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Department of Clinical Pharmacology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, AustriaDepartment of Medicine I, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
Department of Emergency Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, AustriaZentrale Notaufnahme, Wilhelminenspital, Montleartstr.37, 1160 Vienna, Austria
Atrial fibrillation is the most frequent arrhythmia in the Emergency Department.
•
The optimal acute management of stable atrial fibrillation, however, is unclear.
•
Strategies aimed at individualizing management decisions are an unmet need.
•
The ReSinus score allows prediction of spontaneous conversion to sinus rhythm.
•
It is an easy-to-use score providing good discrimination and clinical usefulness.
•
The ReSinus score may aid clinicians in guiding individual cardioversion decisions.
Abstract
Background
The optimal management of patients presenting to the Emergency Department with hemodynamically stable symptomatic atrial fibrillation remains unclear. We aimed to develop and validate an easy-to-use score to predict the individual probability of spontaneous conversion to sinus rhythm in these patients
Methods
This retrospective cohort study analyzed 2426 cases of first-detected or recurrent hemodynamically stable non-permanent symptomatic atrial fibrillation documented between January 2011 and January 2019 in an Austrian academic Emergency Department atrial fibrillation registry. Multivariable analysis was used to develop and validate a prediction score for spontaneous conversion to sinus rhythm during Emergency Department visit. Clinical usefulness of the score was assessed using decision curve analysis
Results
1420 cases were included in the derivation cohort (68years, 57-76; 43% female), 1006 cases were included in the validation cohort (69years, 58-76; 47% female). Six variables independently predicted spontaneous conversion. These included: duration of atrial fibrillation symptoms (<24hours), lack of prior cardioversion history, heart rate at admission (>125bpm), potassium replacement at K+ level ≤3.9mmol/l, NT-proBNP (<1300pg/ml) and lactate dehydrogenase level (<200U/l). A risk score weight was assigned to each variable allowing classification into low (0-2), medium (3-5) and moderate (6-8) probability of spontaneous conversion. The final score showed good calibration (p=0.44 and 0.40) and discrimination in both cohorts (c-indices: 0.74 and 0.67) and clinical net benefit
Conclusion
The ReSinus score, which predicts spontaneous conversion to sinus rhythm, was developed and validated in a large cohort of patients with hemodynamically stable non-permanent symptomatic atrial fibrillation and showed good calibration, discrimination and usefulness
Conversion of recent onset paroxysmal atrial fibrillation to normal sinus rhythm: the effect of no treatment and high-dose amiodarone. A randomized, placebo-controlled study.
In real-world emergency cohorts optimal management is unclear; however, treatment with early cardioversion is a common practice, although the individual probability of a patient converting to sinus rhythm spontaneously is uncertain. Deferred cardioversion in patients with a low probability of spontaneous conversion may result in an unjustified treatment delay and increased risk of stroke, heart failure and progression to persistent atrial fibrillation,
Conversely, patients who are likely to convert to sinus rhythm spontaneously, but undergo early pharmacological or electrical cardioversion may be unnecessarily hospitalized and exposed to the risk of post-conversion arrhythmias and complications by general anesthesia or antiarrhythmic drugs.
of atrial fibrillation, several decision aids have been developed to facilitate the estimation of a patients’ individual stroke, bleeding and mortality risk, thus guiding safe and effective long-term management.
Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation.
A tool to estimate the individual probability of spontaneous conversion of patients presenting with symptomatic atrial fibrillation, however, is not available. Ideally, informed decisions of patients and clinicians should be based on this information.
In this study we developed and validated an easy-to-use score to estimate the individual probability of spontaneous conversion to sinus rhythm in adult patients with hemodynamically stable non-permanent symptomatic atrial fibrillation.
2. Material and methods
2.1 Study design/setting
This retrospective cohort study is based on an atrial fibrillation registry including consecutive adult patients with atrial fibrillation treated at the Emergency Department at the Medical University of Vienna, an academic tertiary care facility. The Emergency Department comprises an outpatient care section and an affiliated critical care unit covering more than 90 000 patients overall
and about 600 patients with atrial fibrillation per year. Patients presenting with atrial fibrillation and hypokalemia (less than 3.5mmol/l) or low-normal potassium levels (3.5-3.9mmol/l) receive potassium replacement with Elozell Spezial intravenous solution (containing 24 mmol potassium and 12mmol magnesium per 250ml) based on provider-dependent practice. Acute rate and rhythm control therapy is based on current European Society of Cardiology (ESC) guidelines
and depends on the patient's left ventricular function, hemodynamics, drug history including anticoagulation status and comorbidities. Rate control medication includes beta-adrenergic receptor blockers (metoprolol, esmolol), verapamil and cardiac gylcosides (digoxin, digitoxin). Pharmacological cardioversion is performed mainly with vernakalant, ibutilide and amiodarone. Standard biphasic defibrillators are used for synchronized direct current electrical cardioversion.
The study was approved by the Ethics Committee of the Medical University of Vienna and conducted in accordance with ICH-GCP guidelines and Helsinki Declaration. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination of our research. The study is registered with clinicaltrials.gov (NCT03272620).
2.2 Atrial fibrillation registry
The atrial fibrillation registry has previously been described in detail.
In brief, the registry started in January 2011 and prospectively includes all cases of atrial fibrillation confirmed by 12-lead electrocardiography. Prior to inclusion informed consent is obtained. Demographics, past medical history including comorbidities, concomitant medication and previous attempts at electrical cardioversion, the CHA2DS2VASC score, results from blood gas analysis, blood count, chemistry, coagulation variables, thyroid function, troponin and NT-proBNP levels are obtained. Additionally, vital signs including heart rate, blood pressure and oxygen saturation, symptoms attributable to atrial fibrillation, the time of symptom onset, the type of atrial fibrillation, and treatments including electrolyte substitution, rate control medication and cardioversion attempts are documented by study fellows.
2.3 The ReSinus score
The score was developed and validated in cases of hemodynamically stable first-detected or recurrent non-permanent symptomatic atrial fibrillation entered into the atrial fibrillation registry between January 2011 and January 2019 (Figure 1).
Figure 1Study flow chart. Out of 3012 cases of symptomatic atrial fibrillation documented between 2011 and 2019, a total of 2426 cases of hemodynamically stable first-detected or recurrent non-permanent symptomatic atrial fibrillation were eligible for analysis. Owing to the benefits of temporal validation the validation sample was restricted to the most contemporary cases at a sample size sufficient to handle up to 10 independent predictors in the model. 1420 cases were finally included in the derivation cohort and 1006 cases were included in the validation cohort.
Patients with a history of permanent atrial fibrillation and atrial fibrillation events occurring in the context of critical illness were excluded before cohort allocation. Initially, internal validation was planned using a random sample for model derivation and one for validation in a 1:1 sample ratio. However, owing to the considerations of Steyerberg and Verguowe
on the benefits of temporal validation we restricted the validation sample to the most contemporary cases at a sample size sufficient to handle up to 10 independent predictors in the model. Accordingly, we aimed at a sample size of approximately 1000 patients for the validation set. Temporal validation assesses the performance of a model by applying it to the most recent data from the population it was initially derived. Thus, it refers to the generalizability and transportability of study findings to plausibly related populations and is considered a more robust test for prediction models. A comparison of the set of excluded patients (n=345) with the set of patients finally included in the validation cohort (n=1006) is available with the supplement (Supp. Table 4).
Spontaneous conversion was defined as return to sinus rhythm during Emergency Department visit without any attempt at pharmacological or electrical cardioversion. Rate-control therapy as well as electrolyte replacement was not rated as an attempt at cardioversion. Patients who converted spontaneously did not receive any anti-arrhythmic drugs prior to restoration of sinus rhythm.
2.4 Statistical methods
Variables are presented as absolute values (n), relative frequencies (%) and median (25-75% interquartile ranges, IQR). Between-group comparisons were performed using the Mann-Whitney U test for continuous variables or the chi-squared test/Fisher's exact test for nominal variables. Univariable logistic regression with spontaneous conversion to sinus rhythm as a dependent variable was performed on available cases. Several candidate predictors for spontaneous conversion, which were judged to be clinically plausible, were tested. Only variables available within one hour upon admission were used to allow the creation of a risk score readily useable in clinical practice. Continuous variables in univariable analysis were categorized by selecting clinically relevant cut-offs, which were the closest to the statistically optimal cut-offs, and examined for linear and non-linear associations including restricted cubic splines. Categorization of variables yielded at parsimony and dichotomy, and cut-offs were optimized for maximum discrimination in the derivation cohort.
Variables significant in univariable analysis were entered in a multivariable logistic regression model to calculate adjusted odds ratio (OR) with 95% confidence intervals (95% CI). A stepwise process was used, aiming at the most parsimonious model. Interaction was assessed using the likelihood ratio test. In case of interaction the interaction terms were used in subsequent models. Multicollinearity between the variables in the final model was assessed as ill-conditioning, i.e. the impact of small random changes (perturbations) to variables on parameter estimates. Firth regression penalizing the log-likelihood with one-half of the logarithm of the determinant of the information matrix was used for the final model to avoid overfitting to the derivation data. These adjusted coefficients of significant multivariable predictors were then divided by the lowest coefficient value in the model and rounded to the nearest integer. From these values a risk score weight was assigned to each predictor in the model. The sum of risk scores for each patient was subsequently calculated.
Calibration was assessed by performing the Hosmer–Lemeshow goodness-of-fit test and by plotting observed vs. predicted incidence rate across categories of the developed score. In the derivation set, as well as in the validation set, the predictive performance of the risk score was assessed using the c-statistic, which was 1000-fold cross-validated by bootstrapping in order to evaluate the discriminative ability.
To test the robustness of the model according to the time to cardioversion we performed a sensitivity analysis restricting the observation time from admission to restoration of sinus rhythm to two hours. Clinical usefulness was evaluated using decision curve analysis.
In this context, clinical net benefit is the relationship between the benefit of treating those, who need treatment, and the harm of treating those, who do not need treatment. Decision curve analysis allows the evaluation of clinical net benefit of a prognostic tool over a range of threshold probabilities of having a positive outcome. Clinical net benefit is calculated as, where n is the total number of patients, and ptis the threshold probability of having a positive outcome. True and false positives are calculated using pt as the cut-point for determining a positive or negative result. This calculation is repeated over a range of clinically meaningful threshold probabilities.
Missing data were included as separate categories for each variable as appropriate. Stata 14 (StataCorp, College Station, TX, USA) was used for data analysis. Generally, a two-sided p-value <0.05 was considered statistically significant.
3. Results
Out of 3012 cases of symptomatic atrial fibrillation entered into the registry between 2011 and 2019, 2426 cases of hemodynamically stable first-detected or recurrent non-permanent symptomatic atrial fibrillation were eligible for analysis. 1420 cases were included in the derivation cohort (median age 68 years, IQR 57 - 76; 43% female) and 1006 cases were included in the validation cohort (median age 69 years, IQR 58 - 76; 47% female). Table 1 provides an overview of demographics, baseline characteristics and clinical course of both study cohorts.
Table 1Demographics and baseline characteristics of the derivation and the validation cohort
Derivation cohort n=1420
Validation cohort n=1006
p =
available* (n)
available* (n)
Clinical Characteristics
Age, years (IQR)
68
(57 - 76)
1420
69
(58 - 76)
1420
0.190
Female gender, n (%)
615
(43)
472
(47)
0.065
BMI, kg/m˄2 (IQR)
28
(25 - 30)
1420
28
(24 - 30)
984
0.824
BP systolic, mmHG (IQR)
134
(120 - 150)
1290
134
(120 - 150)
967
0.876
BP diastolic, mmHG (IQR)
82
(72 - 95)
1279
85
(75 - 95)
962
0.034
Heart rate, bpm (IQR)
130
(111 - 147)
845
130
(111 - 146)
625
0.762
Comorbidities
Lone AF, n (%)
86
(6)
72
(7)
0.311
Heart failure, n (%)
242
(17)
174
(17)
0.894
Hypertension, n (%)
873
(61)
593
(59)
0.185
DM, n (%)
327
(23)
130
(13)
0.433
TIA, n (%)
38
(3)
12
(1)
0.016
Stroke, n (%)
93
(7)
49
(5)
0.061
CAD, n (%)
243
(17)
166
(17)
0.711
Previous myocardial infarcton, n (%)
110
(8)
91
(9)
0.276
PAD, n (%)
59
(4)
39
(4)
0.733
COPD, n (%)
114
(8)
72
(7)
0.452
Valvular pathology, n (%)
349
(25)
235
(23)
0.445
Hyperlipidaemia, n (%)
452
(32)
301
(30)
0.331
Hyperthyreosis, n (%)
56
(4)
46
(5)
0.580
Hypothyreosis, n (%)
221
(16)
163
(16)
0.759
Current smoker, n (%)
77
(5)
44
(4)
0.749
AF history
First AF episode, n (%)
217
(15)
183
(18)
0.505
Previous AF episodes, n (%)
3
(0 - 8)
691
3
(0 - 8)
506
0.373
Previous electrical CV, n (%)
398
(28)
298
(30)
0.392
Ablation, n (%)
229
(16)
180
(18)
0.227
Duration of AF symptoms, h (IQR)
6
(2 - 21)
760
6
(2 - 19)
558
0.997
CHA2DS2-VASc (IQR)
3
(1 - 4)
1339
2
(1 - 4)
961
0.029
Medication
Phenprocoumon, n (%)
355
(25)
198
(20)
0.002
DOACS, n (%)
245
(17)
213
(21)
0.015
Cardiac glycosides, n (%)
56
(4)
38
(4)
0.834
Beta blocker, n (%)
625
(44)
437
(43)
0.779
Amiodarone, n (%)
255
(18)
147
(15)
0.039
AT-2 Blocker, n (%)
316
(22)
266
(16)
0.025
ACE Blocker, n (%)
284
(20)
174
(17)
0.102
Pre-Treatment
Potassium replacement, n (%)
712
(50)
377
(37)
0.171
Laboratory
Haematocrit, % (IQR)
41
(38 - 45)
1351
42
(38 - 45)
941
0.134
WBC, G/l (IQR)
8
(7 - 10)
1363
8
(7 - 10)
950
0.860
Creatinine, mg/dl (IQR)
1
(1 - 1)
1361
1
(1 - 1)
954
0.350
Potassium, mmol/l (IQR)
4.0
(3.8 - 4.3)
1321
4.0
(3.8 - 4.3)
930
0.973
LDH, U/l (IQR)
196
(167 - 238)
1117
198
(169 - 240)
792
0.435
NT-proBNP, pg/ml (IQR)
943
(303 - 2393)
1172
981
(312 - 2739)
831
0.355
hs-Troponin T, ng/l (IQR)
14
(8 - 25)
1090
14
(8 - 27)
821
0.663
CRP, mg/dl (IQR)
0
(0 - 1)
1318
0
(0 - 1)
927
0.750
Lactate, mmol/l (IQR)
1
(1 - 2)
1238
2
(1 - 2)
870
0.226
INR, (IQR)
2
(1 - 3)
738
1
(1 - 2)
527
<0.001
TSH, μU/ml (IQR)
2
(1 - 3)
980
2
(1 - 3)
691
0.492
*Number of patients who had the variable available. Missing data for some score variables in both cohorts ranged from 0 to 21%.
ACE (angiotensin converting enzyme), AF (atrial fibrillation), AT (angiotensin), BMI (body mass index), BP (blood pressure), CAD (coronary artery disease), COPD (chronic obstructive pulmonary disease), CRP (C-reactive protein), CV (cardioversion), DM (diabetes mellitus), DOAC (direct oral anticoagulants), hs (high-sensitive), INR (international normalized ratio), LA (left atrium), LDH (lactate dehydrogenase), NT-proBNP (N-terminal-pro brain natriuretic peptide), PAD (peripheral artery disease), RA (right atrium), TIA (transient ischemic attack), TSH (thyroid stimulating hormone), WBC (white blood cells).
The frequency distribution of individual DOACS, glycosides and beta-blockers is available with the supplement (Supp. Table 1).
968 patients (67.3%) in the derivation cohort and 717 patients (71.3%) in the validation cohort who did not convert spontaneously underwent an attempt of pharmacological/electrical cardioversion.
The median duration from admission to restoration of sinus rhythm was significantly longer in patients who were cardioverted than in those who converted spontaneously to sinus rhythm: 4.1 vs. 2.1 hours in the derivation cohort (p<0.001) and 3.7 vs. 1.5 hours in the validation cohort (p=0.040) (Supp. Table 5).
3.1 Derivation of the ReSinus score
Spontaneous conversion to sinus rhythm occurred in 186 patients (13%). Clinical and laboratory characteristics of patients in the derivation cohort stratified by the occurrence of spontaneous conversion are shown in Table 2.
Table 2Clinical and biomarker characteristics among patients with and without spontaneous conversion
Spontaneous Conversion n=186
No Spontaneous Conversion n=1234
p =
available* (n)
available* (n)
Clinical Characteristics
Age, years (IQR)
68
(56 - 76)
186
68
(58 - 75)
1234
0.490
Male gender, n (%)
108
(58)
697
(56)
0.626
BMI, kg/m˄2 (IQR)
28
(25 - 30)
186
28
(2 - 30)
1234
0.315
BP systolic, mmHG (IQR)
133
(120 - 150)
168
134
(11 - 150)
1122
0.728
BP diastolic, mmHG (IQR)
82
(72 - 95)
167
82
(7 - 95)
1112
0.825
Heart rate, bpm (IQR)
136
(117 - 149)
142
129
(110 - 146)
703
0.012
Comorbidities
Lone AF, n (%)
9
(5)
77
(6)
0.405
Heart failure, n (%)
19
(10)
223
(18)
0.008
Hypertension, n (%)
128
(69)
754
(61)
0.022
DM, n (%)
20
(11)
177
(14)
0.217
TIA, n (%)
5
(3)
33
(3)
0.964
Stroke, n (%)
12
(6)
81
(7)
0.996
CAD, n (%)
33
(18)
210
(17)
0.769
Previous myocardial infarction, n (%)
14
(8)
96
(8)
0.896
PAD, n (%)
4
(2)
55
(4)
0.137
COPD, n (%)
12
(6)
102
(8)
0.329
Valvular pathology, n (%)
46
(25)
303
(25)
0.999
Hyperlipidaemia, n (%)
67
(36)
385
(31)
0.166
Hyperthyreosis, n (%)
6
(3)
50
(4)
0.674
Hypothyreosis, n (%)
37
(20)
184
(15)
0.083
Current smoker, n (%)
22
(12)
135
(11)
0.868
AF history
First AF episode, n (%)
40
(22)
177
(14)
0.027
Previous AF episodes, n (IQR)
2
(0 - 5)
97
3
(0 - 8)
594
0.058
Previous electrical CV, n (IQR)
38
(20)
360
(29)
0.018
Ablation, n (%)
31
(17)
198
(16)
0.802
Duration of AF symptoms, h (IQR)
4
(2 - 8)
133
7
(2 - 24)
627
<0.001
CHA2DS2-VASc (IQR)
3
(2 - 4)
182
2
(1 - 4)
1157
0.366
Medication
Phenprocoumon, n (%)
38
(20)
317
(26)
0.140
DOACS, n (%)
35
(19)
210
(17)
0.545
Cardiac glycosides, n (%)
6
(3)
50
(4)
0.589
Beta blocker, n (%)
92
(49)
533
(43)
0.108
Amiodarone, n (%)
22
(12)
233
(19)
0.022
AT-2 Blocker, n (%)
54
(29)
262
(21)
0.018
ACE Blocker, n (%)
46
(25)
238
(19)
<0.001
Pre-Treatment
Potassium replacement, n (%)
124
(67)
588
(48)
<0.001
Laboratory
Haematocrit, % (IQR)
41
(37 - 44)
180
42
(38 - 45)
1171
0.237
WBC, G/l (IQR)
8
(7 - 10)
183
8
(7 - 10)
1180
0.950
Creatinine, mg/dl (IQR)
1
(1 - 1)
183
1
(1 - 1)
1178
0.094
Potassium, mmol/l (IQR)
3.9
(3.7 - 4.1)
181
4.0
(3.8 - 4.3)
1140
<0.001
LDH, U/l (IQR)
190
(163 - 220)
173
198
(167 - 241)
944
0.012
NT-proBNP, pg/ml (IQR)
516
(22 - 1282)
164
1042
(334 - 2574)
1008
<0.001
hs-Troponin T, ng/l (IQR)
15
(8 - 25)
143
14
(8 - 25)
947
0.406
CRP, mg/dl (IQR)
0
(0 - 1)
176
0
(0 - 1)
1142
0.752
Lactate, mmol/l (IQR)
1
(1 - 2)
177
1
(1 - 2)
1061
0.374
INR, (IQR)
1
(1 - 2)
97
2
(1 - 3)
641
<0.001
TSH, μU/ml (IQR)
2
(1- 3)
136
2
(1 - 3)
844
0.456
*Number of patients who had the variable available. Missing data for some score variables in both cohorts ranged from 0 to 21%.
ACE (angiotensin converting enzyme), AF (atrial fibrillation), AT (angiotensin), BMI (body mass index), BP (blood pressure), CAD (coronary artery disease), COPD (chronic obstructive pulmonary disease), CRP (C-reactive protein), CV (cardioversion), DM (diabetes mellitus), DOAC (direct oral anticoagulants), hs (high-sensitive), INR (international normalized ratio), LA (left atrium), LDH (lactate dehydrogenase), NT-proBNP (N-terminal-pro brain natriuretic peptide), PAD (peripheral artery disease), RA (right atrium), TIA (transient ischemic attack), TSH (thyroid stimulating hormone), WBC (white blood cells).
The frequency distribution of individual DOACS, glycosides and beta-blockers is available with the supplement (Supp. Table 2).
Univariable analysis identified six variables independently associated with spontaneous conversion to sinus rhythm. The identified values of predictors were doubled and rounded to the nearest integer (Table 3).
Table 3Independent predictors of spontaneous conversion to sinus rhythm
Predictor
Coefficient
95% CI
p =
Weighted Score
Duration of AF symptoms <24 h
0.89
(
0.53
-
1.25
)
<0.001
2
No previous electrical CV
0.75
(
0.35
-
1.15
)
<0.001
2
Heart rate > 125 bpm
0.55
(
0.22
-
0.88
)
<0.001
1
Potassium replacement at K+ level ≤3.9mmol/l
0.55
(
0.21
-
0.89
)
0.002
1
NT-proBNP < 1300 pg/ml
0.54
(
0.19
-
0.88
)
0.003
1
LDH < 200 U/l
0.45
(
0.11
-
0.78
)
0.009
1
Firth regression was used to penalize the log-likelihood of the model to avoid overfitting to the derivation data. AF (atrial fibrillation), CV (cardioversion), NT-proBNP (N-terminal-pro brain natriuretic peptide), LDH (lactate dehydrogenase).
The weighted score included the duration of atrial fibrillation related symptoms (<24 hours; 2 points), a lack of prior cardioversion history (2 points), heart rate at admission (>125 beats per minute; 1 point), potassium replacement at K+ level ≤3.9mmol/l (1 point), NT-proBNP (<1300 pg/ml; 1 point) and lactate dehydrogenase level (<200 U/l; 1 point), totaled to a maximum score of 8 points. The factor ‘potassium replacement at K+ level ≤3.9mmol/l’ included all patients who received potassium substitution unless potassium level exceeded 3.9mmol/l. The observed incidence of short-term spontaneous conversion was 50% for patients with 8 points and 0% for patients with 0 points. The odds ratio associated with every increase of one score point was 1.61 (95% CI 1.47–1.76; p < 0.001).
The final model showed good discrimination (c-index 0.74; 95% CI 0.70–0.77). The p-value of the Hosmer-Lemeshow goodness-of-fit test was 0.43. The predicted and observed incidence of short-term spontaneous conversion to sinus rhythm across the groups built by final score values showed good calibration (p=0.43) (Figure 2).
Figure 2Observed and predicted incidence of spontaneous conversion in (A) the derivation cohort and (B) the validation cohort. Calibration was visualized by plotting observed vs. predicted incidence rate across categories of the developed score in the derivation set (A), as well as in the validation set (B). The dotted line represents perfect calibration, the solid line represents actual calibration.
The cumulative final score value of each patient allowed classification into low (0-2), medium (3-5) and moderate (6-8) probability of spontaneous conversion. The rates of short-term spontaneous conversion to sinus rhythm across these three classes were 3.8%, 12.3%, 33.8% respectively (Figure 3).
Figure 3Observed incidence of spontaneous conversion across low (0-2), medium (3-5) and moderate (6-8) probability categories for spontaneous conversion in the (A) derivation cohort and (B) the validation cohort.
The observed incidence of spontaneous conversion in the validation cohort according to their classification (low, medium, moderate) was 7.9%, 9.1% and 19.6%. The score showed good calibration (p=0.40) and discrimination (c statistic 0.67; 95% CI 0.63–0.72) (Figure 2).
The sensitivity analysis censoring the observation time at two hours suggested robustness of the model (Supp. Table 3).
The decision curve analysis indicated a substantial clinical net benefit of using the ReSinus score over the full spectrum of possible thresholds (Figure 4).
Figure 4Decision curve analysis showing clinical usefulness of the ReSinus score. X-axis depicts threshold probability of spontaneous conversion. Y-axis depicts clinical net benefit of three different strategies: Dashed line: ReSinus score. Solid line: assume all patients will convert. Thin line: assume no patient will convert. ReSinus score has a positive net benefit over the whole spectrum of threshold probabilities. This was especially true in the clinically relevant probability range from 5 to 30%, which corresponds to the spontaneous conversion rates observed in our and many other studies.
Figure 5 provides an easy-to-use template for pragmatic calculation of the ReSinus score in the clinical setting. Online calculation of the score is available at www.meduniwien.ac.at/notfall/resinus.
Figure 5Stratification according to the probability of spontaneous conversion using the ReSinus score. Work along criteria A-F from the middle to the edge. Each corresponding answer leads to the adjacent field of the next circle (blue = true; white = false). The final score can be read directly from the outermost circle. The individual probability of spontaneous conversion according to the score is given by the bar on the right side. AF (atrial fibrillation), CV (cardioversion), NT-proBNP (N-terminal-pro brain natriuretic peptide), LDH (lactate dehydrogenase).
In this study we developed and validated an easy-to-use risk score to estimate the individual probability of spontaneous conversion to sinus rhythm in patients with hemodynamically stable non-permanent symptomatic atrial fibrillation. The score includes both clinical and laboratory information and was developed from a cohort of patients treated in a real-world emergency setting. To allow for the creation of a risk score readily applicable in clinical practice, only variables available within one hour of admission were used. Six independent predictors of spontaneous conversion to sinus rhythm were identified including four clinical (actual heart rate, duration of atrial fibrillation related symptoms, clinical history of previous cardioversion, potassium replacement) and two routine laboratory variables (NT-proBNP and LDH level). The final score was well calibrated, showed good discriminative ability and clinical usefulness.
4.1 Clinical predictors
Atrial fibrillation is considered a continuous process of electro-anatomical remodeling promoting electrical dissociation, heterogenic conduction and arrhythmic atrial contractions.
With progressive disease, accumulating structural changes in atrial myocytes promote further episodes of atrial fibrillation, which facilitates electro-physiologic conditions that perpetuate atrial arrhythmia and limit the probability of spontaneous conversion.
Accordingly, in the ReSinus score the duration of atrial fibrillation was assigned the highest risk score weight alongside a lacking history of cardioversion. Several variables of the ReSinus score reflect advanced heart disease and may indicate structural atrial alterations. In this context, a history of previous cardioversion attempts may suggest the presence of structural changes, which impede spontaneous conversion and prompt the need for cardioversion. Likewise, a low heart rate may indicate that the disease is already advanced. Data on the relation between heart rate and the probability of spontaneous conversion in atrial fibrillation are lacking. However, a higher heart rate has previously been shown to favor successful cardioversion and is associated with both a lower rate of progression
and a lower rate of recurrence in atrial fibrillation. In more advanced disease, remodeling processes within atria and the AV-node, as well as negative chrono-and dromotropic treatment, may promote lower ventricular conduction rates, which may thus be associated with a lower probability of spontaneous conversion.
4.2 Laboratory predictors
The final score includes two biomarkers. NT-pro BNP has previously been shown to be associated with atrial appendage dysfunction
Association between the N-terminal plasma brain natriuretic peptide levels or elevated left ventricular filling pressure and thromboembolic risk in patients with non-valvular atrial fibrillation.
B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies.
Europace: European pacing, arrhythmias, and cardiac electrophysiology: journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.2014; 16: 1426-1433
but this is not yet routinely available in clinical practice. Lactate dehydrogenase in contrast is a widely used unspecific marker of tissue damage, which is elevated in various medical conditions. It has recently been shown that lactate cascades play a crucial role in the process of atrial remodeling and in the maintenance of atrial fibrillation.
We can only speculate as to whether elevated LDH levels in our study patients reflect atrial alterations associated with atrial fibrillation or other comorbidities. It is, however, evident that in atrial fibrillation biomarker-guided management lags behind that of other major cardiovascular diseases including myocardial infarction and heart failure, where both diagnosis and treatment rely on laboratory values. The inclusion of biomarkers into prediction models for the assessment of bleeding, stroke and death risk in patients with atrial fibrillation has substantially improved their accuracy and usefulness.
The novel biomarker-based ABC (age, biomarkers, clinical history)-bleeding risk score for patients with atrial fibrillation: a derivation and validation study.
It is conceivable that the inclusion of further biomarkers not yet readily available in clinical practice would likewise improve the performance of the ReSinus score.
4.3 Application of the ReSinus score
The ReSinus score was developed and validated in a real-world cohort of patients with non-permanent symptomatic atrial fibrillation presenting to the Emergency Department. Atrial fibrillation is among the most frequent arrhythmias encountered in the acute care sector and places an increasingly critical economic burden on health care systems,
Costs of atrial fibrillation in five European countries: results from the Euro Heart Survey on atrial fibrillation.
Europace: European pacing, arrhythmias, and cardiac electrophysiology: journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.2008; 10: 403-411
stressing the importance of strategies aimed at personalizing acute management decisions to reduce hospital admissions for unnecessary cardioversions, treatment complications and associated health care costs.
2016 ESC guidelines suggest immediate cardioversion in patients with recent-onset atrial fibrillation and related symptom burden or cardiorespiratory compromise. In hemodynamically stable patients with symptomatic atrial fibrillation, however, decisions as to whether immediate restoration of sinus rhythm in the individual patient is necessary, or whether spontaneous conversion can be expected, remains challenging in clinical practice because no objective decision guidance is available. An easy-to-use decision tool may offer the opportunity to personalize patient treatment already in a very early phase, facilitate shared decision-making processes, fasten acute therapeutic management and may reduce the length of stay in crowded Emergency Departments. The ReSinus score may especially support decisions on early cardioversion in patients in whom timely spontaneous conversion to sinus rhythm is unlikely to occur. Conversely, in patients with high probability of spontaneous conversion, the ReSinus score could strengthen delayed-cardioversion approaches as suggested by Pluymaekers et al and help avoiding treatment associated complications.
The particular strength of this study is its real-world-cohort structure. This suggests robust results that could prove valid beyond highly selected study populations. The Emergency Department at the Medical University of Vienna comprises an outpatient care section and an affiliated critical care unit allowing fast diagnostic work-up and the opportunity to rapidly perform cardioversion at any time. As this was a registry-based study, time to cardioversion attempts and observation periods were not standardized. It is thus conceivable that patients assigned to the non-spontaneous conversion cohort would have converted spontaneously over the following hours or days if they had not undergone early cardioversion to restore sinus rhythm. Some related bias cannot be excluded and should be considered when interpreting our findings. However, sensitivity analysis restricting the observational time-window from admission to two hours suggested robustness of the model. Furthermore, the median duration from admission to restoration of sinus rhythm was significantly longer in patients who were finally cardioverted than in those who converted spontaneously. At our institution, cardioversion of hemodynamically stable symptomatic AF patients, who do not early convert spontaneously, is performed as soon as exam results are available and a trial of fluid and/or potassium challenge has been performed to improve the probability of spontaneous conversion. This might be a common approach and reflect real-world practice in emergency settings, where ED-overcrowding and limited space for observation are common conditions. Yet, a future prospective trial should include a predefined observation schedule and follow-up period. In addition, the single-center design limits our results’ generalizability to different settings and populations. We followed the methodological guidance by Steyerberg and Verguowe and used the most contemporaneous set for validating our score. Geographical or strong external validation may provide more robust model validation, but was not be performed in this single-center study. Further validation of the developed score is needed to conclusively assess its performance.
As not only hypokalemia but also potassium levels in the low-normal range may be considered target conditions for potassium replacement, we included the combined information of low/low-normal potassium level (≤3.9mmol/l) and potassium substitution in the model. Patients with potassium levels exceeding 3.9mmol/l were not counted as potassium replacement. This needs to be noted, as clinical practice on potassium substitution in atrial fibrillation may vary between institutions and clinicians.
Finally, possible bias arising from missing data cannot be fully excluded. For some of the score variables data were missing in the derivation and in the validation set. We avoided strong assumptions on the missing data and included these as separate categories into our models. Yet, it needs to be acknowledged that this does not exclude bias associated with data missing not at random. Our results thus should be interpreted with appropriated caution.
5. Conclusions
The ReSinus score, which uses clinical and routine laboratory variables to predict spontaneous conversion to sinus rhythm, was developed and validated in a large cohort of patients with hemodynamically stable non-permanent symptomatic atrial fibrillation, showed good calibration, discrimination and clinical usefulness. Once validated, its implementation in clinical practice may aid physicians in guiding early cardioversion decisions while reducing the risk of overtreatment.
Authors
Jan Niederdoeckl, Alexander Simon, Filippo Cacioppo, Nina Buchtele, Anne Merrelaar, Nikola Schütz, Sebastian Schnaubelt, Alexander O. Spiel, Dominik Roth, Christian Schörgenhofer, Harald Herkner, Hans Domanovits, Michael Schwameis
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Conversion of recent onset paroxysmal atrial fibrillation to normal sinus rhythm: the effect of no treatment and high-dose amiodarone. A randomized, placebo-controlled study.
Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation.
Association between the N-terminal plasma brain natriuretic peptide levels or elevated left ventricular filling pressure and thromboembolic risk in patients with non-valvular atrial fibrillation.
B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies.
Europace: European pacing, arrhythmias, and cardiac electrophysiology: journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.2014; 16: 1426-1433
The novel biomarker-based ABC (age, biomarkers, clinical history)-bleeding risk score for patients with atrial fibrillation: a derivation and validation study.
Costs of atrial fibrillation in five European countries: results from the Euro Heart Survey on atrial fibrillation.
Europace: European pacing, arrhythmias, and cardiac electrophysiology: journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.2008; 10: 403-411