Comparison of six risk scores for the prediction of atrial fibrillation recurrence after cryoballoon-based ablation and development of a simplified method, the 0-1-2 PL score

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.

Novel scoring system derived from meta-analysis and validated in cohort population for predicting 1-year atrial fibrillation recurrence after cryoballoon catheter ablation: The HeLPS-Cryo score

BACKGROUND

Atrial fibrillation (AF) recurrence rates in 1 year after cryoballoon ablation catheter (CBCA) are still high. We purposed to identify strong predictors for AF recurrence after the successful CBCA procedure and develop a new scoring system based only on pre-procedural parameters.

METHODS

In the derivation phase, a systematic review and meta-analysis identified the strong predictors of AF recurrence after the CBCA. The pooled hazard ratio (HR) was used to create the new scoring system. The second phase validated the new scoring system in the cohort population.

RESULTS

A meta-analysis including 29 cohort studies with 16196 participants confirmed that persistent AF, stroke, heart failure, and left atrial diameter (LAD) >40 mm were powerful predictors for AF recurrence after the CBCA procedure. The HeLPS-Cryo (heart failure [1], left atrial dilatation [1], persistent AF [2], and stroke [2]) was developed based on those pre-procedural predictors. It was validated in 140 patients receiving CBCA procedures and revealed excellent predictive performance for 1-year AF recurrence (AUC = 0.8877; 95% CI = 0.8208 to 0.9546). The HeLPS-Cryo score of ≥3 could predict 1-year AF recurrence with sensitivity and specificity of 78.9% and 87.9%, respectively. The positive predictive value was 66.7%, and the negative predictive value was 93.1%.

CONCLUSION

The HeLPS-Cryo score can help the physician estimate the probability of 1-year AF recurrence after the successful CBCA procedure. Patients with HeLPS-Cryo score <3 are good candidates for the CBCA procedure.

Validation of the SCALE-CryoAF risk model to predict very late return of atrial fibrillation after cryoballoon ablation

BACKGROUND

To date, few risk models have been validated to predict recurrent atrial fibrillation (AF) >1 year after ablation. The SCALE-CryoAF score was previously derived to predict very late return of AF (VLRAF) >1 year following cryoballoon ablation (CBA), with strong predictive ability. In this study, we aim to validate the SCALE-CryoAF score for VLRAF after CBA in a novel patient cohort.

METHODS

Retrospective analysis of a prospectively maintained single-center database was performed. Inclusion criteria were pulmonary vein isolation using CBA 2017-2020. Exclusion criteria included prior ablation, <1-year follow-up, lack of pre-CBA echocardiogram, additional ablation lesion sets, and documented AF recurrence 90-365 days post-CBA. The area under the curve (AUC) of SCALE-CryoAF was compared to the derivation value and other established risk models.

RESULTS

Among 469 CBA performed, 241 (61% male, 62.8 ±11.7 years old) cases were included in analysis. There were 37 (15.4%) patients who developed VLRAF. Patients with VLRAF had a higher SCALE-CryoAF score (VLRAF 5.4 ± 2.7; no VLRAF 3.1 ± 2.9; p<0.001). SCALE-CryoAF was linearly associated with VLRAF (y=14.35x-11.72, R2=0.99), and a score > 5 had a 32.7% risk of VLRAF. The SCALE-CryoAF risk model predicted VLRAF with an AUC of 0.74, which was similar to the derivation value (AUCderivation: 0.73) and statistically superior to MB-LATER, CHA2DS2-VASc, and CHADS2 scores.

CONCLUSIONS

The current analysis validates the ability of SCALE-CryoAF to predict VLRAF after CBA in a novel patient cohort. Patients with a high SCALE-CryoAF score should be monitored closely for recurrent AF >1 year following CBA.

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis

Background

Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature.

Methods

We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type.

Results

After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables.

Conclusions

ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Development and validation of a novel nomogram for predicting recurrent atrial fibrillation after cryoballoon ablation

Background

Few studies have explored the use of machine learning models to predict the recurrence of atrial fibrillation (AF) in patients who have undergone cryoballoon ablation (CBA). We aimed to explore the risk factors for the recurrence of AF after CBA in order to construct a nomogram that could predict this risk.

Methods

Data of 498 patients who had undergone CBA at Ruijin Hospital, Shanghai Jiaotong University School of Medicine, were retrospectively collected. Factors such as clinical characteristics and biophysical parameters during the CBA procedure were collected for the selection of variables. Scores for all the biophysical factors-such as time to pulmonary vein isolation (TTI) and balloon temperature-were calculated to enable construction of the model, which was then calibrated and compared with the risk scores.

Results

A 36-month follow-up showed that 177 (35.5%) of the 489 patients experienced AF recurrence. The left atrial volume, TTI, nadir cryoballoon temperature, and number of unsuccessful freezes were related to the recurrence of AF (P < .05). The area under the curve (AUC) of the nomogram's time-dependent receiver operating characteristic curve was 77.6%, 71.6%, and 71.0%, respectively, for the 1-, 2-, and 3-year prediction of recurrence in the training cohort and 77.4%, 74.7%, and 68.7%, respectively, for the same characteristics in the validation cohort. Calibration and data on the nomogram's clinical effectiveness showed it to be accurate for the prediction of recurrence in both the training and validation cohorts as compared with established risk scores.

Conclusion

Biophysical parameters such as TTI and cryoballoon temperature have a great impact on AF recurrence. The predictive accuracy for recurrence of our nomogram was superior to that of conventional risk scores.

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.

P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA₂DS₂-VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.