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

ABSTRACT: 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.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.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-other | 2015 Sep

REPOSITORIES: biostudies-other

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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: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.

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Project description:BackgroundAbout 30% of stroke patients suffer from aphasia. As aphasia strongly affects daily life, most patients request a prediction of outcome of their language function. Prognostic models provide predictions of outcome, but external validation is essential before models can be used in clinical practice. We aim to externally validate the prognostic model from the Sequential Prognostic Evaluation of Aphasia after stroKe (SPEAK-model) for predicting the long-term outcome of aphasia caused by stroke.MethodsWe used data from the Rotterdam Aphasia Therapy Study - 3 (RATS-3), a multicenter RCT with inclusion criteria similar to SPEAK, an observational prospective study. Baseline assessment in SPEAK was four days after stroke and in RATS-3 eight days. Outcome of the SPEAK-model was the Aphasia Severity Rating Scale (ASRS) at 1 year, dichotomized into good (ASRS-score of 4 or 5) and poor outcome (ASRS-score < 4). In RATS-3, ASRS-scores at one year were not available, but we could use six month ASRS-scores as outcome. Model performance was assessed with calibration and discrimination.ResultsWe included 131 stroke patients with first-ever aphasia. At six months, 86 of 124 (68%) had a good outcome, whereas the model predicted 88%. Discrimination of the model was good with an area under the receiver operation characteristic curve of 0.87 (95%CI: 0.81-0.94), but calibration was unsatisfactory. The model overestimated the probability of good outcome (calibration-in-the-large α = - 1.98) and the effect of the predictors was weaker in the validation data than in the derivation data (calibration slope β = 0.88). We therefore recalibrated the model to predict good outcome at six months.ConclusionThe original model, renamed SPEAK-12, has good discriminative properties, but needs further external validation. After additional external validation, the updated SPEAK-model, SPEAK-6, may be used in daily practice to discriminate between patients with good and patients with poor outcome of aphasia at six months after stroke.Trial registrationRATS-3 was registered on January 13th 2012 in the Netherlands Trial Register: NTR3271 . SPEAK was not listed in a trial registry.

Project description:BackgroundThere are limited data on the association of diabetes mellitus (DM) and levels of glycated hemoglobin (HbA1c) with outcomes in patients with atrial fibrillation (AF).MethodsThis retrospective cohort study included patients who were recently hospitalized with a primary or secondary diagnosis of AF from December 2015 through June 2018. Kaplan-Meier curves and Cox-regression adjusted hazard ratios (aHR) were calculated for the primary outcome of all-cause mortality and for the secondary outcomes of cardiovascular (CV) mortality and the composite outcome of CV death or hospitalization. Competing-risk regression analyses were performed to calculate the cumulative risk of stroke, major bleeding, AF- or HF-hospitalizations adjusted for the competing risk of all-cause death. Spline curve models were fitted to investigate associations of HbA1c values and mortality among patients with AF and DM.ResultsIn total 1109 AF patients were included, of whom 373 (33.6%) had DM. During a median follow-up of 2.6 years, 414 (37.3%) patients died. The presence of DM was associated with a higher risk of all-cause mortality (aHR?=?1.40 95% confidence intervals [CI] 1.11-1.75), CV mortality (aHR?=?1.39, 95% CI 1.07-1.81), sudden cardiac death (aHR?=?1.73, 95% CI 1.19-2.52), stroke (aHR?=?1.87, 95% CI 1.01-3.45) and the composite outcome of hospitalization or CV death (aHR?=?1.27, 95% CI 1.06-1.53). In AF patients with comorbid DM, the spline curves showed a positive linear association between HbA1c levels and outcomes, with values 7.6-8.2% being independent predictors of increased all-cause mortality, and values?<?6.2% predicting significantly decreased all-cause and CV mortality.ConclusionsThe presence of DM on top of AF was associated with substantially increased risk for all-cause or CV mortality, sudden cardiac death and excess morbidity. HbA1c levels lower than 6.2% were independently related to better survival rates suggesting that optimal DM control could be associated with better clinical outcomes in AF patients with DM.

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

Publications

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

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