Comparison of the predictive value of ten risk scores for outcomes of atrial fibrillation patients undergoing radiofrequency pulmonary vein isolation

A new scoring system to predict the risk of late recurrence in extended follow-up after atrial fibrillation catheter ablation: APCEL score

Background

In studies where risk scores used to determine the risk of late recurrence after atrial fibrillation (AF) catheter ablation were defined, significant differences were observed in terms of parameters such as post-procedural follow-up time, pre-procedural AF time, energy sources used for ablation, and cut-off values of left atrium (LA) diameter. Considering all these factors, we aimed to develop a new recurrence risk score for prolonged follow-up after AF ablation.

Methods

The study included 206 patients who underwent index AF catheter ablation for paroxysmal or persistent AF. Independent predictors of late recurrence were identified at a median follow-up of 40 months (range: 21-57), and a risk score was created. The predictive ability of this score for late recurrence was compared with that of other risk scores.

Results

Independent predictors of late recurrence development included pre-ablation AF duration >19 months, persistent AF, early recurrence, chronic obstructive pulmonary disease, and LA volume index >31 mL/m2. The APCEL risk score, derived from these factors (Early recurrence: 3 points, AF duration >19 months: 2 points, others: 1 point), demonstrated good predictive performance for late recurrence at 6th [AUC: 0.940, 95% CI: 0.896-0.983], 12th [AUC: 0.865, 95% CI: 0.796-0.932], 24th [AUC: 0.814, 95% CI: 0.743-0.885], and 36th months [AUC: 0.798, 95% CI: 0.726-0.868].

Conclusions

The APCEL score, calculated at the end of the blanking period for patients who underwent AF ablation, can effectively identify those at high risk of late recurrence during extended follow-up.

Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review

BACKGROUND

Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA).

OBJECTIVE

This scoping review aimed to evaluate the current scientific evidence on the application of ML for managing patients undergoing AFCA, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field.

METHODS

Adhering to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, relevant studies published up to October 7, 2023, were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. The PROBAST (Prediction model Risk Of Bias Assessment Tool) and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) methodological quality assessment tools were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies.

RESULTS

The analysis of 23 included studies showcased the contributions of ML in identifying potential ablation targets, improving ablation strategies, and predicting patient prognosis. The patient data used in these studies comprised demographics, clinical characteristics, various types of imaging (9/23, 39%), and electrophysiological signals (7/23, 30%). In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models (14/23, 61%) showed a high risk of bias due to lack of external validation.

CONCLUSIONS

Our evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of managing patients undergoing AFCA. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation, and to further explore model generalization and interpretability.

Role of a new inflammation predictor in predicting recurrence of atrial fibrillation after radiofrequency catheter ablation

BACKGROUND

Radiofrequency catheter ablation (RFCA) has become an important strategy for treating atrial fibrillation (AF), and postoperative recurrence represents a significant and actively discussed clinical concern. The recurrence after RFCA is considered closely related to inflammation. Systemic immune inflammation index (SII) is a novel inflammation predictor based on neutrophils, platelets, and lymphocytes, and is considered a biomarker that comprehensively reflects the immune inflammatory status of the body.

AIM

To explore the predictive effect of the SII on AF recurrence after RFCA and its predictive value in combination with the existing APPLE score for AF recurrence after RFCA in patients with non-valvular AF (NVAF).

METHODS

We retrospectively included 457 patients with NVAF first receiving RFCA and classified them into the recurrent or non-recurrent group. We also investigated the predictive role of SII on AF recurrence following RFCA. Finally, we explored and compared the additional predictive value of the SII after combining with the APPLE score.

RESULTS

After 12 months of follow-up, 113 (24.7%) patients experienced recurrence. High SII has been demonstrated to be an independent predictor for postoperative AF recurrence. Receiver operating characteristic and decision curve analysis (DCA), as well as net reclassification improvement (NRI) and integrated discrimination improvement (IDI) results, showed that SII combined with the APPLE score had higher predictive efficiency than using the SII or APPLE score alone. The area under the curve of the combined model (0.662, 95% confidence interval: 0.602-0.722) significantly increased compared with that of the SII and APPLE scores alone (P < 0.001). The combined model resulted in an NRI of 29.6% and 34.1% and IDI of 4.9% and 3.5% in predicting AF recurrence compared with the SII and APPLE scores alone, respectively (all P < 0.001). The SII, APPLE score, and their combination demonstrated greater clinical utility than did the treat-all and treat-none strategies over the 20-80% risk threshold according to the DCA.

CONCLUSION

The SII was a predictor of recurrence after RFCA of AF. Moreover, the SII enhanced the predictability of the APPLE score for post-RFCA AF recurrence, providing valuable insights for physicians to optimise patient selection and develop personalised treatment plans.

Better performance of the APPLE score for the prediction of very early atrial fibrillation recurrence post-ablation

OBJECTIVE

The benefits of rhythm control in early atrial fibrillation (AF) are increasingly recognized. This study aimed to investigate whether early AF ablation contributes to long-term sinus rhythm maintenance and to identify a suitable predictive score.

METHODS

According to diagnosis-to-ablation time, this study prospectively enrolled 245 patients with very early AF, 262 with early AF, and 588 with late AF for radiofrequency ablation from June 2017 to December 2022. Clinical data, risk scores, and follow-up results were collected and analyzed.

RESULTS

Baseline characteristics were similar among the three cohorts. During a median follow-up period of 26 months, AF recurrence was observed in 61 (24.9%), 66 (25.2%), and 216 (36.7%) patients in the very early, early, and late AF cohorts, respectively. In the multivariable-adjusted model, very early and early AF were associated with a reduced risk of AF recurrence, with hazard ratios of 0.72 (95% confidence interval [CI] 0.52-0.99) and 0.57 (95% CI 0.41-0.78), respectively. The APPLE score demonstrated the highest predictive power for very early AF, with an area under the curve (AUC) of 0.74. However, its predictive power decreased with time from diagnosis, showing low predictive power for late AF (AUC = 0.58). In addition, the time-dependent concordance index showed consistent results. For very early AF, the Akaike information criterion and decision curve analysis showed that APPLE had the highest predictive value.

CONCLUSION

Very early AF ablation was associated with a lower recurrence rate, and the APPLE score provided a higher predictive value for these patients. (URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR-OIN-17013021).

Digital Electrocardiographic Complex for Risk Stratification of Paroxysmal Atrial Fibrillation

The aim of the study was to develop and clinically test a hardware and software system capable of identifying the predictors of the hidden forms of atrial fibrillation (AF) using 12-lead ECG data in sinus rhythm.

Materials and Methods

There was developed the hardware and software system "Intecard 8.1" to assess a set of markers for atrial electrical instability by 3-5-minute ECG recordings in sinus rhythm. The markers include P-wave amplitude in lead II <0.1 mV, P-wave duration >120 ms, advanced interatrial block, the area of the biphasic P-wave terminal part <-4 mV·ms, and MVP (morphology-voltage- P-wave duration) score >3 points.The clinical testing of "Intecard 8.1" system was carried out on 120 patients with ischemic heart disease or dilated cardiomyopathy. The patients' average age was 57.9±13.1 years.

Results

P-wave detection is a challenging task due to a low signal amplitude, noise, high error probability in atrioventricular block or T-wave and P-wave superposition in case of marked tachycardia. To improve detection, a phase transformation method was used, according to which there was studied its phase component arctg[x(n)/Rv], where x(n) - ECG signal samples, Rv - a constant. We developed an identification algorithm implemented in "Intecard 8.1" software, its clinical trials being conducted.During the 12 [6; 22] month observation period, AF episodes were recorded in 22 from 120 patients (18.3%). The patients with AF episodes exhibited a significant decrease in P-wave amplitude (p=0.029), its duration increase (p<0.001), and a significantly high MVP score (p<0.01). The MVP score with a cut-off point >3 points is of the highest prognostic significance. The area under the ROC curve AUC was 0.988 with a 95% confidence interval: 0.975-0.999 (p<0.001). The prediction model of hidden AF paroxysms has sensitivity and specificity: 92 and 89%, respectively.

Conclusion

The digital electrocardiographic complex "Intecard 8.1" when analyzing 3-5-minute ECG recordings with sinus rhythm enables to identify the patients with high risk or with hidden AF forms. The dynamic assessment of P-wave parameters offers an opportunity to personalize heart rhythm control in this patient cohort.

Influence of ABC stroke score on late recurrence of paroxysmal atrial fibrillation following radiofrequency catheter ablation

BACKGROUND

In this study we investigated the impact of ABC stroke score on the recurrence of paroxysmal atrial fibrillation (PAF) following radiofrequency catheter ablation (RFCA).

METHODS

A total of 132 patients with PAF who underwent RFCA from October 2018 to September 2019 were included in this study. During the first phase of this study the patients were categorized into two groups based on late recurrence of atrial fibrillation after RFCA. In the second phase, the patients were further divided into two groups based on whether their ABC stroke score was ≥ 6.5.

RESULT

The univariate analysis indicated that the risk factors for late recurrence of PAF included early recurrence, ABC stroke score, CHA2DS2-VASc score, and NT-proBNP (P < 0.05). Cox multivariate regression analysis revealed that ABC stroke score (P = 0.006) and early recurrence (P = 0.000) were independent predictors of late recurrence, and ABC stroke score ≥ 6.5 was a risk for predicting recurrence of PAF after RFCA with a sensitivity of 66.7% and specificity of 65.7%. After the completion of the 1:1 matching, the univariate Cox analysis indicated that an elevated score of ABC stroke (≥ 6.5) was an independent predictor of late recurrence of PAF (HR = 2.687, 95% CI: 1.036-6.971, P = 0.042). However, using an ABC stroke score cut off at 6.4 predicted the recurrence of atrial tachyarrhythmia with 85% sensitivity and 58.5% specificity.

CONCLUSION

An ABC stroke score ≥ 6.4 is a predictor for late recurrence of PAF after RFCA.

Multidisciplinary management strategies for atrial fibrillation

There has been a significant increase in the prevalence of atrial fibrillation (AF) over the past 30 years. Pulmonary vein isolation (PVI) is an effective treatment for AF, but research investigations have shown that AF recurrence still occurs in a significant number of patients after ablation. Heart rhythm outcomes following catheter ablation are correlated with numerous clinical factors, and researchers developed predictive models by integrating risk factors to predict the risk of recurrence of atrial fibrillation. The purpose of this article is to outline the risk scores for predicting cardiac rhythm outcomes after PVI and to discuss the modifiable factors that increase the risk of recurrence of AF, with the hope of further improving catheter ablation efficacy through preoperative identification of high-risk populations and postoperative management of modifiable risk factors.

Usefulness of risk scores and predictors of atrial fibrillation recurrence after elective electrical cardioversion

INTRODUCTION

Electrical cardioversion (ECV) is a frequently used procedure for restoring sinus rhythm in atrial fibrillation (AF); however, the rate of recurrence is high. The identification of patients at high risk of recurrence could influence the decision-making process. The present study evaluates the predictive value of risk scores in atrial fibrillation recurrence after elective electrical cardioversion.

METHODS

Unicentric, observational, and prospective study of adult patients who have undergone an elective ECV as rhythm control strategy between July 2017 and September 2022.

RESULTS

From the 283 analyzed patients (mean age 63.95 ± 10.76212, 74.9% male); 99 had paroxysmal AF (35%) and 159 (59%) presented AF recurrence during a follow-up of 6 months. In patients with post-ECV AF recurrence, the period of time from diagnosis until the performance of the procedure was longer (393 ± 891 vs. 195 ± 527, p = .02). No paroxysmal AF (71.3% vs. 57.8%, p = .02) and LA dilatation with >40 mL/m2 (35.9% vs. 23.3%, p = .02) volumes were more frequent within these patients. AF recurrence was more frequent in patients who had previous ECV (HR = 1.32; 95% CI: 1.12-2.35; p = .01) and more than 1 shock to recover sinus rhythm (HR = 1.62; 95% CI: 1.07-1.63; p = .01). The SLAC, ALARMEc, ATLAS, and CAAP-AF scores were statistically significant, although with a moderate predictive capacity for post-ECV recurrence.

CONCLUSIONS

Risk scores analyzed showed a modest value predicting AF recurrence after ECV. Previous ECV, and greater difficulty in restoring SR were independent predictors of recurrence.

Platelet-to-Lymphocyte Ratio Improves the Predictive Ability of the Risk Score for Atrial Fibrillation Recurrence After Radiofrequency Ablation

Purpose

To investigate the effect and comprehensive predictive value of the platelet-to-lymphocyte ratio (PLR) for long-term recurrence in patients with atrial fibrillation (AF) post ablation.

Patients and Methods

We retrospectively analysed 638 consecutive AF patients who underwent ablation, including 302 (47.3%) with paroxysmal AF and 336 (52.7%) with nonparoxysmal AF. Patients were grouped into the recurrence and nonrecurrence groups.

Results

After a mean follow-up of 15.1±9.3 months, 175 patients (27.4%) with AF had long-term recurrence, including 114 patients (33.9%) with nonparoxysmal AF and 61 patients (20.2%) with paroxysmal AF. In the entire cohort and in patients with nonparoxysmal AF, but not in those with paroxysmal AF, the PLR was significantly higher in the recurrence group than in the nonrecurrence group (P<0.05). After adjusting for the APPLE score, the PLR as a continuous variable independently predicted AF recurrence (hazard ratio [HR], 1.003; 95% confidence interval [CI], 1.001-1.005; P<0.01). The addition of the PLR to the APPLE score improved its predictive ability for recurrence (the C-statistic value increased from 0.645 to 0.675, P=0.02; the net reclassification improvement was 0.221, 95% CI 0.049-0.394, P=0.01; and the integrated discrimination improvement was 0.029, 95% CI 0.013-0.045, P<0.01). For nonparoxysmal AF, the PLR was stratified into tertiles, the PLR independently increased the nonparoxysmal AF recurrence risk after adjusting for multiple confounding factors (HR, 1.393; 95% CI, 1.102-1.762; P<0.01), and the addition of the PLR to the left atrial diameter improved its predictive ability for arrhythmia recurrence (the C-statistic value increased from 0.601 to 0.667, P<0.01).

Conclusion

The PLR is an independent predictive factor of long-term AF recurrence post ablation after adjusting for the APPLE score and can improve the predictive ability and clinical usefulness of the APPLE score. However, the PLR is an effective predictor of recurrence in patients with nonparoxysmal AF rather than in paroxysmal AF.

Urinary isoxanthopterin as a novel predictor following catheter ablation for atrial fibrillation

Background

Oxidative stress is associated with atrial fibrillation recurrence following catheter ablation. Urinary isoxanthopterin (U-IXP) is one of the noninvasive markers which reflect the reactive oxygen species; however, its ability to predict atrial tachyarrhythmias (ATAs) occurrence following catheter ablation remains uncertain.

Methods

Among the patients who received scheduled catheter ablation for atrial fibrillation, baseline U-IXP levels were measured just before the procedure. The prognostic impact of baseline U-IXP upon postprocedural ATAs occurrence was investigated.

Results

Among 107 patients (71 years old, 68% men), baseline U-IXP level was 0.33 nmol/gCr on the median. During a mean of 603 days of follow-up, 32 patients had ATAs. Baseline higher U-IXP was independently associated with the occurrence of ATAs following catheter ablation with a hazard ratio of 4.69 (95% confidence interval: 1.82-12.37, p  = .001) adjusted for the left atrial diameter, a persistent type, and hypertension which were potential confounders, with a cutoff of 0.46 nmol/gCr, which stratified cumulative incidence of ATAs occurrence ( p  < .001).

Conclusion

U-IXP can be used as the noninvasive predictive biomarker for ATAs following catheter ablation for atrial fibrillation.

Validation of seven risk scores in an independent cohort: the challenge of predicting recurrence after atrial fibrillation ablation

Purpose

Several predictive scores for atrial fibrillation (AF) recurrence after AF ablation have been developed. We compared the predictive value of seven previously described risk scores ((CHADS2 and CHA2DS2-VASC, HATCH, APPLE, CAAP-AF, BASE-AF2, MB-LATER) for prediction of AF recurrence risk at 12 months after AF ablation in our patient cohort. Further, we aimed to identify additional variables to predict recurrences after AF ablation.

Methods

We used data from our digital AF ablation registry to compare the previously published scores in an independent cohort (n = 883, 50.8% with paroxysmal AF). The scores were chosen based on earlier publications and availability of relevant data.

Results

The BASE-AF2 (AUC 0.630, p < 0.001), MB-LATER (AUC 0.612, p < 0.001), CAAP-AF (AUC 0.591, p < 0.001), APPLE (AUC 0.591, p < 0.001) and CHA2DS2-VASC (AUC 0.547, p = 0.018) scores had a statistically significant but modest predictive value for 12-month AF recurrence. None of the scores were significantly superior. Other analyzed scores had no predictive value. There was no difference in the predictive value for 12-month recurrence of AF between first procedure vs. redo procedure and RF ablation vs. cryoablation. Unlike other scores, MB-LATER showed better predictive value for paroxysmal vs. persistent AF (AUC 0.632 vs. 0.551, p = 0.038). In the multivariate logistic regression, only age (p = 0.006), number of prior electrical cardioversions (p < 0.001) and early AF recurrence (p < 0.001) were independent predictors of AF recurrence.

Conclusion

Despite numerous available scores, predicting recurrences after AF ablation remains challenging. New predictors are needed, potentially based on interventions, as well as novel genetic, functional and anatomic parameters.

A new scoring system: PAT2C2H score. Its clinical use and comparison with HATCH and CHA2DS2-VASc scores in predicting arrhythmia recurrence after cryoballoon ablation of paroxysmal atrial fibrillation

BACKGROUND

Several clinical risk factors and scoring systems have been proposed to predict arrhythmia recurrence after atrial fibrillation (AF) ablation. We sought to determine the ability of a new score to predict atrial arrhythmia recurrence after cryoballoon (CB) ablation of AF and whether the new score shows superior efficiency compared to previously offered scores.

METHODS

A total of 419 patients with paroxysmal AF who underwent their first CB ablation were included. Baseline clinical variables were analyzed, and independent predictors of recurrence at 12 months were used to develop the PAT₂C₂H score. The predictive capability of the new score was calculated and compared with the currently available risk scores.

RESULTS

Chronic obstructive pulmonary disease, left atrial dilatation, transient ischemic attack or stroke, congestive heart failure, and hypertension were independent predictors of recurrence. The PAT₂C₂H score which was developed from these variables had a better clinical predictive capability of arrhythmia recurrence compared to HATCH and CHA₂DS₂-VASc scores. With increasing PAT₂C₂H score and score severity (low, score of 0; moderate, score of 1-2; and high, score of ≥ 3), the proportion of patients with recurrence was increased from 7% (score = 0, severity = low) to 59% (score ≥ 3, severity = high).

CONCLUSIONS

The PAT₂C₂H score may help to identify patients who are likely benefited most from CB ablation of paroxysmal AF and who should be monitored more closely for arrhythmia recurrence at 12 months.

Development and Validation of a Risk Nomogram Model for Predicting Recurrence in Patients with Atrial Fibrillation After Radiofrequency Catheter Ablation

Purpose

This study aimed to develop and validate a risk nomogram model for predicting the risk of atrial fibrillation recurrence after radiofrequency catheter ablation.

Patients and Methods

A retrospective observational study was conducted using data from 485 patients with atrial fibrillation who underwent the first radiofrequency ablation in our hospital from January 2018 to June 2021. All patients were randomized into training cohort (70%; n=340) and validation cohort (30%; n=145). Univariate and multivariate logistic regression analyses were used to identify independent risk factors. The predictive nomogram model was established by using R software. The nomogram was developed and evaluated based on differentiation, calibration, and clinical efficacy by concordance statistic (C-statistic), calibration plots, and decision curve analysis (DCA), respectively.

Results

The nomogram was established by four variables including left atrial diameter (OR 1.057, 95% CI 1.010–1.107, P=0.018), left ventricular ejection fraction (OR 0.943, 95% CI 0.905–0.982, P=0.005), type of atrial fibrillation (OR 2.164, 95% CI: 1.262–3.714), and systemic inflammation score (OR 1.905, 95% CI 1.408–2.577). The C-statistic of the nomogram was 0.741 (95% CI: 0.689–0.794) in the training cohort and 0.750 (95% CI: 0.670–0.831) in the validation cohort. The calibration plots showed good agreement between the predictions and observations in the training and validation cohorts. Decision curve analysis and clinical impact curves indicated the clinical utility of the predictive nomogram.

Conclusion

The nomogram model has good discrimination and accuracy, which can screen high-risk groups intuitively and individually, and has a certain predictive value for atrial fibrillation recurrence in patients after radiofrequency ablation.

Predicting Late Recurrence of Atrial Fibrillation After Radiofrequency Ablation in Patients With Atrial Fibrillation: Comparison of C2HEST and HATCH Scores

Objective

This study was aimed to investigate the risk of recurrence in patients with atrial fibrillation (AF) after radiofrequency ablation and predict risk of recurrence using C2HEST and HATCH scores.

Methods

We retrospectively included 322 patients with AF from Second Hospital of Lanzhou University, and 261 patients were included in the analysis finally. They had AF and were admitted for radiofrequency catheter ablation. We compared the ability of C2HEST and HATCH scores to predict recurrence after radiofrequency ablation of AF. The predictive ability of C2HEST and HATCH scores for AF recurrence was estimated by the area under the receiver operating characteristic curve (AUROC). The difference in receiver operating characteristic curve between the two models was compared using the DeLong test.

Results

Of the 261 patients included in the analysis, 83 (31.6%) patients suffered a late recurrence of AF after radiofrequency ablation. The risk of postoperative recurrence of AF increased with increasing C2HEST and HATCH scores. The AUROC of C2HEST and HATCH scores in predicting postoperative recurrence of AF was 0.773 (95%CI, 0.713–0.833) and 0.801 (95% CI, 0.740–0.861), respectively. There was no significant difference between the two models in their ability to evaluate patients for postoperative recurrence of AF (DeLong test p-value = 0.36). Among the risk factors in both models, hypertension and heart failure (HF) contributed the most to postoperative recurrence after AF, and higher blood pressure and lower cardiac ejection fraction (EF) were associated with a higher risk of recurrence.

Conclusion

Both C2HEST and HATCH scores were significantly associated with the risk of late recurrence after radiofrequency ablation of AF. Besides hypertension and HF contributed the most to postoperative recurrence after AF.