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Nielsen A, Soerensen S, Skaarup K, Djernaes K, Estepar R, Hansen M, Worck R, Johannesen A, Hansen J, Biering-Soerensen T. Left atrial function assessed by speckle tracking echocardiography predicts atrial fibrillation burden after catheter ablation independently of reconduction: a RACE-AF echocardiographic sub-study. Eur Heart J Cardiovasc Imaging 2021. [DOI: 10.1093/ehjci/jeaa356.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
Left atrial (LA) function assessed by 2D speckle tracking echocardiography (STE) has demonstrated to be a useful predictor of recurrence of atrial fibrillation (AF) following catheter ablation (CA). Pulmonary vein reconduction (PVR) is one of the most important causes of recurrent paroxysmal AF (PAF) after ablation. The purpose of this study was to evaluate the association between AF burden (% of time in AF) following CA and LA strain measurements independently of PVR.
Methods
This prospective study included 66 patients with PAF who underwent CA (mean age 60 ± 8 years, 65% male). STE was performed during sinus rhythm prior to CA. AF burden was recorded by continuous rhythm monitoring using implantable loop recorders during a follow-up period of 4-6 months, excluding a blanking period of 3 months. After follow-up, all patients underwent an invasive assessment of pulmonary vein isolation to test for PVR. Multivariable linear regression analysis was used to assess the association between AF burden and peak atrial longitudinal reservoir strain (PALS), peak atrial contraction strain (PACS) and peak atrial conduit strain (PCS).
Results
Prior to CA, median AF burden was 3.8% (IQR: 0.5, 17). During follow-up, 37 patients (56%) were free of AF while median AF burden was 0.7% (IQR: 0.2, 1.6) in patients with an AF burden of more than 0%. A total of 35 patients (54%) were found to have PVR after ablation. Patients with AF recurrence had significantly lower PACS compared to patients with no AF during follow-up (10% ± 6% vs. 14% ± 5%, p = 0.004). No differences in PALS and PCS were observed. Increased PACS remained independently associated with low AF burden following CA after multivariable adjustments for clinical characteristics, comorbidities, and PVR (β=-0.262, p = 0.049) (Figure 1). PALS and PCS did not remain significantly associated with AF burden.
Conclusion
Increased PACS is strongly associated with low AF burden after CA even after adjusting for PVR. This suggests that an analysis of LA function could be useful to stratify patients prior to CA and improve treatment strategies.
Abstract Figure.
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Affiliation(s)
- A Nielsen
- Gentofte University Hospital, Copenhagen, Denmark
| | - S Soerensen
- Gentofte University Hospital, Copenhagen, Denmark
| | - K Skaarup
- Gentofte University Hospital, Copenhagen, Denmark
| | - K Djernaes
- Gentofte University Hospital, Copenhagen, Denmark
| | - R Estepar
- Brigham and Women"s Hospital, Boston, United States of America
| | - M Hansen
- Gentofte University Hospital, Copenhagen, Denmark
| | - R Worck
- Gentofte University Hospital, Copenhagen, Denmark
| | - A Johannesen
- Gentofte University Hospital, Copenhagen, Denmark
| | - J Hansen
- Gentofte University Hospital, Copenhagen, Denmark
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Simonsen J, Skaarup K, Djernaes K, Modin D, Lassen M, Grove G, Pedersen S, Estepar R, Martinez S, Gislason G, Biering-Soernsen T. Unsupervised machine learning generated clusters of left ventricular strain curves identifies patients in risk of heart failure and cardiovascular death following acute myocardial infarction. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Today myocardial deformation, also known as strain, is assessed by the global longitudinal strain (GLS) which only provides information about the maximal deformation during systole. Hence, a lot of information obtained from different patterns of deformation curves might be undiscovered. Unsupervised Machine leaning (uML) is capable of identifying similar patterns of deformation curves. Identifying different phenotypical patterns from myocardial deformation curves might provide insights into the pathophysiological development of cardiac disease and entail useful prognostic information.
Purpose
To investigate whether uML can group specific patterns of myocardial deformation curves which provide prognostic information on heart failure and/or cardiovascular death (HF/CVD) following ST-segment elevation myocardial infarction (STEMI).
Methods
A total of 319 STEMI patients had an echocardiogram performed at median 2 days after primary percutaneous coronary intervention (pPCI). Speckle tracking echocardiography analysis divided the left ventricle into 18 segments. Standardisation of the cardiac cycle was done using linear interpolation and complete strain data (mean of all segments) as function of time throughout the cardiac cycle was used as input for the uML algorithm. Clusters were identified using a K-means cluster analysis algorithm. Primary endpoint was the composite of heart failure (HF) and/or cardiovascular death (CVD). Median follow-up time was 1423 days (IQR: 91; 1660).
Results
Mean age was 62 years, 75% were male and 130 (41%) suffered incident HF/CVD during follow-up. The uML algorithm grouped patients into three clusters containing 97, 104, and 118 patients respectively. GLS curves of the three clusters are illustrated in the Figure 1. Incidence of HF/CVD increased significantly from cluster 1 through 3 (24% vs. 39% vs. 60%, P<0.001). In multivariable Cox regressions adjusting for the variables in the score risk chart model all three clusters were significantly associated with future HF/CVD (Figure 1). Cluster models provided significant incremental prognostic information when comparing C-statistics (0.64 vs. 0.62, p=0.029)
Conclusion
Unsupervised Machine Learning clusters of left ventricular deformation curves identifies patients in risk of HF/CVD following STEMI treated with pPCI, and provides incremental prognostic information to the score risk chart model.
Figure 1. GLS curves of the three clusters
Funding Acknowledgement
Type of funding source: None
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Affiliation(s)
| | | | - K Djernaes
- Gentofte Hospital, Copenhagen S, Denmark
| | - D Modin
- Gentofte Hospital, Copenhagen S, Denmark
| | | | - G.L Grove
- Gentofte Hospital, Copenhagen S, Denmark
| | - S Pedersen
- Gentofte Hospital, Copenhagen S, Denmark
| | - R.S.J Estepar
- Brigham and Women'S Hospital, Harvard Medical School, Applied Chest Imaging Laboratory, Boston, United States of America
| | | | - G Gislason
- Gentofte Hospital, Copenhagen S, Denmark
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