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Predicting spontaneous conversion to sinus rhythm in symptomatic atrial fibrillation: The ReSinus score

Open AccessPublished:September 17, 2020DOI:https://doi.org/10.1016/j.ejim.2020.07.022

      Highlights

      • 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

      Registration

      NCT03272620

      Keywords

      1. Introduction

      In patients with symptomatic atrial fibrillation rates of spontaneous conversion are high.
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      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.
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      A prospective, randomized trial of an emergency department observation unit for acute onset atrial fibrillation.
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      • et al.
      Early or Delayed Cardioversion in Recent-Onset Atrial Fibrillation.
      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,
      • Nuotio I
      • Hartikainen JE
      • Gronberg T
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      Time to cardioversion for acute atrial fibrillation and thromboembolic complications.
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      Atrial fibrillation and heart failure: treatment considerations for a dual epidemic.
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      Atrial fibrillation management: a prospective survey in ESC member countries: the Euro Heart Survey on 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.
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      Arrhythmic complications of electrical cardioversion: relationship to shock energy.
      ,
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      A comparison of rate control and rhythm control in patients with atrial fibrillation.
      Given the rising prevalence
      • Krijthe BP
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      • et al.
      Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060.
      and economic burden
      • Rozen G
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      • et al.
      Emergency Department Visits for Atrial Fibrillation in the United States: Trends in Admission Rates and Economic Burden From 2007 to 2014.
      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.
      • Lane DA
      • Lip GY.
      Use of the CHA(2)DS(2)-VASc and HAS-BLED scores to aid decision making for thromboprophylaxis in nonvalvular atrial fibrillation.
      ,
      • Lip GY
      • Nieuwlaat R
      • Pisters R
      • Lane DA
      • Crijns HJ
      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
      • Schwameis M
      • Buchtele N
      • Schober A
      • Schoergenhofer C
      • Quehenberger P
      • Jilma B
      Prognosis of overt disseminated intravascular coagulation in patients admitted to a medical emergency department.
      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
      • Kirchhof P
      • Benussi S
      • Kotecha D
      • Ahlsson A
      • Atar D
      • Casadei B
      • et al.
      2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS.
      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.
      • Niederdockl J
      • Simon A
      • Schnaubelt S
      • Schuetz N
      • Laggner R
      • Sulzgruber P
      • et al.
      Cardiac biomarkers predict mortality in emergency patients presenting with atrial fibrillation.
      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 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.
      AF, atrial fibrillation.
      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
      • Steyerberg EW
      • Vergouwe Y
      Towards better clinical prediction models: seven steps for development and an ABCD for validation.
      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.
      • Steyerberg EW
      • Vickers AJ
      • Cook NR
      • Gerds T
      • Gonen M
      • Obuchowski N
      • et al.
      Assessing the performance of prediction models: a framework for traditional and novel measures.
      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 astruepositivesnfalsepositivesn(pt1pt), 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.
      • Vickers AJ
      • Elkin EB
      Decision curve analysis: a novel method for evaluating prediction models.
      The analyses followed the framework of derivation and validation of prediction models proposed by Steyerberg and Vergouwe.
      • Steyerberg EW
      • Vergouwe Y
      Towards better clinical prediction models: seven steps for development and an ABCD for validation.
      Reporting followed the TRIPOD statement.
      • Collins GS
      • Reitsma JB
      • Altman DG
      • Moons KG
      Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.
      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=1420Validation cohort n=1006p =
      available* (n)available* (n)
      Clinical Characteristics
      Age, years (IQR)68(57 - 76)142069(58 - 76)14200.190
      Female gender, n (%)615(43)472(47)0.065
      BMI, kg/m˄2 (IQR)28(25 - 30)142028(24 - 30)9840.824
      BP systolic, mmHG (IQR)134(120 - 150)1290134(120 - 150)9670.876
      BP diastolic, mmHG (IQR)82(72 - 95)127985(75 - 95)9620.034
      Heart rate, bpm (IQR)130(111 - 147)845130(111 - 146)6250.762
      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)6913(0 - 8)5060.373
      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)7606(2 - 19)5580.997
      CHA2DS2-VASc (IQR)3(1 - 4)13392(1 - 4)9610.029
      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)135142(38 - 45)9410.134
      WBC, G/l (IQR)8(7 - 10)13638(7 - 10)9500.860
      Creatinine, mg/dl (IQR)1(1 - 1)13611(1 - 1)9540.350
      Potassium, mmol/l (IQR)4.0(3.8 - 4.3)13214.0(3.8 - 4.3)9300.973
      LDH, U/l (IQR)196(167 - 238)1117198(169 - 240)7920.435
      NT-proBNP, pg/ml (IQR)943(303 - 2393)1172981(312 - 2739)8310.355
      hs-Troponin T, ng/l (IQR)14(8 - 25)109014(8 - 27)8210.663
      CRP, mg/dl (IQR)0(0 - 1)13180(0 - 1)9270.750
      Lactate, mmol/l (IQR)1(1 - 2)12382(1 - 2)8700.226
      INR, (IQR)2(1 - 3)7381(1 - 2)527<0.001
      TSH, μU/ml (IQR)2(1 - 3)9802(1 - 3)6910.492
      *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=186No Spontaneous Conversion n=1234p =
      available* (n)available* (n)
      Clinical Characteristics
      Age, years (IQR)68(56 - 76)18668(58 - 75)12340.490
      Male gender, n (%)108(58)697(56)0.626
      BMI, kg/m˄2 (IQR)28(25 - 30)18628(2 - 30)12340.315
      BP systolic, mmHG (IQR)133(120 - 150)168134(11 - 150)11220.728
      BP diastolic, mmHG (IQR)82(72 - 95)16782(7 - 95)11120.825
      Heart rate, bpm (IQR)136(117 - 149)142129(110 - 146)7030.012
      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)973(0 - 8)5940.058
      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)1337(2 - 24)627<0.001
      CHA2DS2-VASc (IQR)3(2 - 4)1822(1 - 4)11570.366
      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)18042(38 - 45)11710.237
      WBC, G/l (IQR)8(7 - 10)1838(7 - 10)11800.950
      Creatinine, mg/dl (IQR)1(1 - 1)1831(1 - 1)11780.094
      Potassium, mmol/l (IQR)3.9(3.7 - 4.1)1814.0(3.8 - 4.3)1140<0.001
      LDH, U/l (IQR)190(163 - 220)173198(167 - 241)9440.012
      NT-proBNP, pg/ml (IQR)516(22 - 1282)1641042(334 - 2574)1008<0.001
      hs-Troponin T, ng/l (IQR)15(8 - 25)14314(8 - 25)9470.406
      CRP, mg/dl (IQR)0(0 - 1)1760(0 - 1)11420.752
      Lactate, mmol/l (IQR)1(1 - 2)1771(1 - 2)10610.374
      INR, (IQR)1(1 - 2)972(1 - 3)641<0.001
      TSH, μU/ml (IQR)2(1- 3)1362(1 - 3)8440.456
      *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
      PredictorCoefficient95% CIp =Weighted Score
      Duration of AF symptoms <24 h0.89(0.53-1.25)<0.0012
      No previous electrical CV0.75(0.35-1.15)<0.0012
      Heart rate > 125 bpm0.55(0.22-0.88)<0.0011
      Potassium replacement at K+ level ≤3.9mmol/l0.55(0.21-0.89)0.0021
      NT-proBNP < 1300 pg/ml0.54(0.19-0.88)0.0031
      LDH < 200 U/l0.45(0.11-0.78)0.0091
      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 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 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.

      3.2 Validation of the ReSinus score

      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 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 5
      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).

      4. Discussion

      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.
      • Allessie MA
      • de Groot NM
      • Houben RP
      • Schotten U
      • Boersma E
      • Smeets JL
      • et al.
      Electropathological substrate of long-standing persistent atrial fibrillation in patients with structural heart disease: longitudinal dissociation.
      ,
      • Nattel S
      • Harada M
      Atrial remodeling and atrial fibrillation: recent advances and translational perspectives.
      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.
      • Ausma J
      • Wijffels M
      • Thone F
      • Wouters L
      • Allessie M
      • Borgers M
      Structural changes of atrial myocardium due to sustained atrial fibrillation in the goat.
      • Thijssen VL
      • Ausma J
      • Liu GS
      • Allessie MA
      • van Eys GJ
      • Borgers M
      Structural changes of atrial myocardium during chronic atrial fibrillation.
      • Zhang L
      • Huang B
      • Scherlag BJ
      • Ritchey JW
      • Embi AA
      • Hu J
      • et al.
      Structural changes in the progression of atrial fibrillation: potential role of glycogen and fibrosis as perpetuating factors.
      It has been shown experimentally that the duration of atrial fibrillation negatively affects its spontaneous conversion probability.
      • Wijffels MC
      • Kirchhof CJ
      • Dorland R
      • Allessie MA
      Atrial fibrillation begets atrial fibrillation.
      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
      • Padfield GJ
      • Steinberg C
      • Swampillai J
      • Qian H
      • Connolly SJ
      • Dorian P
      • et al.
      Progression of paroxysmal to persistent atrial fibrillation: 10-year follow-up in the Canadian Registry of Atrial Fibrillation.
      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
      • Yu GI
      • Cho KI
      • Kim HS
      • Heo JH
      • Cha TJ
      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.
      and the risk of both new-onset and recurrent atrial fibrillation.
      • Takase H
      • Dohi Y
      • Sonoda H
      • Kimura G
      Prediction of Atrial Fibrillation by B-type Natriuretic Peptide.
      • Sinner MF
      • Stepas KA
      • Moser CB
      • Krijthe BP
      • Aspelund T
      • Sotoodehnia N
      • et al.
      B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies.
      • Smith JG
      • Newton-Cheh C
      • Almgren P
      • Struck J
      • Morgenthaler NG
      • Bergmann A
      • et al.
      Assessment of conventional cardiovascular risk factors and multiple biomarkers for the prediction of incident heart failure and atrial fibrillation.
      • Svennberg E
      • Lindahl B
      • Berglund L
      • Eggers KM
      • Venge P
      • Zethelius B
      • et al.
      NT-proBNP is a powerful predictor for incident atrial fibrillation - Validation of a multimarker approach.
      • Zografos TA
      • Katritsis DG.
      Natriuretic Peptides as Predictors of Atrial Fibrillation Recurrences Following Electrical Cardioversion.
      Atrial natriuretic peptide is specifically expressed in the atria and may thus be an even more sensitive indicator of structural atrial disease
      • Buttner P
      • Schumacher K
      • Dinov B
      • Zeynalova S
      • Sommer P
      • Bollmann A
      • et al.
      Role of NT-proANP and NT-proBNP in patients with atrial fibrillation: Association with atrial fibrillation progression phenotypes.
      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.
      • Xu J
      • Xu X
      • Si L
      • Xue L
      • Zhang S
      • Qin J
      • et al.
      Intracellular lactate signaling cascade in atrial remodeling of mitral valvular patients with 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.
      • Hijazi Z
      • Lindback J
      • Alexander JH
      • Hanna M
      • Held C
      • Hylek EM
      • et al.
      The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation.
      • Hijazi Z
      • Oldgren J
      • Lindback J
      • Alexander JH
      • Connolly SJ
      • Eikelboom JW
      • et al.
      The novel biomarker-based ABC (age, biomarkers, clinical history)-bleeding risk score for patients with atrial fibrillation: a derivation and validation study.
      • Hijazi Z
      • Oldgren J
      • Lindback J
      • Alexander JH
      • Connolly SJ
      • Eikelboom JW
      • et al.
      A biomarker-based risk score to predict death in patients with atrial fibrillation: the ABC (age, biomarkers, clinical history) death risk score.
      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,
      • Lee WC
      • Lamas GA
      • Balu S
      • Spalding J
      • Wang Q
      • Pashos CL
      Direct treatment cost of atrial fibrillation in the elderly American population: a Medicare perspective.
      ,
      • Ringborg A
      • Nieuwlaat R
      • Lindgren P
      • Jonsson B
      • Fidan D
      • Maggioni AP
      • et al.
      Costs of atrial fibrillation in five European countries: results from the Euro Heart Survey on atrial fibrillation.
      mainly driven by treatment associated complications and costs of hospitalization.
      • Kirchhof P
      • Benussi S
      • Kotecha D
      • Ahlsson A
      • Atar D
      • Casadei B
      • et al.
      2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS.
      Emergency department visits and hospitalizations for atrial fibrillation are increasing,
      • Rozen G
      • Hosseini SM
      • Kaadan MI
      • Biton Y
      • Heist EK
      • Vangel M
      • et al.
      Emergency Department Visits for Atrial Fibrillation in the United States: Trends in Admission Rates and Economic Burden From 2007 to 2014.
      ,
      • Friberg J
      • Buch P
      • Scharling H
      • Gadsbphioll N
      • Jensen GB
      Rising rates of hospital admissions for atrial fibrillation.
      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.
      • Pluymaekers N
      • Dudink E
      • Luermans J
      • Meeder JG
      • Lenderink T
      • Widdershoven J
      • et al.
      Early or Delayed Cardioversion in Recent-Onset Atrial Fibrillation.

      4.4 Strengths and Limitations

      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:

      Declarations of interest

      none

      Appendix. Supplementary materials

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