) PCI during hospitalization 15,092 (68.6)
AF (n=2851)
p
Previous AF (n=1283)
New AF (n=1 568)
p
2176 (78.8) 1753 (63.5) 1600 (65.5) 1273 (52.2) 131 (5.4) 566 (23.2) 449 (19.3) 512 (18.5) 88/433 (18.5) 209/433 (48.3) 86/433 (20)
0.26 0.06 0.05 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.039 <0.001
1141 (93) 1021 (84) 589 (48) 545 (45) 73 (6) 410 (41) 147 (15) 202 (16.5) 59 (12) 885 (84) 67 (25)
967 (67.2) 694 (48.2) 736 (51) 589 (41) 10 (1) 564 (45) 275 (22) 297 (21) 297 (21) 761 (59) 8 (9.2)
<0.001 <0.001 0.134 0.05 <0.001 0.05 <0.001 0.007 0.016 <0.001 0.002
804 (54.1) 194 (13.1) 487 (32.8) 975 (65.7)
0.006 0.16 0.04 0.02
239 (44) 112 (21) 189 (35) 351 (65)
565 (60) 82 (9) 298 (31) 624 (66)
<0.001 <0.001 0.002 0.687
Data are n (%). Categories do not add up to 100% for all variables due to missing values. aDrugs used in the acute stage. bData from 4828 patients. cData from 20,403 patients with STEMI diagnosis. ACE, angiotensin-converting enzyme; AF, atrial fibrillati on; GP, glycoprotein; pPCI, primary percutaneous coronary intervention; STEMI, ST-elevation myocardial infarction.
admission with a greater proportion of patients with STEMI and anterior MI, a higher Killip class, and cardiac troponin levels and lower LVEF, together with more ventricular arrhythmias.
Pharmacological and reperfusion treatment Table 2 summarizes the pharmacological treatment and revascularization strategies during hospitalization. In general, the AF group was less likely to be treated with evidence-based therapies. Thrombolysis was applied less frequently to AF patients diagnosed with STEMI than to non-AF STEMI patients. During hospitalization, AF patients as a group received less reperfusion, although in the STEMI cohort, more AF patients received primary percutaneous coronary intervention (pPCI) than the nonAF STEMI patients. Compared with PrAF patients, the NAF group was less likely to receive antiplatelet agents, statins, vitamin K antagonist, beta-blockers, and pPCI. Thrombolysis was more frequently performed in the NAF group than in the PrAF group, but NAF and PrAF patients underwent similar numbers of PCI procedures during hospitalization.
Figure 1. Predictors of atrial fibrillation. *, Every ten years increased; †, Every 5 bpm increased; ‡, Every 5 points increased. AF, atrial fibrillation; HR, heart rate; LVEF, left ventricular ejection fraction. C-statistic: 0.79 (0.72-0.86 95% CI; p<0.0001).
admission, and heart failure, whereas a better ejection fraction behaved as a protective factor (Figure 1).
Hospital outcomes
AF patients had a poorer prognosis, with a higher incidence of malignant arrhythmias, reinfarction, ischaemic mitral regurgitation, heart failure, cardiogenic shock, and inhospital mortality than non-AF patients. NAF patients had a poorer outcome than PrAF patients (Table 3). There was no difference in bleeding between AF and non-AF patients. Predictors of AF Table 4 summarizes the impact of AF on hospital mortality. The predictor variables for AF development included age, In the unadjusted analysis, both PrAF and NAF patients had being female, prior history of AF, elevated heart rate at an approximately 2-fold increase in mortality compared with
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Almendro-Delia et al. Table 3. Differences in outcomes in patients with and without atrial fibrillation during hospitalization.
Ventricular fibrillation SVT AV block Asystolia Reinfarction Cardiac rupture VSR Ischaemic MR Stroke HF (KC ≥2) Cardiogenic shock Total bleeding (major and minor) Total inhospital mortality CCU stay (days) Hospital stay (days)
Non AF (n=36,386)
AF (n=2851)
p
Previous AF (n=1283)
New AF (n=1568)
p
1165 (3.2) 1994 (5.5) 2119 (5.8) 542 (1.5) 532 (1.9) 93 (0.3) 45 (0.2) 67 (0.2) 99 (2.4) 8174 (22.6) 1473 (4.1) 587 (14.5) 1881(5.2) 1 (1–2) 2 (1–6)
147 (5.2) 565 (19.8) 259 (9.1) 99(3.5) 62 (2.2) 13 (0.5) 4 (0.1) 16 (0.6) 21 (2.9) 1590 (56) 396 (14) 90 (13) 365 (12.8) 2 (1–3) 3 (1–7)
<0.001 <0.001 0.02 <0.001 0.003 0.22 0.85 0.001 0.44 <0.001 <0.001 0.196 <0.001 <0.001 <0.001
51 (4) 62 (5) 19 (1.5) 40 (32) 18 (1.6) 5 (0.4) 1 (0.1) 8 (0.7) 6 (2.6) 648 (52) 156 (12,6) 32 (14) 144 (11.6) 1 (1–2) 3 (1–7)
95 (6.3) 500 (33.3) 103 (7) 55 (35) 44 (2.9) 8 (0.5) 3 (0.2) 8 (0.5) 14 (3) 881 (59) 231 (15.4) 35 (12) 210 (14) 1 (1–4) 4 (2–7)
0.01 <0.001 <0.001 0.531 0.009 0.750 0.472 0.558 0.765 0.001 0.035 0.434 0.001 <0.001 0.001
Data are n (%)or median (interquartile range). Categories do not add up to 100% for all variables due to missing values. AF, atrial fibrilation; AV, atrioventricular; CCU, coronary care unit; HF, heart failure; KC, Ki llip class; MR, mitral regurgitation; SVT, sustained ventricular tachycardia; VSR, ventricular septal rupture.
Table 4. Predictors of mortality: multivariate analysis.
Unadjusted
PrAF NAF
Adjusted
PS cohort
HR
p
HR
p
HR
p
1.89 (1.6–2.4) 2.19 (1.9–2.53)
<0.001 <0.001
– 1.70 (1.12–3.40)
– <0.001
– 1.62 (1.09–2.89)
– <0.001
Values are hazard ratios (95% confidence interval). NAF, new atrial fibrillation; PrAF, previous atrial fibrillation; PS, propensity score.
non-AF patients; the increase was slightly more pronounced our findings. Schmitt et al. concluded that AF during MI for NAF patients (Supplementary Table S1, available online). implied worse short- and long-term outcomes, independent After adjustment, NAF but not PrAF remained an independent of MI treatment strategies. Two well-conducted meta-anal predictor of mortality (Figure 2 and Supplementary Table S2). yses focusing on the prognostic implication of AF during By using PS matching, 1908 pairs of AF and non-AF patients MI have been published. The first one, by Jabre et al., 12 were matched with both groups balanced with respect to base- included 43 articles with a total of 278,854 MI patients. line characteristics (Supplementary Table S3). After matching, Their analysis indicated that AF led to a 40% increase in the only NAF remained as an independent predictor of inhospital risk of mortality in the setting of MI, even after adjustment mortality, with a slight reduction in HR (Table 4 and for the same variables as in our study, and concluded that Supplementary Table S4). NAF and PrAF did not differ in mortality risk. The second and most recent study, by Angeli et al., 13 was a meta-analysis of 24 studies comparing outcomes in patients with MI Discussion and sinus rhythm with those observed for AF patients. In We have demonstrated an increased risk of mortality asso- their study, patients with AF had a 2-fold increased risk of ciated with the presence of AF in the setting of ACS. NAF inhospital mortality and patients with clear evidence of is associated with an almost 70% increase in mortality risk. NAF had a 3-fold higher risk of mortality than sinus rhythm This increased risk was not demonstrated for PrAF after a patients, although the study did not adjust for the Killip two well-conducted adjustment analyses. The results of class, an important confounding variable. 15,26–28 previous studies are conflicting regarding the prognostic It is important to note that in the meta-analysis by Jabre 1–6,8,9 impact of AF types in MI. However, our results are et al.,12 the association between AF and mortality was simconsistent with some previous reports. 2–13 ilar for NAF and PrAF. These results persisted even after The predictors and significance of AF in the setting of adjusting for confounding variables such as heart failure MI reviewed by Schmitt et al. 11 are fairly consistent with and revascularization, although only four of the 43 studies Downloaded from acc.sagepub.com at HOSPITAL VIRGEN DE LA VICTORI on February 13, 2014
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Figure 2. Kaplan Meier survival curves for inhospital mortality.
included in his work performed such important adjust- consumption, loss of atrial contraction, and AV synchrony, ments. These results are consistent with ours with respect which lead to a decrease in effective cardiac output. 7,9,12–15,39 to risk ratios for NAF patients but differ in relation to PrAF Secondly, irregular RR intervals can develop malignant patients. To understand these conflicting results, we per- arrhythmias, as was observed in our cohort. 12–15,40 However, formed a PS analysis, demonstrating that only NAF per- the mechanism of AF-related mortality in ACS remains elusists as an independent predictor of inhospital mortality. sive; the association of AF with higher heart rates, lower The association of PrAF with mortality found in the analy- LVEF and an increased incidence of HF suggests that sis of Jabre et al. 12 is most likely explained by the inclusion haemodynamic impairment is a potential explanation as of studies that analysed long-term mortality and not only well as increased adrenergic and neurohormonal inhospital outcomes, as we did in our study. NAF in the activation.9,12,13,21 setting of ACS reflects a greater ischaemic burden than The rate of cerebrovascular events in patients with any PrAF, as shown by the lower LVEF, larger infarct sizes, type of AF in our study was quite low and not significantly and higher Killip class associated with NAF compared different from patients in sinus rhythm. This may have been with PrAF in our study. In contrast, PrAF is associated due to the relatively young age of patients enrolled in our with a worse clinical baseline profile than NAF due to the ACS registry, but we cannot rule out other reasons. The existence of chronic heart disease. 9,36 Along the same line current AF guidelines do not provide a better therapeutic of reasoning, recent data from a large registry in Canada 37 approach for treating AF during an ACS. 29–30 However, has showed that patient with prior history of AF receive European Society of Cardiology STEMI guidelines clearly less evidence-based therapies and inhospital coronary recommend optimized triple antithrombotic therapy for angiography compared to non-AF patients, similarly to our patients with a clear indication of oral anticoagulation. 31 results. As in our observational study, patients with prior The use of oral anticoagulants was quite low in our study, AF were older and sicker, 37 and broadly differ to those below 30%, clearly not in line with clinical guidelines. 29–30 included in the work by Lopes et al. 38 that examined pooled This finding may be due to the use of dual antiplatelet therdata from 10 ACS trials, which includes more cases of apy, thereby avoiding the risk of bleeding associated with NAF and STEMI and probably less patients with signifi- triple therapy. Nonetheless, our data are consistent with cant comorbidities or those receiving warfarin, as is com- those of a Swedish registry (RIKS-HIA), in which longmon in patients included in clinical trials compared with term oral anticoagulation therapy was only given to 30% of those derived from registries. the patients with AF during an acute MI. 41 Likewise, statin The development of AF may worsen the prognosis of use was quite low in our study. Danchin et al. 42 have demACS due to several factors. First, AF triggers some adverse onstrated that early statin therapy reduces the incidence of haemodynamic effects, including increased oxygen AF during MI; thus, the mortality risk associated with AF
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in our study may be greater than expected due to the lack of early use of statin. Considering our results, there is sufficient evidence to consider AF a mortality risk factor in the course of ACS.
Study limitations
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. However, the Health Council of the Junta de Andalucia gives support to the virtual platform of the Registry. References
Several limitations of the present investigation deserve discussion. First, the study population were derived from an ACS-specific registry, not from an AF-specific registry. The results should be considered only as hypothesis generating, and we cannot exclude the influence of other noncontrolled confounders in our conclusions. There is also selection bias because we included only patients admitted to coronary care units. Secondly, data concerning the temporal relation between the onset of AF and the appearance of complications were not available, so we can establish an association but not a causality relationship. Silent episodes of AF could not be analysed in the sinus rhythm group. Furthermore, the adjustment of variables that were not measured at baseline (e.g. mechanical complications of MI and revascularization) in multivariable analysis, and comparison of outcomes which might occur before or after AF (since its timing was not known), should be interpreted with caution. Thirdly, data concerning the causes of death were only available for a minority of patients; thus, the causes of death are not shown. Data concerning treatment strategies used for AF as well as the timing, duration, and final outcome of AF episodes were not assessed. Although rates of reperfusion were low in our registry, the mortality numbers were similar to that reported in the GRACE registry.18 In the final adjusted model, three-vessel disease was not finally included, because in our study coronary angiography rates was low and this might influence the results.
Conclusions AF is not an infrequent event during ACS. The presence of NAF is associated with nearly an 70% mortality increase in MI patients. Our results suggest that the appearance of NAF during ACS should not be considered an isolated benign event but a severe complication with prognosis implications. These results are hypothesis generating and the prognostic impact of treatment strategies for NAF in MI patients must be assessed in future studies. ARIAM Andalucia Study Group
A list of investigators is given in the Appendix available online. Conflict of interest
The authors declare that there is no conflict of interest.
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