Dataset Information

Prognostic Significance of Premature Atrial Complexes Burden in Prediction of Long-Term Outcome.

ABSTRACT:

Background

The prognostic significance of premature atrial complex (PAC) burden is not fully elucidated. We aimed to investigate the relationship between the burden of PACs and long-term outcome.

Methods and results

We investigated the clinical characteristics of 5371 consecutive patients without atrial fibrillation (AF) or a permanent pacemaker (PPM) at baseline who underwent 24-hour electrocardiography monitoring between January 1, 2002, and December 31, 2004. Clinical event data were retrieved from the Bureau of National Health Insurance of Taiwan. During a mean follow-up duration of 10±1 years, there were 1209 deaths, 1166 cardiovascular-related hospitalizations, 3104 hospitalizations for any reason, 418 cases of new-onset AF, and 132 PPM implantations. The optimal cut-off of PAC burden for predicting mortality was 76 beats per day, with a sensitivity of 63.1% and a specificity of 63.5%. In multivariate analysis, a PAC burden >76 beats per day was an independent predictor of mortality (hazard ratio: 1.384, 95% CI: 1.230 to 1.558), cardiovascular hospitalization (hazard ratio: 1.284, 95% CI: 1.137 to 1.451), new-onset AF (hazard ratio: 1.757, 95% CI: 1.427 to 2.163), and PPM implantation (hazard ratio: 2.821, 95% CI: 1.898 to 4.192). Patients with frequent PAC had increased risk of mortality attributable to myocardial infarction, heart failure, and sudden cardiac death. Frequent PACs increased risk of PPM implantation owing to sick sinus syndrome, high-degree atrioventricular block, and/or AF.

Conclusions

The burden of PACs is independently associated with mortality, cardiovascular hospitalization, new-onset AF, and PPM implantation in the long term.

SUBMITTER: Lin CY

PROVIDER: S-EPMC4599506 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

Prognostic Significance of Premature Atrial Complexes Burden in Prediction of Long-Term Outcome.

Lin Chin-Yu CY Lin Yenn-Jiang YJ Chen Yun-Yu YY Chang Shih-Lin SL Lo Li-Wei LW Chao Tze-Fan TF Chung Fa-Po FP Hu Yu-Feng YF Chong Eric E Cheng Hao-Min HM Tuan Ta-Chuan TC Liao Jo-Nan JN Chiou Chuen-Wang CW Huang Jin-Long JL Chen Shih-Ann SA

Journal of the American Heart Association 20150827 9

<h4>Background</h4>The prognostic significance of premature atrial complex (PAC) burden is not fully elucidated. We aimed to investigate the relationship between the burden of PACs and long-term outcome.<h4>Methods and results</h4>We investigated the clinical characteristics of 5371 consecutive patients without atrial fibrillation (AF) or a permanent pacemaker (PPM) at baseline who underwent 24-hour electrocardiography monitoring between January 1, 2002, and December 31, 2004. Clinical event dat ...[more]

PMID: 26316525

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Project description:Atrial stretch may contribute to the mechanism of atrial fibrillation (AF) recurrence after atrial fibrillation catheter ablation (AFCA). We tested whether the left atrial (LA) wall stress (LAW-stress[measured]) could be predicted by artificial intelligence (AI) using non-invasive parameters (LAW-stress[AI]) and whether rhythm outcome after AFCA could be predicted by LAW-stress[AI] in an independent cohort. Cohort 1 included 2223 patients, and cohort 2 included 658 patients who underwent AFCA. LAW-stress[measured] was calculated using the Law of Laplace using LA diameter by echocardiography, peak LA pressure measured during procedure, and LA wall thickness measured by customized software (AMBER) using computed tomography. The highest quartile (Q4) LAW-stress[measured] was predicted and validated by AI using non-invasive clinical parameters, including non-paroxysmal type of AF, age, presence of hypertension, diabetes, vascular disease, and heart failure, left ventricular ejection fraction, and the ratio of the peak mitral flow velocity of the early rapid filling to the early diastolic velocity of the mitral annulus (E/Em). We tested the AF/atrial tachycardia recurrence 3 months after the blanking period after AFCA using the LAW-stress[measured] and LAW-stress[AI] in cohort 1 and LAW-stress[AI] in cohort 2. LAW-stress[measured] was independently associated with non-paroxysmal AF (p < 0.001), diabetes (p = 0.012), vascular disease (p = 0.002), body mass index (p < 0.001), E/Em (p < 0.001), and mean LA voltage measured by electrogram voltage mapping (p < 0.001). The best-performing AI model had acceptable prediction power for predicting Q4-LAW-stress[measured] (area under the receiver operating characteristic curve 0.734). During 26.0 (12.0-52.0) months of follow-up, AF recurrence was significantly higher in the Q4-LAW-stress[measured] group [log-rank p = 0.001, hazard ratio 2.43 (1.21-4.90), p = 0.013] and Q4-LAW-stress[AI] group (log-rank p = 0.039) in cohort 1. In cohort 2, the Q4-LAW-stress[AI] group consistently showed worse rhythm outcomes (log-rank p < 0.001). A higher LAW-stress was associated with poorer rhythm outcomes after AFCA. AI was able to predict this complex but useful prognostic parameter using non-invasive parameters with moderate accuracy.

Project description:BackgroundAtrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores.Methods and resultsWe retrospectively identified Veterans Health Administration serviced patients with cardiac implantable electronic device remote monitoring data and at least one day of device-registered AF. The first 30 days of remote monitoring in nonstroke controls were compared against the past 30 days of remote monitoring before stroke in cases. We trained 3 types of models on our data: (1) convolutional neural networks, (2) random forest, and (3) L1 regularized logistic regression (LASSO). We calculated the CHA2DS2-VASc score for each patient and compared its performance against machine learned indices based on AF burden in separate test cohorts. Finally, we investigated the effect of combining our AF burden models with CHA2DS2-VASc. We identified 3114 nonstroke controls and 71 stroke cases, with no significant differences in baseline characteristics. Random forest performed the best in the test data set (area under the curve [AUC]=0.662) and convolutional neural network in the validation dataset (AUC=0.702), whereas CHA2DS2-VASc had an AUC of 0.5 or less in both data sets. Combining CHA2DS2-VASc with random forest and convolutional neural network yielded a validation AUC of 0.696 and test AUC of 0.634, yielding the highest average AUC on nontraining data.ConclusionsThis proof-of-concept study found that machine learning and ensemble methods that incorporate daily AF burden signature provided incremental prognostic value for risk stratification beyond CHA2DS2-VASc for near-term risk of stroke.

Dataset Information

Prognostic Significance of Premature Atrial Complexes Burden in Prediction of Long-Term Outcome.

Background

Methods and results

Conclusions

Publications

Prognostic Significance of Premature Atrial Complexes Burden in Prediction of Long-Term Outcome.

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