Atrial flutter and fibrillation in patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in the ASPIRE registry: Comparison of rate versus rhythm control approaches

BACKGROUND

The development of atrial flutter and fibrillation (AFL/AF) in patients with pre-capillary pulmonary hypertension has been associated with an increased risk of morbidity and mortality. Rate and rhythm control strategies have not been directly compared.

METHODS

Eighty-four patients with pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension (CTEPH) with new-onset AFL/AF were identified in the ASPIRE registry. First, baseline characteristics and rates of sinus rhythm (SR) restoration of 3 arrhythmia management strategies (rate control, medical rhythm control and DC cardioversion, DCCV) in an early (2009-13) and later (2014-19) cohort were compared. Longer-term outcomes in patients who achieved SR versus those who did not were then explored.

RESULTS

Sixty (71%) patients had AFL and 24 (29%) AF. Eighteen (22%) patients underwent rate control, 22 (26%) medical rhythm control and 44 (52%) DCCV. SR was restored in 33% treated by rate control, 59% medical rhythm control and 95% DCCV (p < 0.001). Restoration of SR was associated with greater improvement in functional class (FC) and Incremental Shuttle Walk Distance (p both <0.05). It also independently predicted superior survival (3-year survival 62% vs 23% in those remaining in AFL/AF, p < 0.0001). In addition, FC III/IV independently predicted higher mortality (HR 2.86, p = 0.007). Right atrial area independently predicted AFL/AF recurrence (OR 1.08, p = 0.01). DCCV was generally well tolerated with no immediate major complications.

CONCLUSIONS

Restoration of SR is associated with superior functional improvement and survival in PAH/CTEPH compared with rate control. DCCV is generally safe and is more effective than medical therapy at achieving SR.

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning

Introduction

Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.

Methods

A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.

Results

Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases.

Conclusion

Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.