Key result
Machine learning-derived fractal features of the left atrium and pulmonary veins from CT scans predicted postablation AF recurrence with an AUROC of 0.81 (95% CI, 0.78–0.85).
Why the study?
To evaluate whether computerized morphological analysis of the left atrium and pulmonary veins via fractal measurements of shape and texture features could predict AF recurrence after ablation.
Does a machine learning model using fractal features from preablation CT scans predict atrial fibrillation recurrence postablation better than clinical parameters or LA volume?
Observational (n=203)
Does a machine learning model using fractal features from preablation CT scans predict atrial fibrillation recurrence postablation better than clinical parameters or LA volume?
Effect estimate: AUROC 0.81 (95% CI 0.78-0.85)
p-value: p=<0.001
Machine learning-derived fractal features from preablation CT scans, when combined with clinical parameters, accurately predict the risk of AF recurrence after ablation.
Fractal LA analysis was associated with post-ablation AF recurrence; leaves open prospective validation for risk stratification.
Background: We hypothesized that computerized morphological analysis of the left atrium (LA) and pulmonary veins (PVs) via fractal measurements of shape and texture features of the LA myocardial wall could predict atrial fibrillation (AF) recurrence after ablation. Methods: Preablation contrast computed tomography scans were collected for 203 patients who underwent AF ablation. The LA body, PVs, and myocardial wall were segmented using a semi-automated region growing method. Twenty-eight fractal-based shape and texture-based features were extracted from resulting segments. The top features most associated with postablation recurrence were identified using feature selection and subsequently evaluated with a Random Forest classifier. Feature selection and classifier construction were performed on a discovery cohort (D 1 ) of 137 patients; classifiers were subsequently validated on an independent set (D 2 ) of 66 patients. Dedicated classifiers to capture the fractal and morphological properties of LA body (C LA ), PVs (C PV ), and LA myocardial (C LAM ) tissue were constructed, as well as a model (C All ) capturing properties of all segmented compartments. Fractal-based models were also compared against a model employing machine estimation of LA volume. To assess the effect of clinical parameters, such as AF type and catheter technique, a clinical model (C clin ) was also compared against C All . Results: Statistically significant differences were observed for fractal features of C LA , C LAM , and C All in distinguishing AF recurrence ( P <0.001) on D 1 . Using the 5 top features, C All had the best prediction performance (area under the receiver operating characteristic curve [AUROC], 0.81 [95% CI, 0.78–0.85]), followed by C PV (AUROC, 0.78 [95% CI, 0.74–0.80]), and C LA (AUROC, 0.70 [95% CI, 0.63–0.78]) on D 2 . The clinical parameter model C clin yielded an AUROC, 0.70 (95% CI, 0.65–0.77), while the atrial volume model yielded an AUROC, 0.59. Combining C All and C clin on D 2 improved the AUROC to 0.87 (95% CI, 0.82–0.93). Conclusions: Fractal measurements of the LA, PVs, and atrial myocardium on computed tomography scans were associated with likelihood of postablation AF recurrence.
No takes yet. Share an insight, caveat, or question.
Firouznia et al. (2021) conducted an observational in Atrial fibrillation (n=203). Machine learning-derived fractal features of shape and texture of the left atrium and pulmonary veins vs. Clinical parameter model and atrial volume model was evaluated on Postablation atrial fibrillation recurrence (AUROC 0.81, 95% CI 0.78-0.85, p=<0.001). Machine learning-derived fractal features of the left atrium and pulmonary veins from CT scans predicted postablation AF recurrence with an AUROC of 0.81 (95% CI, 0.78–0.85).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: